Posted: August 4th, 2022
audit report
Please read the “attachment” audit report. WRITE A THREE PAGES LONGS, SINGLE SPACE, 12 FRONT REPORT. REGARDING THE RISKS OF DATA MINING OF THIS AUDIT REPORT.
GAO
United
St
a
tes General Accounting Office
Report to the Ranking Minority Member,
Subcommittee on Financial Management,
the Budget, and International Security,
Committee on Governmental Affairs,
U.S. Senate
May
20
04
Federal Efforts Cover
a Wide Range of Uses
a
GAO-04-54
8
Federal agencies are using data mining for a variety of purposes, ranging
from improving service or performance to analyzing and detecting terrorist
patterns and activities. Our survey of 128 federal departments and agencies
on their use of data mining shows that 52 agencies are using or are planning
to use data mining. These departments and agencies reported 199 data
mining efforts, of which 68 are planned and 131 are operational. The figure
here shows the most common uses of data mining efforts as described by
agencies. Of these uses, the Department of Defense reported the largest
number of efforts aimed at improving service or performance, managing
human resources, and analyzing intelligence and detecting terrorist
activities. The
Department of Education
reported the largest number of
efforts aimed at detecting fraud, waste, and abuse. The National Aeronautics
and Space Administration reported the largest number of efforts aimed at
analyzing scientific and research information. For detecting criminal
activities or patterns, however, efforts are spread relatively evenly among
the agencies that reported having such efforts.
In addition, out of all 199 data mining efforts identified, 122 used personal
information. For these efforts, the primary purposes were improving service
or performance; detecting fraud, waste, and abuse; analyzing scientific and
research information; managing human resources; detecting criminal
activities or patterns; and analyzing intelligence and detecting terrorist
activities.
Agencies also identified efforts to mine data from the private sector and data
from other federal agencies, both of which could include personal
information. Of 54 efforts to mine data from the private sector (such as
credit reports or credit card transactions), 36 involve personal information.
Of 77 efforts to mine data from other federal agencies, 46 involve personal
information (including student loan application data, bank account numbers,
credit card information, and taxpayer identification numbers).
Top Six Purposes of Data Mining Efforts in Departments and Agencies
Both the government and the
private sector are increasingly
using “data mining”—that is, the
application of database technology
and techniques (such as statistical
analysis and modeling) to uncover
hidden patterns and subtle
relationships in data and to infer
rules that allow for the prediction
of future results. As has been
widely reported, many federal data
mining efforts involve the use of
personal information that is mined
from databases maintained by
public as well as private sector
organizations.
GAO was asked to survey data
mining systems and activities in
federal agencies. Specifically, GAO
was asked to identify planned and
operational federal data mining
efforts and describe their
characteristics.
www.gao.gov/cgi-bin/getrpt?GAO-04-548
To view the full product, including the scope
and methodology, click on the link above.
For more information, contact Linda Koontz at
(202) 512-6240 or koontzl@gao.gov.
Highlights of GAO-04-548, a report to the
Ranking Minority Member, Subcommittee
on Financial Management, the Budget,
and International Security, Committee on
Governmental Affairs, U.S. Senate
May 200
4
DATA MINING
Federal Efforts Cover a Wide Range of
Uses
/cgi-bin/getrpt?GAO-04-548
http://www.gao.gov/cgi-bin/getrpt?GAO-04-548
Letter 1
Results in Brief 2
Background 3
Agencies Identified Numerous Data Mining Efforts with Various
Aims
7
Summary
12
Appendixes
Appendix I:
14
Appendix II:
1
6
Appendix III: Departments and Agencies Reporting No Data Mining
Efforts 23
Appendix IV:
27
Tables Table 1: Top Six Purposes of Data Mining Efforts in Departments
and Agencies and Number of Efforts Reported 8
Table 2: Department of Agriculture’s Inventory of Data Mining
Efforts 27
Table 3: Department of Commerce’s Inventory of Data Mining
Efforts 2
9
Table 4: Department of Defense’s Inventory of Data Mining
Efforts 29
Table 5: Department of Education’s Inventory of Data Mining
Efforts 37
Table 6: Department of Energy’s Inventory of Data Mining
Efforts 40
Table 7: Department of Health and Human Services’ Inventory of
Data Mining Efforts 41
Table 8: Department of Homeland Security’s Inventory of Data
Mining Efforts 43
Table 9: Department of the Interior’s Inventory of Data Mining
Efforts 46
Table 10: Department of Justice’s Inventory of Data Mining
Efforts 47
Table 11: Department of Labor’s Inventory of Data Mining Efforts 49
Table 12:
Department of State
’s Inventory of Data Mining
Efforts 50
Table 13: Department of Transportation’s Inventory of Data Mining
Efforts 50
Page i GAO-04-548 Data Mining
Contents
Table 14: Department of the Treasury’s Inventory of Data Mining
Efforts 51
Table 15: Department of Veterans Affairs’ Inventory of Data Mining
Efforts 54
Table 16: Environmental Protection Agency’s Inventory of Data
Mining Efforts 56
Table 17: Export-Import Bank of the United States’ Inventory of Data
Mining Efforts 56
Table 18: Federal Deposit Insurance Corporation’s Inventory of Data
Mining Efforts 57
Table 19: Federal Reserve System’s Inventory of Data Mining
Efforts 57
Table 20: National Aeronautics and Space Administration’s
Inventory of Data Mining Efforts 58
Table 21: Nuclear Regulatory Commission’s Inventory of Data
Mining Efforts 62
Table 22: Office of Personnel Management’s Inventory of Data
Mining Efforts 62
Table 23: Pension Benefit Guaranty Corporation’s Inventory of Data
Mining Efforts 63
Table 24: Railroad Retirement Board’s Inventory of Data Mining
Efforts 63
Table 25: Small Business Administration’s Inventory of Data Mining
Efforts 64
Figures Figure 1: Top Six Purposes of Data Mining Efforts That Involve
Personal Information
10
Figure 2: Top Six Purposes of Data Mining Efforts That Involve
Private Sector Data 11
Figure 3: Top Six Purposes of Data Mining Efforts That Involve
Data from Other Federal Agencies 12
Page ii GAO-04-548 Data Mining
Contents
Abbreviations
CARDS Counterintelligence Analytical Research Data System
CG Coast Guard
CI-AIMS Counterintelligence Automated Investigative
Management System
DHHS
Department of Health and Human Services
DOD
Department of Defense
DOE
Department of Energy
DOT
Department of Transportation
EFTPS Electronic Federal Tax Payment System
EOS Earth Observing System
FARS Fatality Analysis Reporting System
FDA
Food and Drug Administration
GENESIS Global Environmental and Earth Science Information
System
GSFC Goddard Space Federal Center
HR Human Resources
HRSA
Health Resources and Services Administration
MATRIX Multistate Anti-terrorism Information Exchange System
NASA
National Aeronautics and Space Administration
NVO National Virtual Observatory
OIG Office of Inspector General
OLAP On-line Analytical Processing
RSST Real Estate Stress Test
SAA Spectral Analysis Automation
SAS Safety Automated System
SMARTS Statistical Management Analysis and Reporting Tool
System
SWC Space Warfare Center
TIMS Technical Information Management System
TOP Treasury Offset Program
VA Veterans Affairs
VHA
Veterans Health Administration
VISN Veterans Integrated Service Network
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Page iii GAO-04-548 Data Mining
United States General Accounting Office
Washington, D.C. 20548
A
May 4, 2004 Letter
The Honorable Daniel K. Akaka
Ranking Minority Member
Subcommittee on Financial Management, the Budget,
and International Security
Committee on Governmental Affairs
United States Senate
Dear Senator Akaka:
Data mining—a technique for extracting knowledge from large volumes of
data—is increasingly being used by government and by the private sector.
As has been widely reported, many federal data mining efforts involve the
use of personal information1 that is mined from public as well as private
sector organizations.
This report responds to your request that we identify and describe
operational and planned data mining systems and activities in federal
agencies. In a follow-up report, we plan to perform an in-depth review of
selected federal data mining efforts.
The term “data mining” has a number of meanings. For purposes of this
work, we define data mining as the application of database technology and
techniques—such as statistical analysis and modeling—to uncover hidden
patterns and subtle relationships in data and to infer rules that allow for the
prediction of future results. We based this definition on the most commonly
used terms found in a survey of the technical literature. In our initial survey
of chief information officers, these officials found the definition sufficient
to identify agency data mining efforts.
1As used in this report, personal information is all information associated with an individual
and includes both identifying information and nonidentifying information. Identifying
information, which can be used to locate or identify an individual, includes name, aliases,
Social Security number, e-mail address, driver’s license number, and agency-assigned case
number. Nonidentifying personal information includes age, education, finances, criminal
history, physical attributes, and gender.
Page 1 GAO-04-548 Data MiningPage 1 GAO-04-548 Data Mining
To address our objective to identify and describe operational and planned
data mining systems and activities in federal agencies, we surveyed chief
information officers or comparable officials at 128 federal departments and
agencies to determine whether the agencies had operational and planned
data mining systems or activities.2 We then conducted telephone interviews
with the reported system managers to obtain information on the
characteristics of the identified data mining efforts. To verify the
information we received, we sent follow-up letters to agencies that
responded as well as to those that did not respond, we asked responsible
officials to verify the information, and we performed random assessments
of the means that these officials used to verify the information.
In addition, we conducted a search of technical literature and periodicals
to develop a comprehensive list of federal government data mining efforts
and then compared these efforts with data mining efforts reported by
federal agencies. If the data mining efforts on our lists were not reported on
the survey, we contacted the appropriate chief information officers and,
with their concurrence, added the efforts.
We performed our work from May 2003 to April 2004 in accordance with
generally accepted government auditing standards. Additional details on
our scope and methodology are provided in appendix I.
Results in Brief Federal agencies are using data mining for a variety of purposes, ranging
from improving service or performance to analyzing and detecting terrorist
patterns and activities. Our survey of 128 federal departments and agencies
on their use of data mining shows that 52 agencies are using or are planning
to use data mining. These departments and agencies reported 199 data
mining efforts, of which 68 were planned and 131 were operational. The
most common uses of data mining efforts were described by agencies as
• improving service or performance;
• detecting fraud, waste, and abuse;
• analyzing scientific and research information;
2That is, we asked about both systems explicitly dedicated to data mining and activities
using automated tools to “mine” databases that are part of other systems. In this report, we
use the word “efforts” to refer to both systems and activities, unless otherwise specified.
Page 2 GAO-04-548 Data Mining
• managing human resources;
• detecting criminal activities or patterns; and
• analyzing intelligence and detecting terrorist activities.
The Department of Defense reported having the largest number of data
mining efforts aimed at improving service or performance and at managing
human resources. Defense was also the most frequent user of efforts aimed
at analyzing intelligence and detecting terrorist activities, followed by the
Departments of Homeland Security, Justice, and Education.
The Department of Education reported the largest number of efforts aimed
at detecting fraud, waste, and abuse, while the National Aeronautics and
Space Administration targets most of their data mining efforts (21 out of
23) toward analyzing scientific and research information. Data mining
efforts for detecting criminal activities or patterns, however, were spread
relatively evenly among the reporting agencies.
In addition, out of all 199 data mining efforts identified, 122 used personal
information. For these efforts, the primary purposes were detecting fraud,
waste, and abuse; detecting criminal activities or patterns; analyzing
intelligence and detecting terrorist activities; and increasing tax
compliance.
Agencies also identified efforts to mine data from the private sector and
data from other federal agencies, both of which could include personal
information. Of 54 efforts to mine data from the private sector (such as
credit reports or credit card transactions), 36 involve personal information.
Of 77 efforts to mine data from other federal agencies, 46 involve personal
information (including student loan application data, bank account
numbers, credit card information, and taxpayer identification numbers).
Background Data mining enables corporations and government agencies to analyze
massive volumes of data quickly and relatively inexpensively. The use of
this type of information retrieval has been driven by the exponential
growth in the volumes and availability of information collected by the
public and private sectors, as well as by advances in computing and data
storage capabilities. In response to these trends, generic data mining tools
are increasingly available for—or built into—major commercial database
applications. Today, mining can be performed on many types of data,
Page 3 GAO-04-548 Data Mining
including those in structured, textual, spatial, Web, or multimedia forms.
Data mining is becoming a big business; Forrester Research has estimated
that the data mining market is passing the billion dollar mark.
Although the use and sophistication of data mining have increased in both
the government and the private sector, data mining remains an ambiguous
term. According to some experts, data mining overlaps a wide range of
analytical activities, including data profiling, data warehousing, online
analytical processing, and enterprise analytical applications.3 Some of the
terms used to describe data mining or similar analytical activities include
“factual data analysis” and “predictive analytics.” We surveyed technical
literature and developed a definition of data mining based on the most
commonly used terms found in this literature. Based on this search, we
define data mining as the application of database technology and
techniques—such as statistical analysis and modeling—to uncover hidden
patterns and subtle relationships in data and to infer rules that allow for the
prediction of future results. We used this definition in our initial survey of
chief information officers; these officials found the definition sufficient to
identify agency data mining efforts.
Data mining has been used successfully for a number of years in the private
and public sectors in a broad range of applications. In the private sector,
these applications include customer relationship management, market
research, retail and supply chain analysis, medical analysis and diagnostics,
financial analysis, and fraud detection. In the government, data mining was
initially used to detect financial fraud and abuse. For example, data mining
has been an integral part of GAO audits and investigations of federal
government purchase and credit card programs.4 Data mining and related
technologies are also emerging as key tools in Department of Homeland
Security initiatives.
3Lou Agosta, “Data Mining Is Dead—Long Live Predictive Analytics!” (Forrester Research,
Oct. 30, 2003), http://www.forrester.com/Research/LegacyIT/0,7208,
33
030,00.html
(downloaded Jan. 26, 2004).
4For more information on the uses of data mining in GAO audits, see U.S. General
Accounting Office, Data Mining: Results and Challenges for Government Programs,
Audits, and Investigations, GAO-03-591T (Washington, D.C: Mar. 25, 2003).
Page 4 GAO-04-548 Data Mining
http://www.forrester.com/Research/LegacyIT/0,7208,33030,00.html
http://www.forrester.com/Research/LegacyIT/0
http://www.gao.gov/cgi-bin/getrpt?GAO-03-591T
Data Mining Poses Privacy
Challenge
Since the terrorist attacks of September 11, 2001, data mining has been
seen increasingly as a useful tool to help detect terrorist threats by
improving the collection and analysis of public and private sector data. In a
recent report on information sharing and analysis to address the challenges
of homeland security, it was noted that agencies at all levels of government
are now interested in collecting and mining large amounts of data from
commercial sources.5 The report noted that agencies may use such data not
only for investigations of known terrorists, but also to perform large-scale
data analysis and pattern discovery in order to discern potential terrorist
activity by unknown individuals. Such use of data mining by federal
agencies has raised public and congressional concerns regarding privacy.
One example of a large-scale development effort launched in the wake of
the September 11 attacks is the Multistate Anti-terrorism Information
Exchange System, known as MATRIX. MATRIX, currently used in five
states,6 provides the capability to store, analyze, and exchange sensitive
terrorism-related and other criminal intelligence data among agencies
within a state, among states, and between state and federal agencies.
Information in MATRIX databases includes criminal history records,
driver’s license data, vehicle registration records, incarceration records,
and digitized photographs. Public awareness of MATRIX and of similar
large-scale data mining or data mining-like projects has led to concerns
about the government’s use of data mining to conduct a mass
“dataveillance”7—a surveillance of large groups of people—to sift through
vast amounts of personally identifying data to find individuals who might fit
a terrorist profile.
5
Creating a Trusted Information Network for Homeland Security (New York City: The
Markle Foundation, December 2003),
http://www.markletaskforce.org/Report2_Full_Report (downloaded Mar. 8, 2004).
6Five states are currently participating in the MATRIX pilot project: Connecticut, Florida,
Michigan, Ohio, and Pennsylvania.
7Roger Clarke, “Information Technology and Dataveillance,” Communications of the ACM,
vol. 31, issue 5 (New York City: ACM Press, May 1988),
http://www.anu.edu.au/people/Roger.Clarke/DV/CACM88.html (downloaded Mar. 5, 2004).
Clarke defines mass dataveillance as the systematic use of personal data systems in the
investigation or monitoring of the actions or communications of groups of people.
Page 5 GAO-04-548 Data Mining
http://www.markletaskforce.org/Report2_Full_Report
http://www.anu.edu.au/people/Roger.Clarke/DV/CACM88.html
http://www.markletaskforce.org/Report2_Full_Report
http://www.anu.edu.au/people/Roger.Clarke/DV/CACM88.html
Mining government and private databases containing personal information
creates a range of privacy concerns. Through data mining, agencies can
quickly and efficiently obtain information on individuals or groups by
exploiting large databases containing personal information aggregated
from public and private records. Information can be developed about a
specific individual or about unknown individuals whose behavior or
characteristics fit a specific pattern. Before data aggregation and data
mining came into use, personal information contained in paper records
stored at widely dispersed locations, such as courthouses or other
government offices, was relatively difficult to gather and analyze. As one
expert noted, data mining technologies that provide for easy access and
analysis of aggregated data challenge the concept of privacy protection
afforded to individuals through the inherent inefficiency of government
agencies analyzing paper, rather than aggregated, computer records.8
Privacy concerns about mined or analyzed personal data also include
concerns about the quality and accuracy of the mined data; the use of the
data for other than the original purpose for which the data were collected
without the consent of the individual; the protection of the data against
unauthorized access, modification, or disclosure; and the right of
individuals to know about the collection of personal information, how to
access that information, and how to request a correction of inaccurate
information.9
8K.A. Taipale, “Data Mining and Domestic Security: Connecting the Dots to Make Sense of
Data,” The Columbia Science and Technology Law Review, vol. V, 2003-2004 (New York
City: Columbia Law School, 2004), http://www.stlr.org/cite.cgi?volume=5&article=2
(downloaded Mar. 18, 2004).
9These privacy concerns are reflected in the Fair Information Practices proposed in 1980 by
the Organization for Economic Cooperation and Development and endorsed by the U.S.
Department of Commerce in 1981. These practices govern collection limitation, purpose
specification, use limitation, data quality, security safeguards, openness, individual
participation, and accountability.
Page 6 GAO-04-548 Data Mining
http://www.stlr.org/cite.cgi?volume=5&article=2
http://www.stlr.org/cite.cgi?volume=5&article=2
Agencies Identified
Numerous Data Mining
Efforts with Various
Aims
Of 128 federal departments and agencies surveyed for information on their
planned and operational data mining efforts (listed in app. II), 52 agencies
reported 199 data mining efforts, and 69 agencies reported that they were
not engaged in data mining and were not planning such efforts (listed in
app. III). Of the 199 data mining efforts, 68 were planned and 131 were
operational. Seven agencies did not respond to our survey.10 Appendix IV
lists the 199 data mining efforts reported, along with key characteristics.
Agencies described the most common purposes of data mining efforts as
• improving service or performance;
• detecting fraud, waste, and abuse;
• analyzing scientific and research information;
• managing human resources;
• detecting criminal activities or patterns; and
• analyzing intelligence and detecting terrorist activities.
As shown in table 1, the Department of Defense reported the largest
number of efforts aimed at improving service or performance (with 19 out
of 65 reported efforts) and at managing human resources (with 14 out of 17
efforts). Defense was also the most frequent user of efforts aimed at
analyzing intelligence and detecting terrorist activities, with 5 of 14 efforts,
followed by the Departments of Homeland Security and Justice, with 4 and
3 efforts, respectively. The Department of Education has the largest
number of efforts aimed at detecting fraud, waste, and abuse (9 out of 24
efforts reported). The National Aeronautics and Space Administration
accounts for 21 of the 23 identified efforts for analyzing scientific and
research information. Efforts are spread relatively evenly among the
agencies that reported using data mining efforts for detecting criminal
10Agencies that did not respond to our survey are (1) the Central Intelligence Agency; (2) the
Corporation for National and Community Service
s; (3) the Department of Army, Department
of Defense; (4) the Equal Employment Opportunity Commission; (5) the National Park
Service, Department of the Interior; (6) the National Security Agency, Department of
Defense; and (7) the Rural Utilities Service, Department of Agriculture.
Page 7 GAO-04-548 Data Mining
activities or patterns. Table 1 summarizes the top six uses of data mining
efforts among the responding agencies.
Table 1: Top Six Purposes of Data Mining Efforts in Departments and Agencies and Number of Efforts Reported
Source: GAO analysis of agency-provided data.
Department or agency
Improving
service or
performance
Detecting
fraud, waste,
and abuse
Analyzing
scientific and
research
information
Managing
human
resources
Detecting
criminal
activities or
patterns
Analyzing
intelligence
and detecting
terrorist
activities
Department of Agriculture 8 1
Department of Commerce
Department of Defense 19 1 1 14 1 5
Department of Education 6 9 3 1
Department of Energy 3
Department of Health and Human
Services 4 1 1
Department of Homeland Security 5 2 2 4
Department of the Interior 1
Department of Justice 1 1 3 3
Department of Labor 3 1
Department of State 2
Department of Transportation 1
Department of the Treasury 4 1 2
Department of Veterans Affairs 5 5 1
Environmental Protection Agency 1
Export-Import Bank of the United
States 1
Federal Deposit Insurance Corporation 1
Federal Reserve System 1
National Aeronautics and Space
Administration 1 1 21
Nuclear Regulatory Commission 1
Office of Personnel Management 1
Pension Benefit Guaranty Corporation 2
Railroad Retirement Board 1
Small Business Administration 1
Total 65 24 23 17 15 14
Page 8 GAO-04-548 Data Mining
Some data mining purposes focus on human activities and therefore are
inherently likely to involve personal information; examples of these
purposes are detecting fraud, waste, and abuse; detecting criminal
activities or patterns; managing human resources; and analyzing
intelligence. The following are examples of data mining efforts for each of
these purposes:
• Detecting fraud, waste, and abuse. The Veterans Benefits
Administration’s C & P Payment Data Analysis effort mines veterans’
compensation and pension data for evidence of fraud.
• Detecting criminal activities or patterns. The Department of
Education’s Title IV Identity Theft Initiative effort focuses on identity
theft cases involving education loans.
• Managing human resources. The U.S. Air Force’s Oracle HR (Human
Resources) uses data mining to provide information on promotions, pay
grades, clearances, and other information relevant to human resources
planning.
• Analyzing intelligence and detecting terrorist activities. The Defense
Intelligence Agency’s Verity K2 Enterprise mines data from the
intelligence community and Internet sources to identify foreign
terrorists or U.S. citizens connected to foreign terrorism activities.
On the other hand, other categories of efforts do not necessarily focus on
human activities or involve personal information, such as many of the
efforts aimed at analyzing scientific and research information. The National
Aeronautics and Space Administration, for example, mines large, complex
earth science data sets to find patterns and relationships to detect hidden
events (the system is called Machine Learning and Data Mining for
Improved Data Understanding of High Dimensional Earth Sensed Data).
Similarly, many efforts aimed at improving service or performance (the
most frequently cited purpose of data mining efforts) do not involve
personal information. For example, the Department of the Navy’s Supply
Management System Multidimensional Cubes system includes a data
warehouse containing data on every ship part that has been ordered since
the 1980s, with multidimensional information on each part. The Navy uses
data mining to calculate failure rates and identify needed improvements;
according to the Navy, this system reduces downtime on ships by
improving parts replacement.
Page 9 GAO-04-548 Data Mining
However, some efforts aimed at improving service or performance do
involve personal information. For example, the Veterans Administration’s
VISN (Veterans Integrated Service Network) 16 Data Warehouse is mined
for a variety of information, including patient visits, laboratory tests, and
pharmacy records, to provide management with health care system
performance information.
Overall, 122 of the 199 data mining efforts involve personal information.
Figure 1 shows the top six purposes of these efforts, as well as their
distribution.
Figure 1: Top Six Purposes of Data Mining Efforts That Involve Personal Information
Of the 199 data mining efforts, 54 use or plan to use data from the private
sector. Of these, 36 involve personal information. The personal information
from the private sector included credit reports and credit card transaction
records. Figure 2 shows the distribution of the top six purposes of the 54
efforts involving data from the private sector.
0 10 20 30 40
Purposes
Source: GAO analysis of agency data.
Number of data mining efforts
Managing human resources
Analyzing intelligence and detecting
terrorist activities
Increasing tax compliance
Detecting criminal activities or
patterns
Improving service or performance
Detecting fraud, waste, and abuse
7
10
15
15
24
33
Page 10 GAO-04-548 Data Mining
Figure 2: Top Six Purposes of Data Mining Efforts That Involve Private Sector Data
Of the 199 data mining efforts, 77 efforts use or plan to use data from other
federal agencies. Of the 77 efforts, 46 involve personal information. The
personal information from other federal agencies included student loan
application data, bank account numbers, credit card information, and
taxpayer identification numbers. Figure 3 shows the top six uses for the 77
efforts involving data from other federal agencies and their distribution.
0 10 20 30 40
Purposes
Source: GAO analysis of agency data.
Number of data mining efforts
Improving safety
Detecting criminal activities or
patterns
Analyzing intelligence and
detecting terrorist activities
Analyzing scientific and research
information
Detecting fraud, waste, and abuse
Improving service or performance
4
4
5
8
9
14
Page 11 GAO-04-548 Data Mining
Figure 3: Top Six Purposes of Data Mining Efforts That Involve Data from Other
Federal Agencies
Summary Driven by advances in computing and data storage capabilities and by
growth in the volumes and availability of information collected by the
public and private sectors, data mining enables government agencies to
analyze massive volumes of data. Our survey shows that data mining is
increasingly being used by government for a variety of purposes, ranging
from improving service or performance to analyzing and detecting terrorist
patterns and activities.
Although this survey provides a broad overview of the emerging uses of
data mining in the federal government, more work is needed to shed light
on the privacy implications of these efforts. In future work, we plan to
examine selected federal data mining efforts and their implications.
As agreed with your office, unless you publicly announce the contents of
the report earlier, we plan no further distribution until 30 days from the
report date. At that time, we will send copies of this report to the Chairmen
and Ranking Minority Members of the House Committee on Government
Reform; Subcommittee on Civil Service and Agency Organization, House
Committee on Government Reform; Select Committee on Homeland
Security, House of Representatives; Senate Committee on Governmental
0 10 20 30 40
Purposes
Source: GAO analysis of agency data.
Number of data mining efforts
Detecting fraud, waste, and abuse
Analyzing scientific and research
information
Analyzing intelligence and detecting
terrorist activities
Detecting criminal activities or patterns
Managing human resources
Improving service or performance
5
6
7
12
13
20
Page 12 GAO-04-548 Data Mining
Affairs; and the Subcommittee on Oversight of Government Management,
the Federal Workforce and the District of Columbia, Senate Committee on
Governmental Affairs. We will also make copies available to others on
request. In addition, this report will be available at no charge on the GAO
Web site at http://www.gao.gov.
If you have any questions concerning this report, please call me at (202)
512-6240 or Mirko J. Dolak, Assistant Director, at (202) 512-6362. We can
also be reached by e-mail at koontzl@gao.gov and dolakm@gao.gov,
respectively. Key contributors to this report were Camille M. Chaires,
Barbara S. Collier, Orlando O. Copeland, Nancy E. Glover, Stuart M.
Kaufman, Lori D. Martinez, Morgan F. Walts, and Marcia C. Washington.
Sincerely yours,
Linda D. Koontz
Director, Information Management Issues
Page 13 GAO-04-548 Data Mining
mailto:koontzl@gao.gov
mailto:dolakm@gao.gov
mailto:koontz@gao.gov
mailto:dolakm@gao.gov
http://www.gao.gov.
Appendix I
AppendixesObjective, Scope, and Methodology Appendix I
Our objective was to identify and describe planned and operational federal
data mining efforts. As a first step in addressing this objective, we
developed a definition of “data mining.” Because this expression has a
range of meanings, we surveyed the technical literature to develop a
definition based on the most commonly used terms found in this literature.
We defined data mining as the application of database technology and
techniques—such as statistical analysis and modeling—to uncover hidden
patterns and subtle relationships in data and to infer rules that allow for the
prediction of future results. In our initial survey of chief information
officers, these officials found the definition sufficient to identify agency
data mining efforts.
We then surveyed chief information officers or comparable officials at 128
federal departments and agencies (see app. II) and asked them to identify
whether their agency had operational and planned data mining efforts. We
achieved a 95 percent response rate. Of the 121 agencies that responded, 69
reported that they did not have any data mining efforts (see app. III). We
followed up with these 69 agencies and gave them another opportunity to
report data mining efforts.
To obtain information on the characteristics of the identified operational or
planned data mining efforts, we conducted structured telephone
interviews1 with the identified system owners or activity managers. The
interviews were designed to obtain detailed information about each data
mining system, including the purpose and size, the use of personal
information, and the use of data from the private sector or other federal
organizations. We pretested the structured interview to ensure relevance
and clarity.
We aggregated these data by agency and sent them back to the chief
information officer, comparable official, or their designee and asked that
they review the characteristics for completeness and accuracy. One of the
52 departments and agencies that reported data mining systems—the
Department of Homeland Security—has not responded to our request to
review the reported data for completeness and accuracy.
1In a structured interview, the interviewer asks the same questions of numerous individuals
or individuals representing numerous organizations in a precise manner, offering each
interviewee the same set of possible responses.
Page 14 GAO-04-548 Data Mining
Appendix I
Objective, Scope, and Methodology
We performed random assessments of the means that these officials used
to verify the information. Based on these assessments, we concluded that
the agencies’ verification methods were reasonable and that as a result, we
could rely on the accuracy of the reported data. We also conducted a
search of technical literature and periodicals to develop a list of federal
government data mining efforts and then compared the efforts on this list
with the data mining efforts reported by federal agencies. If the data mining
efforts on our list were not reported on the survey, we contacted the chief
information officer or comparable official to determine whether that data
mining effort should be included in our survey.
Because this was not a sample survey, there are no sampling errors.
However, the practical difficulties of conducting any survey may introduce
errors, commonly referred to as nonsampling errors. For example,
difficulties in how a particular question is interpreted, in the sources of
information that are available to respondents, or in how the data are
entered into a database or were analyzed can introduce unwanted
variability into the survey results. We took steps in the development of the
structured interview, the data collection, and the data analysis to minimize
these nonsampling errors. Among these steps, we pretested the structured
interview instrument, contacted nonresponding agencies as well as
agencies not identifying data mining efforts, and sent the aggregated data
to the agency chief information officer for review.
We conducted our work from May 2003 to April 2004 in accordance with
generally accepted government auditing standards.
Page 15 GAO-04-548 Data Mining
Appendix II
Surveyed Departments and Agencies Appendix II
Department of Agriculture
• Agricultural Marketing Service
• Agricultural Research Service
• Animal and Plant Health Inspection Service
• Cooperative State Research, Education, and Extension Service
• Farm Service Agency
• Food and Nutrition Service
• Food Safety and Inspection Service
• Foreign Agricultural Service
• Forest Service
• National Agricultural Statistics Service
• Natural Resources Conservation Service
•
Risk Management Agency
• Rural Utilities Service
Department of Commerce
• Bureau of the Census
• Economic Development Administration
• International Trade Administration
• National Oceanic and Atmospheric Administration
• U.S. Patent and Trademark Office
Page 16 GAO-04-548 Data Mining
Appendix II
Surveyed Departments and Agencies
Department of Defense
• Missile Defense Agency
• Defense Advanced Research Projects Agency
•
Defense Commissary Agency
• Defense Contract Audit Agency
• Defense Contract Management Agency
• Defense Information Systems Agency
• Defense Intelligence Agency
• Defense Legal Services Agency
• Defense Logistics Agency
• Defense Security Cooperation Agency
• Defense Security Service
• Defense Threat Reduction Agency
• Department of the Air Force
• Department of the Army
• Department of the Navy
• National Geospatial-Intelligence Agency
• National Security Agency
• U.S. Marine Corps
Department of Education
Page 17 GAO-04-548 Data Mining
Appendix II
Surveyed Departments and Agencies
Department of Energy
• Bonneville Power Administration
• Southeastern Power Administration
• Southwestern Power Administration
• Western Area Power Administration
Department of Health and Human Services
• Administration for Children and Families
•
Agency for Healthcare Research and Quality
•
Centers for Disease Control and Prevention
• Centers for Medicare and Medicaid Services
• Food and Drug Administration
• Health Resources and Services Administration
• Indian Health Service
• National Institutes of Health
•
Program Support Center
Department of Homeland Security
•
Border and Transportation Security Directorate
• Bureau of Citizenship and Immigration Services
•
Emergency Preparedness and Response Directorate
• Information Analysis and Infrastructure Protection Directorate
• Management Directorate
Page 18 GAO-04-548 Data Mining
Appendix II
Surveyed Departments and Agencies
• Science and Technology Directorate
•
U.S. Coast Guard
• U.S. Secret Service
Department of Housing and Urban Development
Department of the Interior
• Bureau of Indian Affairs
• Bureau of Land Management
• Bureau of Reclamation
• Minerals Management Service
• National Park Service
• Office of Surface Mining Reclamation and Enforcement
• U.S. Fish and Wildlife Service
• U.S. Geological Survey
Department of Justice
• Bureau of Alcohol, Tobacco, Firearms, and Explosives
•
Drug Enforcement Administration
•
Federal Bureau of Investigation
•
Federal Bureau of Prisons
• U.S. Marshals Service
Department of Labor
Department of State
Page 19 GAO-04-548 Data Mining
Appendix II
Surveyed Departments and Agencies
Department of Transportation
• Federal Aviation Administration
• Federal Highway Administration
• Federal Motor Carrier Safety Administration
• Federal Railroad Administration
• Federal Transit Administration
•
National Highway Traffic Safety Administration
Department of the Treasury
• Bureau of Engraving and Printing
• Bureau of the Public Debt
• Financial Management Service
• Internal Revenue Service
• Office of the Comptroller of the Currency
• Office of Thrift Supervision
• U.S. Mint
Department of Veterans Affairs
• Veterans Benefits Administration
• Veterans Health Administration
Agency for International Development
Central Intelligence Agency
Corporation for National and Community Service
Page 20 GAO-04-548 Data Mining
Appendix II
Surveyed Departments and Agencies
Environmental Protection Agency
Equal Employment Opportunity Commission
Executive Office of the President
Export-Import Bank of the United States
Federal Deposit Insurance Corporation
Federal Energy Regulatory Commission
Federal Reserve System
Federal Retirement Thrift Investment Board
General Services Administration
Legal Services Corporation
National Aeronautics and Space Administration
National Credit Union Administration
National Labor Relations Board
National Science Foundation
Nuclear Regulatory Commission
Office of Management and Budget
Office of Personnel Management
Peace Corps
Pension Benefit Guaranty Corporation
Railroad Retirement Board
Securities and Exchange Commission
Page 21 GAO-04-548 Data Mining
Appendix II
Surveyed Departments and Agencies
Small Business Administration
Smithsonian Institution
Social Security Administration
U.S. Postal Service
Page 22 GAO-04-548 Data Mining
Appendix III
Departments and Agencies Reporting No Data
Mining Efforts Appendix III
The following 69 departments and agencies reported that they have no
operational or planned data mining efforts:
Department of Agriculture
• Agricultural Marketing Service
• Agricultural Research Service
• Animal and Plant Health Inspection Service
• Cooperative State Research, Education, and Extension Service
• Farm Service Agency
• Foreign Agricultural Service
• Forest Service
• National Agricultural Statistics Service
• Food Safety and Inspection Service
Department of Commerce
• Economic Development Administration
• Bureau of the Census
• International Trade Administration
• Department of Commerce Headquarters
• National Oceanic and Atmospheric Administration
Department of Defense
• Defense Contract Audit Agency
• Missile Defense Agency
• Defense Legal Services Agency
Page 23 GAO-04-548 Data Mining
Appendix III
Departments and Agencies Reporting No
Data Mining Efforts
• Defense Security Service
• Defense Threat Reduction Agency
• Defense Logistics Agency
• Defense Advanced Research Projects Agency
• Defense Contract Management Agency
• Defense Security Cooperation Agency
Department of Energy
• Bonneville Power Administration
• Southeastern Power Administration
• Southwestern Power Administration
• Western Area Power Administration
Department of Health and Human Services
• Centers for Medicare and Medicaid Services
• Administration for Children and Families
• National Institutes of Health
• Indian Health Service
Department of Homeland Security
• Science and Technology Directorate
• Management Directorate
• Bureau of Citizenship and Immigration Services
• Department of Homeland Security Headquarters
Page 24 GAO-04-548 Data Mining
Appendix III
Departments and Agencies Reporting No
Data Mining Efforts
Department of Housing and Urban Development
Department of the Interior
• Bureau of Reclamation
• Bureau of Land Management
• U.S. Geological Survey
• Fish and Wildlife Service
• Office of Surface Mining Reclamation and Enforcement
• Bureau of Indian Affairs
• Department of the Interior Headquarters
Department of Justice
• Bureau of Alcohol, Tobacco, Firearms, and Explosives
Department of Transportation
• Federal Aviation Administration
• Federal Transit Administration
• Federal Railroad Administration
• Federal Motor Carrier Safety Administration
• Federal Highway Administration
Department of the Treasury
• Comptroller of the Currency
• Bureau of the Public Debt
• Office of Thrift Supervision
Page 25 GAO-04-548 Data Mining
Appendix III
Departments and Agencies Reporting No
Data Mining Efforts
• Department of the Treasury Headquarters
• Bureau of Engraving and Printing
Agency for International Development
Executive Office of the President
Federal Energy Regulatory Commission
Federal Retirement Thrift Investment Board
General Services Administration
Legal Services Corporation
National Credit Union Administration
National Labor Relations Board
National Science Foundation
Office of Management and Budget
Peace Corps
Security and Exchange Commission
Smithsonian Institution
Social Security Administration
U.S. Postal service
Page 26 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts Appendix IV
The following tables present selected information from our survey of 128
major federal departments and agencies on their use of data mining. The
tables list the purpose of each data mining effort, whether the system is
planned or operational, and whether the system uses personal information,
data from the private sector, or data from other federal agencies. The
survey shows that 52 departments and agencies are using or are planning to
use data mining. These departments and agencies reported 199 data mining
efforts, of which 68 were planned and 131 were operational.
Table 2: Department of Agriculture’s Inventory of Data Mining Efforts
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector data
Other
agency
data
Department of Agriculture Headquarters
Travel Data Mart Will consolidate employee travel
information from financial and
travel systems. Will allow for a
governmentwide e-travel system
and provide the department with
information on the financial
ramifications of its travel.
Improving
service or
performance
Planned Yes No No
Financial Statements
Data Warehouse
Is used in the production of
consolidated financial statements.
Provides information for products
that are used to satisfy external
reporting requirements, such as
Office of Management and Budget
and Department of the Treasury
requirements.
Financial
management
Operational No No No
Financial Data
Warehouse
Is the department’s internal
financial management reporting
system. Data mining is done for ad
hoc and on-demand reports.
Financial
management
Operational Yes No No
Food and Nutrition Service
Grantee Monitoring
Activities—Southeast
Regional Office
Assists in monitoring the financial
status of grant holders. Grantees
are required to provide
expenditure reports, and analysis
is performed quarterly that
matches stated draws to the
actual draws from the U.S.
Treasury.
Improving
service or
performance
Operational Yes No No
Page 27 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Source: Department of Agriculture.
Grantee Monitoring
Activities—Mountain
Plains Regional
Office
Assists in monitoring the
management and distribution of
Indian funds for major food benefit
programs, such as food stamps, in
10 grantee states.
Improving
service or
performance
Operational Yes No No
Grantee Monitoring
Activities—
Southwest Regional
Office
Maximizes on-site monitoring
efforts by confirming the accuracy
of grantee accounting. Reduces
on-site time, maximizes time to
complete reviews, and has
achieved a 50 percent travel
savings.
Improving
service or
performance
Operational Yes No No
Grantee Monitoring
Activities—Midwest
Regional Office
Will be a reporting system to
provide reports and automate the
audit process. Plans are to
acquire data mining tools to review
and compare budgets, reports,
and plans.
Improving
service or
performance
Planned No No Yes
Grantee Monitoring
Activities—Northeast
Regional Office
Supports on-site reviews of
analyses to confirm financial
report information.
Improving
service or
performance
Operational Yes Yes No
Integrated Program
Accounting System
Data Integrity
Will create ad-hoc reporting
centers to validate accounting
information.
Improving
service or
performance
Planned No No No
Natural Resources Conservation Service
National Resource
Inventory Used for
Statistical Analysis of
Past Soil Survey
Databases.
Is a trending database that tracks
more than 200 resource issues
such as monitoring erosion. Also
processes statistical technology.
Improving
service or
performance
Operational No No No
Risk Management Agency
CAE Is part of a congressionally
mandated project to assist the
Risk Management Agency in
controlling fraud, waste, and
abuse in the Federal Crop
Insurance Corporation program.
Detecting
fraud, waste,
and abuse
Operational Yes Yes Yes
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector data
Other
agency
data
Page 28 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 3: Department of Commerce’s Inventory of Data Mining Efforts
Source: Department of Commerce.
Table 4: Department of Defense’s Inventory of Data Mining Efforts
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector data
Other
agency
data
U.S. Patent and Trademark Office
Compensation
Projection Model in
the
Enterprise Data
Warehouse
Generates and makes available
compensation projection data,
both salary and benefits, on
current employees and on
planned hires. It also accounts for
planned attritions.
Managing
human
resources
Operational Yes No Yes
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Defense Commissary Agency
DeCA Electronic
Records
Management and
Archive System
Will be a corporate information
system for managing unstructured
data. It will allow for electronic
record keeping, document
management, and automated
receipt processes.
Improving
service or
performance
Planned Yes Yes Yes
Corporate Decision
Support System/
Commissary
Operations
Management System
Mines data to produce analytical
data on commissary operations.
Provides information such as what
items stores are selling and helps
determine whether cashiers are
being honest.
Improving
service or
performance
Operational No No No
Defense Information Systems Agency
Enterprise Business
Intelligence System
Will replace the current
management information
environment, which includes
operations, reporting, billing,
statistics, and other management
information activities.
Improving
service or
performance
Planned No No No
Page 29 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Defense Intelligence Agency
Insight Smart
Discovery
Will be a data mining knowledge
discovery tool to work against
unstructured text. Will categorize
nouns (names, locations, events)
and present information in images.
Analyzing
intelligence
and detecting
terrorist
activities
Planned Yes No Yes
Verity K2 Enterprise Mines data from the intelligence
community and Internet searches
to identify foreign terrorists or U.S.
citizens connected to foreign
terrorism activities.
Analyzing
intelligence
and detecting
terrorist
activities
Operational Yes Yes Yes
PATHFINDER Is a data mining tool developed for
analysts that provides the ability to
analyze government and private
sector databases rapidly. It can
compare and search multiple
large databases quickly.
Analyzing
intelligence
and detecting
terrorist
activities
Operational Yes No Yes
Autonomy Is a large search engine tool that
is used to search hundreds of
thousands of word documents. Is
used for the organization and
knowledge discovery of
intelligence.
Analyzing
intelligence
and detecting
terrorist
activities
Operational No No Yes
Department of the Air Force
ANG Data
Warehouse—
Guardian
Will be used to measure military
readiness. It incorporates
information on all disciplines to
provide management information
needed to assess military
readiness.
Measuring
military
readiness
Planned Yes No No
Integrated Space
Warfare Center
(SWC) Information
System
Will be an internal database
containing information on all
development/execution activities
within the SWC. Will be used by all
management and analyst
personnel to track and align the
center’s activities to warfighter
needs, report on execution status,
financial status, schedule status,
and performance measurements.
Improving
service or
performance
Planned Yes No No
Safety Automated
System (SAS)
Will query databases to find
automation mishaps. Governed by
Directive 920124 and will allow for
the investigation and reporting of
identified automation mishaps.
Improving
safety
Planned Yes No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 30 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Enterprise Business
System
Will support strategic planning,
assist in building scientific and
technical budgets for the Air
Force, and serve as a launch point
for all new programs. Research
and development case files will be
maintained for 75 years; the
activity indexes, catalogs, and
tracks these files.
Improving
service or
performance
Planned No No Yes
Genomic and
Proteomic Results
Analysis
Analyzes National Institutes of
Health’s genetic data.
Analyzing
scientific and
research
information
Operational No No Yes
IG Corporate
Information System
Enhances combat readiness and
mission capabilities for Air Combat
Command units and commanders.
It assists in preparing for and
conducting inspections.
Improving
service or
performance
Operational Yes No No
Computer Network
Defense System
Evaluates network activities to
create rules for intrusion detection
system signature sets.
Improving
information
security
Operational No No No
FAME Will serve as a central repository
for Air Force manpower
information. Will track manpower
and unit authorization funding.
Managing
human
resources
Planned No No Yes
Resource Wizard Serves as a manpower tracking
system. Tracks positions and
captures data for specific funding
purposes.
Improving
service or
performance
Operational No No No
Government
Purchase Card
Is used in overseeing purchases
made by Air Force personnel with
government-provided credit cards.
Detecting
fraud, waste,
and abuse
Operational Yes Yes No
Ambulatory Data
System Queries
Tracks the initial diagnosis of
patients with the results of further
testing and diagnosis. Allows for
early notification of diseases and
injuries.
Monitoring
public health
Operational Yes No No
Modus Operandi
Database
Is an investigative tool used to
identify and track trends in
criminal behavior. It links
characteristics of crimes and
provides details on crime scenes
and other crime factors.
Detecting
criminal
activities or
patterns
Operational Yes No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 31 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Executive Decision
Support System
Takes data from all functional
metric balances. Processes charts
and graphs to identify trends and
to make sure goals are
accomplished.
Improving
service or
performance
Operational No No No
Inspire Is a tool that assists in providing a
narrative description of all
research and development that is
being conducted within the Air
Force. Provides cost and
milestone information on research
and development projects.
Performing
strategic
planning
Operational Yes No Yes
Discoverer Is used to manage personnel
records, including individual
aliases and histories.
Managing
human
resources
Operational Yes No No
Requirements and
Concepts System
Will serve as a repository for new
system projects and system
requirements. It will be available
for consultation for information on
all project requests and identified
requirements.
Improving
service or
performance
Planned No No No
Business Objects Is a commercial off-the-shelf tool
that is used to analyze and report
on human resources activities.
Managing
human
resources
Operational Yes No Yes
THRMIS Uses commercial off-the-shelf
software to maintain a data
warehouse of integrated inventory
and manpower data for the Total
Force: active duty (officer and
enlisted), Air Force Reserve, Air
National Guard, and civilians. Is
used to assess and analyze the
health of the Air Force.
Managing
human
resources
Operational Yes No No
SAS Is a Web-enabled personnel data
system that gives authorized
users worldwide the ability to
tabulate demographic data on
recruitment, promotion, and
retention.
Managing
human
resources
Operational Yes No No
Oracle HR Is a personnel management
system that manages information
for promotions, pay grades,
clearances, and other information
relevant to human resources.
Managing
human
resources
Operational Yes No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 32 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Health Modeling and
Informatics Division
Data Mart
Provides information and decision
support to the Air Force
headquarters’ surgeon general for
decision making, policy
development, and resource
allocation. It also provides
performance information and
analysis to medical field units in
support of performance
measurement objectives.
Improving
service or
performance
Operational Yes No No
FIRST EDV (BRIO) Will deal with Air Force budgets
and other components of its
financial environment. Historical
analyses and trend analyses will
be performed on the budget
process.
Improving
service or
performance
Planned No Yes No
IG World Is used to store and track data and
requirements, such as lodging and
augmentee requirements, for the
PAC inspector general.
Improving
service or
performance
Operational Yes No No
Department of Defense Headquarters
Automated
Continuing
Evaluation System
Will be used to improve personnel
security continuing evaluation
efforts within Department of
Defense (DOD) by identifying
issues of security concern
between the normal
reinvestigation cycle for those who
hold DOD security clearances and
have signed a consent form that is
still in effect.
Managing
human
resources
Operational Yes Yes Yes
Department of the Navy
Human Resource
Trend Analysis
Is used to improve Navy
readiness. Data on personnel
manning levels are mined to
ensure that each Navy unit has
the correct number of training
personnel aboard.
Managing
human
resources
Operational No No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 33 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
U.S. Naval Academy Allows for the assessment of
academic performance of
midshipmen. It includes
demographic information,
information on grades,
participation in sports, leadership
positions, etc. It is an extension of
the registrar’s system and is
mined for comparisons and
trends.
Managing
human
resources
Operational Yes No No
Navy Training Master
Planning System
Provides overall Navy training
information to assist in delivering
Navy training in the most efficient
manner. Pertinent data from
multiple databases are
consolidated into a single
database that is mined.
Managing
human
resources
Operational Yes Yes No
DHAMS
Multidimensional
Cubes
Is a database that contains
information on the time and
attendance of 3,000 mariners
across 120 ships. Allows
managers to look at what people
were doing at a particular time and
to look across the fleet as a whole
and compare ship activities.
Improving
service or
performance
Operational No No No
National Cargo
Tracking Plan Cargo
Tracking Division
Is used to conduct predictive
analysis for counterterrorism,
small weapons of mass
destruction proliferation, narcotics,
alien smuggling, and other high-
interest activities involving
container shipping activity.
Analyzing
intelligence
and detecting
terrorist
activities
Operational No Yes No
Supply Management
System
Multidimensional
Cubes
Reduces downtime on ships by
allowing for the analysis of ship
parts information. The data
warehouse contains data on every
part that has been ordered since
the 1980s, and has
multidimensional information on
each part. Failure rates can be
calculated and improvements can
be identified.
Improving
service or
performance
Operational No No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 34 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Type Commanders
Readiness
Management System
Is designed to provide a fully
integrated environment for online
analytical processing of readiness
indicators. Examples of readiness
indicators include status of
supplies available, equipment in
operation, health status, and
capabilities of the crew.
Measuring
military
readiness
Operational No No Yes
FATHOM (APMC—
Human Resources)
Will be an internal program and
project tool used to improve
staffing, recruiting, and managing
day-to-day operations.
Managing
human
resources
Planned Yes No No
Navy Training Quota
Management System
Is used for planning and
forecasting training needs based
on skill requirements.
Improving
service or
performance
Operational No No Yes
National Geospatial-Intelligence Agency
OLAP (On-Line
Analytical
Processing)
Will provide aggregations of
imagery system performance data
for management officers and
senior source decision makers to
characterize system performance
and contribution to intelligence
issues of national priority.
Improving
service or
performance
Planned No No No
CITO Data Mining Will evaluate and identify imagery
system performance trends for
optimization, monitoring, or
reengineering.
Improving
service or
performance
Planned No No No
Information
Relevance Prototype
Will establish an information
relevancy prototype to serve as a
framework for community
evaluation of commercial
information relevance
approaches, methods, and
technology. The term information
relevance refers to the ability of
users to receive or extract, then
display and describe, information
with measurable satisfaction
according to their need.
Improving
service or
performance
Planned No No No
U.S. Marine Corps
Operational Data
Store Enterprise
Is used for workforce planning. Managing
human
resources
Operational Yes No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 35 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Source: Department of Defense.
Global Combat
Support Systems—
Marine Corps
Will be a physical implementation
of the IT enterprise architecture
designed to support both
improved and enhanced marine
air/ground task force combat
service support functions and
commander and combatant
commander joint task force
combatant support information
requirements. Data mining will
allow for interoperability with
legacy Marine Corps systems and
allow for a shared data
environment.
Improving
service or
performance
Planned No Yes No
Total Force Data
Warehouse
Is a system whose primary
purpose is workforce planning and
workforce policy decision making.
It contains current (after 30 days)
and historical workforce data.
Managing
human
resources
Operational Yes No No
Marine Corps
Recruiting
Information Support
System
Is a Web-based information
system used for managing assets
and tracking enlisted and officer
accessions into the Marine Corps.
Managing
human
resources
Operational Yes No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 36 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 5: Department of Education’s Inventory of Data Mining Efforts
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Citizenship of PLUS
Loan Borrowers—
National Student
Loan Data Systems
Looks for issues regarding
citizenship among its PLUS loan
borrowers. Flags records based
on selected criteria and requests
additional information from
schools.
Improving
service or
performance
Operational Yes Yes Yes
Foreign Schools
Initiatives National
Student Loan Data
System/Central
Processing
Is a proactive investigation effort
that looks at whether financial aid
was granted individuals attending
foreign institutions during periods
of nonenrollment.
Detecting
criminal
activities or
patterns
Operational Yes No Yes
Professional
Judgment Practices:
Title IV Pell Grants,
National Student
Loan Data
Used to determine when
professional judgment has been
exercised for “special” situations
where families cannot afford
college expenses.
Improving
service or
performance
Operational Yes Yes Yes
Title IV Applicant—
Death Database
Match
Compares Department of
Education data with the Social
Security Administration’s death
database to detect fraud or
criminal activity.
Detecting
fraud, waste,
and abuse
Operational Yes No Yes
Title IV Loans with
No Applications
Will compare information from the
Free Application for Federal
Student Aid Program with the
Federal Family Education Loan
Program to identify fraud.
Detecting
fraud, waste,
and abuse
Planned Yes No No
OIG—Project
Strikeback
Compares Department of
Education and Federal Bureau of
Investigation data for anomalies.
Also verifies personal identifiers.
Analyzing
intelligence
and detecting
terrorist
activities
Operational Yes No Yes
Accuracy of U.S.
Department of
Education Personal
Data
Audits and verifies personal
information that is contained in the
Department of Education’s
personal data system.
Detecting
fraud, waste,
and abuse
Operational Yes No Yes
Impact of Cohort
Default Rate
Redefinition—
National Student
Loan Data System
Audits data to determine the
impact of legislation that extended
the college loan repayment default
period from 180 to 270 days.
Legislative
impact
Operational Yes No No
Page 37 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
CheckFree
Software/Purchase
Card Program
Takes monthly billing information
from the Bank of America to
create reports on purchases,
purchase quantity, and frequency
of purchases. Data are mined for
instances of fraud or abuse.
Detecting
fraud, waste,
and abuse
Operational Yes Yes No
Improper Pell Grant
Payment Activity
Will compare Pell Grants issued
with the amounts received and
look at the eligibility of grant
recipients.
Detecting
fraud, waste,
and abuse
Planned Yes No No
Title IV Identity Theft
Initiative
Helps identify patterns and trends
in identity theft cases involving
loans for education. Provides an
investigative resource for victims
of identity theft.
Detecting
criminal
activities or
patterns
Operational Yes No No
Title IV Applicant—
Use of Multiple
Addresses/Central
Processing System
Reviews addresses listed on Title
IV applications to see if they are
valid. For example, jails or
employment addresses are not
considered valid addresses.
Improving
service or
performance
Operational Yes No Yes
Lapsed
Funds/Improper
Draw of Federal
Grant Proceeds
Identifies funds that remain in the
grants and payment processing
system beyond the time period for
allocating the funds.
Improving
service or
performance
Operational No No No
Decision Support
System with Online
Analytical Processing
Query
Will support the department’s
performance-based initiative. Will
allow custom queries of schools
from state and local databases for
demographics and test scores.
Improving
service or
performance
Planned No No No
Grant Administration
and Payment System
Assists in managing grant
activities and aids in detecting
instances of fraud or abuse in
grant activities.
Detecting
fraud, waste,
and abuse
Operational Yes Yes Yes
Budget Execution
Support
Uses information in the National
Student Loan Data System and a
sample drawn from it to estimate
cohort distributions for financial
activities related to the Federal
Family Education Loan Program
pursuant to the Credit Reform Act.
Financial
management
Operational Yes No No
Pell Grant Model
Assumptions
Provides estimates on the total
cost of the Pell Grant program. It
uses data from previous years and
makes assumptions for future
years.
Financial
management
Operational No No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 38 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Source: Department of Education.
National Student
Loan Data System
Compiles student loan information
from the guaranteeing agencies.
Is used for eligibility tracking and
to calculate default rates.
Detecting
fraud, waste,
and abuse
Operational Yes No Yes
Loan Model
Assumptions
Estimates the cost of loan
programs. Also analyzes loan
default behavior.
Financial
management
Operational Yes No Yes
Office of the
Inspector General
(OIG) Projects:
Tumbleweed/
Snowball
Is part of an OIG investigation to
determine potential fraud of
financial aid grants primarily in
New Hampshire.
Detecting
criminal
activities or
patterns
Operational Yes No Yes
Central Processing
System
Processes applications for student
aid. Contains data on more than
13 million applications. Data are
mined for demographic trends.
Detecting
fraud, waste,
and abuse
Operational Yes No No
Direct Loan Services
System
Is used to track the life of student
direct loans and to monitor loan
repayments.
Improving
service or
performance
Operational Yes Yes Yes
CheckFree
Software/Travel Card
Program
Uses monthly billing information
from Bank of America to create
reports on travel expenditures to
look for improper use of travel
cards.
Detecting
fraud, waste,
and abuse
Operational Yes Yes No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 39 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 6: Department of Energy’s Inventory of Data Mining Efforts
Source: Department of Energy.
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Counterintelligence
Automated
Investigative
Management System
(CI-AIMS)
Is an investigative management
system used by Department of
Energy (DOE) field sites to track
investigative cases on individuals
or countries that threaten DOE
assets. Information stored in this
database is also used to support
federal and state law enforcement
agencies in support of national
security.
Detecting
criminal
activities or
patterns
Operational Yes No No
Autonomy Will be used to mine a myriad
intelligence-related databases
within the intelligence community
to uncover criminal or terrorist
activities relating to DOE assets.
Detecting
criminal
activities or
patterns
Planned Yes No No
Counterintelligence
Analytical Research
Data System
(CARDS)
Is used to log briefings and
debriefings given to DOE
employees who travel to foreign
countries or interact with foreign
visitors to DOE facilities. Data are
mined to identify potential threats
to DOE assets.
Detecting
criminal
activities or
patterns
Operational Yes No Yes
Page 40 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 7: Department of Health and Human Services’ Inventory of Data Mining Efforts
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector data
Other
agency data
Agency for Healthcare Research and Quality
National Patient
Safety Network
Will contain reports on adverse
medical events that are filed by
hospitals. The planned network’s
purpose is to take out patient
personal identifiers and other
items that may violate certain
rules and create a warehouse that
can be used by registered and
unregistered users to evaluate and
implement patient safety and
quality measures. The network will
be used to create tools that
hospitals can use for making
quality improvements.
Improving
service or
performance
Planned No No No
Centers for Disease Control and Prevention
BioSense Enhances the nation’s capability to
rapidly detect bioterrorism events.
Analyzing
intelligence
and detecting
terrorist
activities
Operational No Yes Yes
Department of Health and Human Services Headquarters
DHHS Blood
Monitoring Program
Monitors the country’s blood
supply by keeping an inventory on
red blood cells and platelets and
monitors blood supply shortages,
the nature of the shortage, and
size of the shortages.
Monitoring
public health
Operational No Yes No
Food and Drug Administration
Mission
Accomplishment and
Regulatory
Compliance Services
System
Is a comprehensive redesign and
reengineering of two core mission-
critical legacy systems at Food
and Drug Administration (FDA)
that support the regulatory
functions that primarily take place
in FDA’s field offices.
Monitoring
food or drug
safety
Operational No Yes Yes
Page 41 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Turbo Establishment
Inspection Report
Provides a standardized database
of citations of regulations and
statutes, and help investigators in
preparing reports. It will collect
data on specific observations
uncovered during inspections and
provide a more uniform format
nationwide that will allow for
electronic searches and statistical
analysis to be performed by
citation.
Improving
safety
Operational No Yes No
Phonetic
Orthographic
Computer Analysis
Is a search engine that provides
results indicating how similar two
drug names are on a phonetic and
orthographic basis. Its purpose is
to help in the safety evaluation of
proposed proprietary names to
reduce drug name confusion after
an application is approved by the
FDA.
Improving
safety
Operational No Yes No
MPRIS Data
Warehouse
Will provide data to support end
user ad-hoc query analysis and
standard reporting needs. It will
provide the foundation for a central
reporting repository that can be
used to populate business-specific
data marts.
Improving
service or
performance
Planned No No No
Development and
Deployment of
Advanced Analytical
Tools for Drug Safety
Risk Assessment
Will develop advanced software
tools for quantitative analysis of
drug safety data. Medical officers
and safety evaluators will use
these advances in software tools.
Analyzing
scientific and
research
information
Planned Yes Yes Yes
Add data mining
capability to CFSAN
Adverse Event
Reporting System
Is a comprehensive system for
tracking, reviewing, and reporting
adverse event incidences involving
foods, cosmetics, and dietary
supplements. Integrating and
centralizing the system and
eliminating patchwork systems
make information on these
adverse events available to
federal, state, and local
governments as well as to industry
and the public in a more timely
and efficient manner.
Monitoring
food or drug
safety
Planned Yes Yes Yes
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector data
Other
agency data
Page 42 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Source: Department of Health and Human Services.
Table 8: Department of Homeland Security’s Inventory of Data Mining Efforts
Health Resources and Services Administration
HRSA Geospatial
Data Warehouse
Data warehouse that primarily
collects programmatic,
demographic, and statistical data.
Improving
service or
performance
Operational No Yes Yes
Program Support Center
Employee Assistance
Program Analysis
Uses information from a database
of employee assistance program
case information that does not
contain client personal identifiers.
Data are mined for quality
assurance and program
management information that is
used to enhance the quality and
cost effectiveness of services.
Improving
service or
performance
Operational No No No
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector data
Other
agency data
Border and Transportation Security Directorate
Workforce Profile
Data Mart
Contains payroll and personnel
data and is mined for workforce
trends.
Managing
human
resources
Operational Yes No Yes
Customs Integrated
Personnel Payroll
System Data Mart
Is a Customs data mart contained
within Department of Homeland
Security’s workforce profile data
mart. Personnel and payroll data
are mined for workforce trends.
Managing
human
resources
Operational Yes No Yes
Internal Affairs
Treasury
Enforcement
Communications
System Audit Data
Mart
Assists the Internal Affairs group by
mining criminal activity data to
ascertain how Customs’ employees
are using the Treasury Enforcement
System.
Detecting
criminal
activities or
patterns
Operational Yes No Yes
Operations
Management
Reports Data Mart
Assists in managing the operation
of all ports of entry for incoming
carriers, people, and cargo. Helps
in making resource (people and
equipment) allocation and
operational improvement decisions.
Improving
service or
performance
Operational No No Yes
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector data
Other
agency data
Page 43 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Automated Export
System Data Mart
Mines data on export trade in the
U.S. and produces reports on
historical shipping and receiving
trends.
Improving
service or
performance
Operational No Yes Yes
Seized Property/
Forfeitures,
Penalties, and Fines
Case Management
Data Mart
Mines data to ensure data quality
and review work assignments.
System has two components: one
that processes legal cases like a
law firm, and a second that serves
as property and inventory control by
tracking property seized.
Improving
service or
performance
Operational Yes No No
Incident Data Mart Will look through incident logs for
patterns of events. An incident is an
event involving a law enforcement
or government agency for which a
log was created (e.g., traffic ticket,
drug arrest, or firearm possession).
The system may look at crimes in a
particular geographic location,
particular types of arrests, or any
type of unusual activity.
Analyzing
intelligence
and detecting
terrorist
activities
Planned Yes Yes Yes
Case Management
Data Mart
Assists in managing law
enforcement cases, including
Customs cases. Reviews case
loads, status, and relationships
among cases.
Analyzing
intelligence
and detecting
terrorist
activities
Operational Yes Yes Yes
Emergency Preparedness and Response Directorate
Enterprise Data
Warehouse
Will take data from multiple,
disparate systems and integrate the
data into one reporting
environment. The objective of the
effort is to allow for the reduction of
data within the agency and to
provide an enterprise view of
information necessary to drive
critical business processes and
decisions. Data on internal human
resources, all aspects of disaster
management, infrastructure,
equipment location, etc., will be
used.
Disaster
response and
recovery
Planned Yes Yes Yes
Information Analysis and Infrastructure Protection Directorate
Analyst Notebook I2 Correlates events and people to
specific information
Analyzing
intelligence
and detecting
terrorist
activities
Operational Yes Yes No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector data
Other
agency data
Page 44 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Source: Department of Homeland Security.
Automatic Message
Handling System
(Verity)
Automatically takes messages from
external agencies and routes them
to appropriate recipients
Analyzing
intelligence
and detecting
terrorist
activities
Planned No No Yes
U.S. Coast Guard
Readiness
Management
System
Assists in ensuring readiness for all
Coast Guard missions.
Improving
service or
performance
Operational Yes No No
CG Info Provides one-stop shopping for
Coast Guard information. It is the
central location and common
interface for the entire Coast Guard
to gain near real-time access to
data from multiple, disparate Coast
Guard information systems. It
provides a single interface for users
to view mission-critical support
data.
Improving
service or
performance
Operational Yes No Yes
U.S. Secret Service
Criminal
Investigation
Division Data Mining
Mines data in suspicious activity
reports received from banks to find
commonalities in data to assist in
strategically allocating resources.
Detecting
criminal
activities or
patterns
Operational Yes No Yes
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector data
Other
agency data
Page 45 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 9: Department of the Interior’s Inventory of Data Mining Efforts
Source: Department of the Interior.
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Minerals Management Service
Data Mining of the
Technical Information
Management System
(TIMS) Database
Is a corporate database for oil and
gas leases. The database is
mined in support of policy
development. One area of data
mining is identification of leases
that will be abandoned in the near
future. Data mining has shown
that leases with six or more
producing wells in 1 year are
almost never abandoned in the
next year. Another application of
data mining is the safety of oil and
gas operations. For example, data
mining has shown that accidents
have a peak rate on Thursday
mornings.
Improving
service or
performance
Operational Yes Yes No
Page 46 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 10: Department of Justice’s Inventory of Data Mining Efforts
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Department of Justice Headquarters
Drug/Financial
Fusion Center
Will contain data from, and be
used by, Organized Crime and
Drug Enforcement Task Force
agencies. The system will permit
the collection and cross case
analysis of all drug and related
financial investigative data.
Detecting
criminal
activities or
patterns
Planned Yes Yes Yes
Drug Enforcement Administration
Statistical
Management
Analysis and
Reporting Tool
System (SMARTS)
/SPSS
Is a query analysis and reporting
tool that pulls data from many
systems. It allows for statistical
analyses of drug cases Drug
Enforcement Administration’s
statistical reporting.
Detecting
criminal
activities or
patterns
Operational Yes No Yes
TOLLS Is a database of telephone calls
from court ordered and approved
wiretaps and Title III
investigations. Information such
as telephone numbers, time and
date of calls, and call duration is
captured. Data are mined for
patterns to give leads in
investigations of drug trafficking.
Detecting
criminal
activities or
patterns
Operational Yes No No
Federal Bureau of Investigation
Secure Collaborative
Operational
Prototype
Environment/
Investigative Data
Warehouse
Allows the FBI to search multiple
data sources through one
interface to uncover terrorist and
criminal activities and
relationships. Data sources are a
combination of structured and
unstructured text.
Analyzing
intelligence
and detecting
terrorist
activities
Operational Yes No Yes
Foreign Terrorist
Tracking Task Force
Activity
Supports the Foreign Terrorist
Tracking Task Force that seeks to
prevent foreign terrorists from
gaining access to the United
States. Data from the Department
of Homeland Security, Federal
Bureau of Investigation, and public
data sources are put into a data
mart and mined to determine
unlawful entry and to support
deportations and prosecutions.
Analyzing
intelligence
and detecting
terrorist
activities
Operational Yes Yes Yes
Page 47 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Source: Department of Justice.
FBI Intelligence
Community Data
Marts
Is intended to take a subset of
approved data from a data
warehouse and make it available
to the intelligence community.
Analyzing
intelligence
and detecting
terrorist
activities
Planned Yes No Yes
Federal Bureau of Prisons
Business Information
Warehouse
Will be a warehouse designed to
provide information on
manufacturing by Federal Prison
Industries, which runs 100
factories in various prisons. Data
will be mined for information on
the manufacturing environment
(such as information on material
on hand, scheduling, and the
production process) and financial
activities.
Improving
service or
performance
Planned No No Yes
U.S. Marshals Service
USMS Workload
Modeling
Will seek to develop a workforce
model that will support budget
formulation, execution, and
resource analysis. Will be a
planning and execution activity
that will be used to help determine
the quantity and location of
required resources.
Managing
human
resources
Planned Yes No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 48 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 11: Department of Labor’s Inventory of Data Mining Efforts
Source: Department of Labor.
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Dashboard Display Provides links to programs
throughout the Department of
Labor’s Employment Training
Administration to provide reports
or information on financial
activities.
Improving
service or
performance
Operational Yes No No
Enforcement
Management
System, Case
Opening, and
Results Analysis
Is used to track investigations of
violations of Title I and other
criminal laws pertaining to
pension and welfare rights.
Improving
service or
performance
Operational Yes Yes No
Employee
Retirement Income
Security Act Data
System
Is used to monitor compliance
with Title I of the Employee
Retirement Income Security Act.
Detecting
fraud, waste,
and abuse
Operational Yes No No
Mine Safety and
Health Administration
Teradata Data Store
Mines data from a data store of
information on safety and health
enforcement and demographic
data for mine operations, along
with miner accidents, injury, and
illness data.
Improving
safety
Operational Yes No Yes
Mathematical
Statistics Research
Center
Will look at data from economic
surveys to compare rates of
nonresponse for Bureau of Labor
Statistics.
Improving
service or
performance
Planned No No No
Page 49 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 12: Department of State’s Inventory of Data Mining Efforts
Source: Department of State.
Table 13: Department of Transportation’s Inventory of Data Mining Efforts
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Citibank’s Ad Hoc
Reporting System
Enables purchase card managers
to track trends related to the
usage of credit cards by
employees in purchasing supplies
and services for official use.
Purchase card program is
worldwide, and spending patterns
and purchases are monitored for
potential misuse or fraud.
Detecting
fraud, waste,
and abuse
Operational Yes Yes No
Purchase Card
Management System
Will involve the automation of
internal workflow processes
(system is in the early phases of
development). Will use internal
data and bank data to track trends
and anomalies in the Department
of State’s worldwide purchase
card program.
Detecting
fraud, waste,
and abuse
Planned Yes Yes No
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Department of Transportation Headquarters
DOT IT Security
Management System
Will collect information to allow
management to assess its IT
security infrastructure.
Detecting
fraud, waste,
and abuse
Planned Yes No No
National Highway Traffic Safety Administration
State Data System Analyzes, mines, and researches
automotive crash data, such as
statistics from rollovers of SUVs,
from 22 states to improve highway
safety and lessen fatalities.
Policies can be set based on the
data.
Improving
safety
Operational No No No
Page 50 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Source: Department of Transportation.
Table 14: Department of the Treasury’s Inventory of Data Mining Efforts
Fatality Analysis
Reporting System
(FARS)
Helps to evaluate the
effectiveness of motor vehicle
safety standards and highway
safety programs. Data are
collected from all 50 states, the
District of Columbia, and Puerto
Rico and are used to evaluate and
support highway safety.
Improving
safety
Operational Yes Yes Yes
National Automotive
Sampling System
Collects and mines information on
automotive crashes. System is
related to the Federal Motor
Vehicle Safety Standards that
regulate vehicle compliance items
such as seat belts, air bags, and
the stopping distance of brakes.
Improving
safety
Operational Yes Yes No
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Financial Management Service
Treasury Offset
Program (TOP)
Cleanup
Mines data to reduce the number
of debts listed in TOP.
Improving
service or
performance
Operational Yes No Yes
Electronic Federal
Tax Payment System
(EFTPS) Marketing
Is a free service offered by the
Department of the Treasury for
individuals and business
taxpayers who pay their federal
taxes electronically. Mining activity
tracks enrollment, tax payment
history, and usage trends.
Increasing tax
compliance
Operational Yes No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 51 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Internal Revenue Service
Planning, Analysis,
and Decision
Support System
Will be a component of the
Custodial Accounting Program,
which is the warehouse that is
used to query transactional data
and produce reports. This activity
is meant to improve reporting and
use decision support tools.
Improving
service or
performance
Planned Yes No Yes
Abusive Corporate
Tax Shelter Detection
Model
Will model characteristics of
corporate tax shelters and use
models to predict corporate tax
shelter abuse and to assess
compliance risk in the corporate
taxpayer population.
Increasing tax
compliance
Planned Yes Yes No
K-1 Link Analysis Will be used to detect potential tax
evasion.
Increasing tax
compliance
Planned Yes No No
Research on the
Population of
Taxpayers Who
Receive Earned
Income Tax Credit
Will be used to research data on
taxpayers who receive the EITC.
Detecting
fraud, waste,
and abuse
Planned Yes No No
Issue Based
Management
Information System
Will provide access to a variety of
data sources within IRS. Will
assist in research and case work.
Increasing tax
compliance
Planned No Yes No
Electronic Fraud
Detection System
Mines data to evaluate and rate
potentially fraudulent individual tax
returns.
Improving
service or
performance
Operational Yes No No
Reveal Will be used to detect financial
criminal activity such as tax
evasion.
Detecting
criminal
activities or
patterns
Planned Yes Yes No
Oracle Model 22
Partnership Return
Scoring System
Takes information from individual
tax returns and attempts to
replicate judgments made by
taxpayers to detect the likelihood
of material errors.
Increasing tax
compliance
Operational Yes No No
SPSS Form 1120-S
Return Scoring
System
Will automate the classification of
certain corporate tax returns.
Increasing tax
compliance
Planned Yes No No
Oracle Model 33
Partnership Scoring
Model
Will identify noncompliance in
partnership returns.
Increasing tax
compliance
Planned Yes No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 52 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Source: Department of the Treasury.
Compliance
Laboratory
Will identify taxpayer
noncompliance by looking at
groups of returns.
Increasing tax
compliance
Planned Yes Yes Yes
U.S. Mint
Information
Technology Intrusion
Detection System
Collects information on potential
intrusions to U.S. Mint systems.
Looks for trends in information
reported by sensors to determine
if illicit activity has occurred.
Minimizes false positives.
Improving
information
security
Operational No No No
E-Commerce Fraud
Analysis Activity
Attempts to identify and stop
fraudulent activity involving stolen
credit cards to order products over
the Internet or via telephone.
Fraud rating identifiers are used to
identify areas where fraud has
occurred and to determine the
likelihood of fraud. Allows for
orders to be stopped or for orders
over a certain dollar limit to be
stopped.
Detecting
criminal
activities or
patterns
Operational Yes Yes Yes
Data Warehouse Will be an integrated, scalable,
expandable data warehouse that
will support business functions by
grouping the data in subject-
oriented data marts. Each
warehouse data mart will be
defined to integrate both internal
and external data to provide the
necessary information to perform
both historical and predictive
analysis and support numerous
calculations.
Improving
service or
performance
Planned No No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 53 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 15: Department of Veterans Affairs’ Inventory of Data Mining Efforts
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Department of Veterans Affairs Headquarters
Veterans Affairs
Central Incident
Response Center
Is used to monitor and manage
intrusion detection and firewalls.
Scripts are written for forensic
analysis to go through data
collected from system and
network logs.
Detecting
criminal
activities or
patterns
Operational Yes Yes No
Purchase Card Data
Mining (SAS)
Reports
Will identify patterns in purchase
card use to identify fraud and
misuse and to maintain good
internal controls.
Detecting
fraud, waste,
and abuse
Planned Yes Yes No
Travel Card Data
Mining (SAS)
Reports
Will be used to look for patterns in
the use of travel credit cards that
indicate misuse or fraud and to
maintain good internal controls.
Detecting
fraud, waste,
and abuse
Planned Yes Yes No
Office of Inspector
General (OIG)
Analyzes and matches (within the
guidelines of the law) Veterans
Affairs (VA) files, pertaining to both
VA-provided benefits and health
care services to detect patterns of
waste, fraud, or abuse.
Detecting
fraud, waste,
and abuse
Operational Yes No No
Veterans Benefits Administration
C & P Payment Data
Analysis
Analyzes compensation and
pension data to detect fraud,
waste, and abuse.
Detecting
fraud, waste,
and abuse
Operational Yes No Yes
C & P Large Payment
Verification Process
Serves as an internal control
intended to make sure that
payments over a certain dollar
threshold are reviewed to detect
potential fraud or abuse.
Detecting
fraud, waste,
and abuse
Operational Yes No No
Veterans Health Administration
Primary Analysis and
Classification
Is used mainly to discover trends,
incidents/events, and
vulnerabilities that may exist in VA
hospitals.
Improving
safety
Operational No No No
Allocation Resource
Center Database
Is used in making resource
allocation decisions based on the
analysis of patient workload and
cost data.
Improving
service or
performance
Operational Yes No No
Page 54 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Source: Department of Veterans Affairs.
Veteran’s Health
Administration (VHA)
Financial and Clinical
Data Mart
Integrates patient, clinical, and
financial data to present a unified
management perspective and
enable consistent reporting.
Improving
service or
performance
Operational Yes No No
Decision Support
System
Is used to identify patterns of care
and patient outcomes linked to
resource consumption and costs
associated with each patient
encounter.
Improving
service or
performance
Operational Yes No No
Top 50
Standardization
Listing/Managed
Inventory System
Is used to standardize medical
and hospital supplies and
equipment to (1) improve VHA’s
bargaining position when soliciting
bids and (2) facilitate the ability to
move doctors among hospitals.
Improving
service or
performance
Operational No Yes No
VISN 16 Data
Warehouse
Provides unified view of the VISN
16 VA region, composed of 10
medical centers and 30 outpatient
clinics. The system gives a view of
the enterprise for management
purposes. It is mined for a variety
of types of information such as
patient encounters, lab tests,
pharmacy records, etc.
Improving
service or
performance
Operational Yes No No
(Continued From Previous Page)
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 55 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 16: Environmental Protection Agency’s Inventory of Data Mining Efforts
Source: Environmental Protection Agency.
Table 17: Export-Import Bank of the United States’ Inventory of Data Mining Efforts
Source: Export-Import Bank of the United States.
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Conceptual Plans to
Design an Approach
and System to
Review Financial
Data
Will regularly review financial data
systems for contracts, bank cards,
and small purchases and other
financial databases for misuse or
fraud of Environmental Protection
Agency’s assets.
Detecting
fraud, waste,
and abuse
Planned Yes No No
Drinking Water Data
Warehouse
Integrates and analyzes drinking
water information from state,
regional, and headquarters
sources. Includes data on water
systems, compliance, sample
analytical results, and audit data.
Monitoring
public health
Operational Yes No Yes
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Integrated
Information System
Data Warehouse
Mining for Financial
Risk Information
Is used to generate reports that
describe bank lending activities
and exposure trends.
Improving
service or
performance
Operational Yes No No
Page 56 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 18: Federal Deposit Insurance Corporation’s Inventory of Data Mining Efforts
Source: Federal Deposit Insurance Corporation.
Table 19: Federal Reserve System’s Inventory of Data Mining Efforts
Source: Federal Reserve System.
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Real Estate Stress
Test (RSST)
Is used to measure real estate
risk. Bank examiners use data
from the system data as part of a
pre-examination planning process
to assist in identifying risk
concentrations.
Detecting risk
in financial
systems
Operational No No Yes
Determination of
Insured Deposits
Will support the development of a
new system for implementing the
deposit insurance claims.
Improving
service or
performance
Planned Yes No No
Statistical CAMELS
Offsite Review
Is used to rate financial
institutions’ performance and risk
management practices.
Detecting risk
in financial
systems
Operational No No Yes
Growth Monitoring
System
Is used to identify financial
institutions that have experienced
significant growth. Serves as an
early warning system for detecting
financial institutions that might
pose financial risk to FDIC.
Detecting risk
in financial
systems
Operational No No Yes
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Office of the
Inspector General
(OIG), Audit Services
Will support audits and
evaluations. Using ACL, queries
will be run against the board’s
financial and personnel systems
to detect fraud, waste, and abuse,
or to provide information
supporting any aspect of an OIG
project.
Detecting
fraud, waste,
and abuse
Planned Yes No No
Page 57 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 20: National Aeronautics and Space Administration’s Inventory of Data Mining Efforts
Features
Organization/ system
name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Archiving of Web
Information at National
Aeronautics and
Space Administration
(NASA) and Goddard
Space Federal Center
(GSFC)
Will gather metadata on the
GSFC Web site at NASA to
preserve NASA legacy
information.
Analyzing
scientific and
research
information
Planned No No Yes
My Goddard Search—
Mining of Goddard’s
Web environment
Will allow Web mining of scientific
data at Goddard Space Center. It
is referred to as “Google for
Goddard.”
Analyzing
scientific and
research
information
Planned No Yes No
NetContext Will monitor network traffic for the
purpose of identifying bandwidth
use, fraud, abuse, and IT security-
related activities.
Detecting
fraud, waste
and abuse
Planned Yes No No
Geophysics Time
Series Analysis
Will develop a set of algorithms to
identify patterns within temporal
activities. The data will be
trajectories of objects and
movement of objects within
images.
Analyzing
scientific and
research
information
Planned No No Yes
“Simmarizer”
(Simulation-Based
Summary/
Discovery of
Knowledge)
Uses data mining techniques to
extract knowledge from
simulators to understand
conditions and scenarios
regarding space missions.
Analyzing
scientific and
research
information
Operational No No No
Global Environmental
and Earth Science
Information System
(GENESIS)
Is used to obtain information
about global climate changes.
Analyzing
scientific and
research
information
Operational No No Yes
Machine Learning and
Data Mining for
Improved Intelligent
Data Understanding of
High Dimensional
Earth Sensed Data
Will find patterns and
relationships in large, complex
earth science data sets,
specifically for rare and small
events hidden in larger data
signals. Will build new capabilities
to understand NASA science
data.
Analyzing
scientific and
research
information
Planned No No Yes
Page 58 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Distributed Data
Mining Techniques for
Object Discovery in
the National Virtual
Observatory (NVO)
Involves using a data mining tool
set for space science research.
Incorporates a small number of
targeted data mining techniques
in order to address specific NASA
space science research
programs. In particular, the data
mining environment will be used
to explore NASA’s large space
science data collections. These
techniques are being applied to
astronomical object discovery,
identification, classification, and
interpretation across large
multiple distributed astronomy
data collections.
Analyzing
scientific and
research
information
Operational No No Yes
Diamond Eye (System
for Mining Images)
Analyzes large sets of images
looking for specific features.
Analyzing
scientific and
research
information
Operational No Yes Yes
Data Mining of 3-D
Numerical Model
Forecast Output and
Its Application to
Atmospheric Research
Will automate the analysis of
weather model output,
observation, and satellite data to
allow for a better understanding of
the science of weather dynamics
and to predict future weather
events.
Analyzing
scientific and
research
information
Planned No No Yes
Ecological Forecasting Will develop an adaptable system
that can be used to mine large
volumes of scientific data, identify
novel causal relationships in the
data about earth system
processes, and rapidly
incorporate discoveries with
biospheric models to generate
now-casts and forecasts of
biospheric events and conditions.
Analyzing
scientific and
research
information
Planned No No Yes
(Continued From Previous Page)
Features
Organization/ system
name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 59 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Distributed Data
Mining for Large NASA
Databases (Earth
Science Earth
Observing System
Data)
Will research changes, trends,
and relationships in Earth
Observing System (EOS) data.
The major feature of this activity is
that it will allow for different data
to be mined in parts and then
merged. The capability is needed
for instances when scientific data
are at different locations. A
research quality software will be
used to allow for a communication
system and run-time environment
for applying a collective data
analysis approach not bound to
any specific platform, learning
algorithm, or representation of
knowledge.
Analyzing
scientific and
research
information
Planned No No Yes
Discovery of Changes
from the Global
Carbon Cycle and
Climate System Using
Data Mining Activity
Will detect patterns in scientific
data that are geospatial and
dynamic and represented as
raster data (gridded cells of
surfaces such as the sun’s or
earth’s surfaces). Mining
capabilities are being developed
for future NASA-relevant data and
science.
Analyzing
scientific and
research
information
Planned No No Yes
“AutoSciProd”
(Automatic Generation
of Science Products
from Large Image
Data Sets)
Uses statistical and image data to
determine and improve science
products.
Analyzing
scientific and
research
information
Operational No No No
Near Archive Data
Mining of Earth
Science Data
Pulls data from an archive of
earth science data and applies
scientists’ analyses and
algorithms to the data.
Analyzing
scientific and
research
information
Operational No No No
Spectral Analysis
Automation (SAA)
System
Will improve the collection,
identification, and evaluation of
spectral data to better meet
scientists’ requirements.
Analyzing
scientific and
research
information
Planned No No No
Multiple Sensor Image
Registration, Image
Fusion and Dimension
Reduction Using
Wavelets
Will be used for collaborative
preprocessing of data and
research on wavelets. Will
comprise research software that
looks at different technologies
such as image processing and
dimensions.
Analyzing
scientific and
research
information
Planned No No No
(Continued From Previous Page)
Features
Organization/ system
name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 60 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Source: National Aeronautics and Space Administration.
GMSEC Event
Message Data Mining
Task
Will be used to determine health
of and reasons for problems with
satellite systems.
Analyzing
scientific and
research
information
Planned No No No
Intrusion Detection
System
Looks at all traffic that traverses
NASA’s networks’ borders.
Improving
information
security
Operational Yes No No
AvSP/ASMM Foreign
Object Detection
Toolset
Is used with simulations to identify
foreign object damage indicators
for commercial jet engines.
Analyzing
scientific and
research
information
Operational No Yes No
Mission and Science
Measurement and
Discovery Systems
Will be a basic technology
research program that will also
support infusion of resulting
technologies into NASA missions.
Purpose of the program is to
solve the research challenge in
extracting the most scientific
knowledge from NASA’s space
missions and data archives.
Analyzing
scientific and
research
information
Planned No No No
StarTool: Solar Active
Region Detection
Is used for recognition of solar
activity in sequences of multiband
solar images.
Analyzing
scientific and
research
information
Operational No No No
“Toogle” (Times-Series
Search Engine)
Searches for time-series data. Is
similar to a Google search
engine.
Improving
safety
Operational No No No
Use of Data Mining,
Remote Sensing, and
Geographic
Information Systems
for Wildfire Detection
and Prediction
Will help the National Oceanic
and Atmospheric Administration
automate its fire detection
systems and improve the
accuracy of fire detection
systems.
Improving
service or
performance
Planned No No Yes
Knowledge Discovery
and Data Mining
Based on Hierarchical
Image Segmentation
Will mine data using software that
has been developed to exploit
information from a hierarchical
image segmentation process.
Analyzing
scientific and
research
information
Planned No Yes Yes
(Continued From Previous Page)
Features
Organization/ system
name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Page 61 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 21: Nuclear Regulatory Commission’s Inventory of Data Mining Efforts
Source: Nuclear Regulatory Commission.
Table 22: Office of Personnel Management’s Inventory of Data Mining Efforts
Source: Office of Personnel Management.
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Licensee Event
Report Data
Identifies nuclear safety trends
and patterns in commercial
nuclear power events.
Improving
safety
Operational No Yes No
Centralized
Information Delivery
Will consolidate and standardize
reporting for nuclear reactor
regulations.
Improving
service or
performance
Planned Yes No No
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
CRIS Retirement
Data Mining Activity
Mines federal employee benefits
data such as information on
retirement and life insurance to
assist in managing federal
employee eligibilities and
entitlements.
Improving
service or
performance
Operational Yes No Yes
Page 62 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 23: Pension Benefit Guaranty Corporation’s Inventory of Data Mining Efforts
Source: Pension Benefit Guaranty Corporation.
Table 24: Railroad Retirement Board’s Inventory of Data Mining Efforts
Source: Railroad Retirement Board.
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Corporate
Performance
Indicators and
Analytics
Will streamline access to
management and operational
performance measures and
permit the correlation of
performance and output
measures.
Improving
service or
performance
Planned No No Yes
Corporate Policy and
Research
Department’s
Forecasting System
Is a stochastic simulation model
that incorporates historic equity
and interest rates and bankruptcy
possibilities to forecast scenarios
for more than 300 pension plans
and their related corporate
sponsors.
Improving
service or
performance
Operational No Yes Yes
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Railroad Retirement
Board Data Stores
Consists of two major databases
(payment and entitlement history
and employment data
maintenance) that are mined by
actuaries to produce annual
actuarial reports and for audit
support and quality control.
Improving
service or
performance
Operational Yes No Yes
Page 63 GAO-04-548 Data Mining
Appendix IV
Inventories of Efforts
Table 25: Small Business Administration’s Inventory of Data Mining Efforts
Source: Small Business Administration.
Features
Organization/
system name Description Purpose Status
Personal
information
Private
sector
data
Other
agency data
Loan Monitoring
System
Helps to identify, measure, and
manage the risk of Small
Business Administration’s
portfolio. Business credit scores
are used but individual credit
scores are not.
Improving
service or
performance
Operational Yes Yes No
MONSTER and
Econometric Models
Mines data from database that
includes all transactions for each
loan that affects SBA subsidy
costs, to assist in determining
credit subsidy rates for SBA’s
various credit programs.
Financial
management
Operational Yes No No
Page 64 GAO-04-548 Data Mining
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- Report to the Ranking Minority Member, Subcommittee on Financial Managem\
ent, the Budget, and International Security, Committee on Governmental A\
ffairs, U.S. Senate - Departments and Agencies Reporting No Data Mining Efforts
May 2004
DATA MINING
Federal Efforts Cover a Wide Range of Uses
Contents
Results in Brief
Background
Data Mining Poses Privacy Challenge
Agencies Identified Numerous Data Mining Efforts with Various Aims
Summary
Objective, Scope, and Methodology
Surveyed Departments and Agencies
Inventories of Efforts
http://www.gao.gov
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