Index

Note: Page numbers followed by “f” and “t” indicate figures and tables respectively

A
A/B testing, 142
Acquisition, data, 131–132, 132t, 135
Actionable information
from Big Data, 204
military and, 81
Actionable information subclass, 86, 86f
Active shooter event, 43
See also Big Data for law enforcement case study and workshop
content-based image retrieval for, 47–48
dynamic classification of Twitter content for, 46–47
FEMA definition of, 41
word cloud visualization for social media and, 46, 47f
Adaptation, for real-time Big Data systems, 104
Adaptive Robust Integrative Analysis for Finding Novel Associations (ARIANA), 204–205
See also Ontology mapping
case studies on, 201–204
data repurposing and AD, 202
lethal drug interaction, 201–202
conceptual framework of, 187–193, 189f
data stratification and POLSA and, 189–190, 191f
features of, 187, 188f
implementation for biomedical applications, 193–200
automatic heading selection, 196–198, 196t
data stratification and POLSA, 195–198, 196t
MGD creation, 195
OM and MGD creation, 194–195
POLSA encoding fine-tuning, 198
reverse OM and I&V, 200
I&V and, 192–193, 193f
reverse OM and, 192
RM and, 187, 190–192, 192f
Adlerian Therapy as a Relational Constructivist Approach, 219
Akbar, Jawad, 24–27
Alzheimer disease (AD), 202
Amazon Elastic MapReduce, 11
Amazon Web Services, 11
Amerithrax case, 55–56
impact of, 57
importance of, 57–58
Iraq War and, 57
pattern analysis and visualization for, 63–64
relevant evidence of, 59–61
sentiment and stylometric analysis for, 61–62
Amerithrax Task Force, 57
Amin, Salahuddin, 25–27
Ammonium nitrate fertilizer, 24, 26
Analytics
See also Big Data analytics, High-performance analytics, Predictive analytics
analysis compared to synthesis for, 151–152
data streaming, 10–11
for business use, 10
geospatial, 10–11
voice and video, 10
deep analytics subclass, 86f
geospatial, 10–11
global analytics vision subclass, 86f
for real-time Big Data systems, 102–104
fault tolerance and, 103
flexibility and adaptation, 104
security and, 103
text, 16–17
voice and video, 10
Anaphora resolution, 161
Anonymous, 113, 115–116
Anthrax mailings of 2001, See Amerithrax case
Anthropology-based computing (ABC), 261, 262f, 267
Apache Cassandra, 145
Apache Hive, 96
Apache Kafka, 98–100, 103
Apache Pig, 96, 101
Application Program Interface (API), 45–46
Artificially intelligent system (AIS)
Big Data concerns in autonomous, 209–210
constructivist learning and, 218–221
memory management in, 210–212
sensory memories, 210, 211f
STMs, 211, 212f
memory processing and encoding in, 212–217
Assessment, of terror threats, 27–29
Association rule learning, 143
Astill, Michael, 27
Attentive processing, 263f, 264, 269–270
Authority for Personal Data Protection, Italy, 242
Authorship attribution, 61–62
Automatic heading selection, for ARIANA implementation, 196–198, 196t
Autonomous decision making, 209–210
Autonomous robotics, 209–210
B
Babar, Mohammed Junaid, 25–26
Bacterial pathogenomics, 59–60
“Batch layer,” real-time Big Data systems, 93, 94f
batch processing into, 95
machine learning and filtering into, 99–100
processing into, 95–96
“Battle rhythm”, 91
Battlefield
intelligence for success on, 81
interconnectivity of, 81–82
“on the fly” processing for, 84–85
in real-time Big Data systems, 91–93, 92f
superscalar datacenter on, 104, 105f
Bayesian networks (BNs)
aggregation of structural and probabilistic data with, 166
automatic extraction from text, 165–166, 165f
crime detection with social media extraction of, 167–169, 168f
example of, 170, 169
general architecture for, 167–169, 170f
dependence relation extraction from text, 165–166
for NLP, 163–164, 164f
probability information extraction and, 166
structure of, 166
variables identification for, 166
Behavioral analysis
See also Amerithrax case
algorithmic techniques for, 59
decision making and, 56
visualization for, 63–64
Big Data
See also Implementation, Real-time Big Data systems
actionable information from, 204
advancements of, 4–5
advantages and applications of, 7–9
age of, 14–15
on Amazon Web Services, 11
anticipated growth of, 15f
attractions of, 229
autonomous AIS concerns for, 209–210
business needs and, 9
CI strategic landscape for applying, 69–73
clouds for, 11–12
counterterrorism with, 34–35
definition of, 3–4, 39
dynamic nature of, 3, 6
expense of, 83
five V’s of, 5–7
value, 7
variety, 6–7
velocity, 6, 39
veracity, 7
volume, 6
health care applications of, 9
human interaction with, 261–262
importance of, 7
interpretation, 250, 254–257
for law enforcement, 39–41, 108
dilemmas of, 230, 236–237
human rights in EU and, 231–233
legal framework in EU for, 230–236
potential of, 51–52
public trust and confidence in EU with, 234–236
purpose limitation in EU and, 233–234
sources for, 39–40
UK integration of, 109
military and, 82–83
canonic use cases for, 87–89, 87t
correlation over space and time for, 88–89
digitalized world and, 89–91
filtering for, 88, 98–100
quality of data, metadata, and content for, 90–91
real-time requirement of, 84–85
simple to complex use cases of, 83–86, 86f
subclasses for, 85–86, 86f
NCW and, 82
practical security solutions for, 221–224, 222f, 224f
predictive analytics and, 238–239
predictive modeling and, 10
processing stages administered to, 267–271, 272f
public safety concerns with, 51
public trust challenged with, 36
risk analysis with, 9
SCAF and application of, 77–78
for building trust and common purpose, 77f, 78
for cohesion and coherence and interoperability, 78, 78f
for shared safety, security, and resilience plan, 78, 79f
solutions overview of, 11–12
sources of, 4–5
technology challenges and, 15–18, 17f
trust in, 150
unstructured data and, 174–175
Big Data analysis, 142–144
A/B testing for, 142
association rule learning for, 143
classification for, 143
crowdsourcing for, 143
data mining for, 143
insider threats and, 64–65
natural language processing for, 143–144
predictive uses of, 56
sentiment analysis for, 144
signal processing for, 144
text analysis for, 143–144
visualization for, 144
Big Data analytics, 36
See also High-performance analytics
advancement after 2001 of, 58–59
analysis compared to synthesis for, 151–152
attractions of, 229
capabilities of, 9–10
categories of, 58
computational tools for, 144–145
for CONTEST, 35
cultural dependence in
demand side of, 252–256, 253f
supply side of, 252–256, 253f
cultural influence in, 250–251, 258–259
context mismatches and, 256–257, 256f
cross-cultural psychology and, 251–252, 257–258
cultural intelligence integration with, 257–258
ethics and, 58
ETL and, 134–135
policy concerns with, 229–230
social media challenges for, 40–41
sources for, 150–151
cloudsourcing and crowdsourcing, 150
corporate systems, 150–151
validity of, 250, 256, 258
Big Data architectures
capacity planning considerations for, 137
cloud computing considerations for, 137
data stack requirements for, 137, 138f
deployment for, 131–132, 132t
infrastructure requirements for, 134–137
data acquisition, 135
data analysis, 135–136
data organization, 135
network considerations, 136
performance considerations, 136–137
planning, 134–137
scalability of, 131–132
technological underpinning for, 132–134
DW and data mart for, 134
HBase for, 134
HDFS and, 133
MapReduce frameworks for, 132–133, 133f
Big Data for law enforcement case study and workshop
alerting and prediction in, 44–45
feedback for, 51
history used for, 43–44
overview of, 41–43
situation awareness and, 43–45
social media for public interactions and, 44
tools and prototypes during, 46–51
content-based image retrieval, 47–48
detecting anomalies, 49, 49f
dynamic classification of Twitter content, 46–47
geographic information maximization, 48
influence and reach of messaging, 49–50
technology integration, 50–51
word cloud visualization, 46, 47f
Twitter analysis for, 45–46
BigQuery, See Google BigQuery
bin Laden, Osama, 24
Binary information fragments (BIFs), 214–215, 215f, 217
Bing Liu, 61
Biological select agents or toxins (BSATs), 55–56, 59–60
Biomedical literature, 184–185
Blair, Tony, 27–28, 30
Bogachev, Evgeniy, 121
Bolts, 97–100
Boston Marathon bombings, 40, 269–270
Boston Police Department, Twitter used by, 40
Botella, Luis, 219
Bush, George W., 57–58
Business intelligence (BI), 108, 132
C
Calce, Michael, 113–114, 121–122
Calibration, 264, 272
Cameron, David, 33–34
Carlile, Alex, 31
Cascalog, 96
Center for the Elaboration of Data (CED), 244
Change champions, 149–150
Chief information officers (CIOs), 149, 152
Clarke, Charles, 30
Classification, 143
Climate change, CI threats from, 69
Cloud-based databases, 142
Clouds
for Big Data, 11–12
Big Data architecture considerations for, 137
Cloudsourcing, 150
Collaborative phishing, 157
Commercial motivated cyberattacks, 120
Commodity hardware and software, 140–141
Common operational picture (COP), 81–82
Public Order Policing Model and, 265–267, 266f–267f
Communities
See also Strategic community architecture framework, Strategic community requirement
as ecosystem, 70
fluidity of modern, 70
security of, 70–71
Community data, 58
Concept extraction, 162, 162f
Conceptual equivalence, 255
Constructivist learning, AIS and, 218–221
Content uncertainty, 175
Content-based image retrieval, 47–48
Context mismatches, culture and, 256–257, 256f
Co-reference resolution, 161, 177
Corollary discharge cycle, 272
Corporate systems data, 150–151
Correlation uncertainty, 175
Counterterrorism
See also Operation CREVICE, Terrorism
with Big Data, 34–36
collaboration for, 23–24
Internet challenges for, 34–35
with Operation STEPFORD, 30
OSINT used for, 35–36
public scrutiny of, 31
threats assessed by, 27–29
UK’s strategic approach for, 32–33, 36
vulnerabilities exposed with, 27
CouNter-TErrorism STrategy (CONTEST)
Big Data analytics for, 35
4 P’s of, 32–33
security and law enforcement uses of, 32, 35
Court of Justice of European Union (ECJ), 232
Credit Crunch, 2008-2009, 151
Crime
BN extraction from social media for detecting, 167–169, 168f
example of, 170, 169
general architecture for, 167–169, 170f
social networks mining for, 157–158
violent, 39
alerting and prediction of, 44–45
Crisis management, social media for, 40–41, 51
Crisis response dashboard, social media, 50–51, 50f
Critical infrastructure (CI), 68–69
See also Strategic community architecture framework, Strategic community requirement
architecture supporting, 73–75
Big Data and strategic landscape of, 69–73
climate change threats and, 69
complexity of, 80
local, 72
national, 71–72
natural disasters and, 71
SCR and, 72
security of, 70–71
as system of systems, 68–69
Cross-cultural psychology, Big Data analytics and, 251–252, 257–258
Crowdsourcing, 143, 150
Cultural dependence, in Big Data analytics
demand side of, 252–256, 253f
supply side of, 252–256, 253f
Cultural equivalence, 253, 259
Cultural intelligence, 251, 257–258
Culture
Big Data analytics and, 250–251, 258–259
context mismatches and, 256–257, 256f
cross-cultural psychology and, 251–252, 257–258
misunderstandings of, 256–257, 256f
Cyberattacks, 108–110
motivations taxonomies for, 117–122, 119f
commercial, 120
emotional, 120–121
exploitation, 122
financial, 121
ideological, 120
informational/promotional, 121
personal, 121–122
political, 118
national security issues with, 117–118
organizers of, 113–115
tools used to facilitate, 115–116
types of, 111–113
Cybercrime, 108–110
attack classification and parameters of, 111–113
costs of, 110–111
defining, 110–111
motivations for, 117–118
OSINT detecting, 122–123
text analytics detecting, 122–123
national security threat of, 111–113
with social networks, 157
Cyberespionage, 112–113
Cyberterrorism, 108–110
defining, 110–111
evolution of, 112
national security threats from, 120
OSINT detecting motivations for, 122–123
potential impact of, 112
Cyberwarfare
motivations, 108
rise of, 112
D
DAG job, 95, 97
Data acquisition, 131–132, 132t, 135
Data analysis, 131–132, 132t, 135–136
Data collection equivalence, 254
Data exhaust, 58
Data mart, 134
Data mining, 56
See also Natural language processing
for Big Data analysis, 143
in social networks for crime, 157–158
Data of self-quantification, 58
Data organization, 131–132, 132t, 135
Data processing, See Processing
Data protection
EU laws for, 239–242
EU legal framework for
Data Protection Directive 95/46/EC, 239–241
Data Retention Directive 2006/24/EC, 241–242
Italian legal framework for, 242–245
Authority for Personal Data Protection, 242
Privacy Code, 242–243
requirements and privacy for, 152–153, 232, 236–237
sensitive, 238, 243
Data Protection Act (DPA), 152
Data Protection Directive 95/46/EC, 239–241
Data Retention Directive 2006/24/EC, 241–242
Data sets, 3, 12
Data stack architecture, 137, 138f
Data streaming
analytics, 10–11
for business use, 10
geospatial, 10–11
voice and video, 10
real-time, 8
Data to information, knowledge, and wisdom (DIKW), 4
Data validity, 250, 256, 258
Data visualization, See Visualization
Data warehouses (DW), 4, 134
Databases, 178
cloud-based, 142
distributed, 141
knowledge, 167, 170f
NoSQL and SQL, 135
Databricks, 97
Data-mining grids, 141–142
Decision making, autonomous, 209–210
Deep analytics subclass, 85, 86f
Demand side, of Big Data analytics, 252–256, 253f
Department of Homeland Security, 19
Department of Information and Security (DIS), 247
Department of Justice (DOJ), 55, 57, 59
Deployment, for big Data architectures, 131–132, 132t
Digitalized world, 89–91
Dilemmas, of Big Data for law enforcement, 230, 236–237
Dimensional approach, to DW, 134
Direct attached storage (DAS), 133
Discrete obfuscation (DO), 221
Distributed databases, 141
Distributed denial of service (DDoS), 113–114
Distributed file systems, 142
Document classification, 177
DrinkOrDie, 114
Drucker, Johanna, 64
DStream, 98, 100
Dynamic classification of Twitter content, 46–47
Dynamic data-driven dictionary (DDD), 187, 198–200
Dynamic nature, of Big Data, 3, 6
E
Early Pursuit Against Organized Crime Using Environmental Scanning the Law and Intelligence Systems project (ePOOLICE), 109
EBIMED, 203
Ecosystem, community as, 70
Emotional motivated cyberattacks, 120–121
e-Privacy Directive 2002/58/EC, 239–240
European Union (EU)
Big Data for law enforcement issues in
human rights and, 231–233
legal framework for, 230–236
public trust and confidence with, 234–236
purpose limitation and, 233–234
Court of Justice of, 232
data protection laws of, 239–242
data protection legal framework of
Data Protection Directive 95/46/EC, 239–241
Data Retention Directive 2006/24/EC, 241–242
European Union Data Protection Working Party, 229
Evans, Jonathan, 28, 31
Event extraction, 177
Events, world as, 89–91
Evidential uncertainty, 175
Expert Behavioral Analysis Panel (EBAP), 56, 59
Exploitation motivated cyberattacks, 122
Extraction, data, 132, 132t, 137
Extract-transform-load (ETL), 134–135
Extremism, violent
challenges confronting, 32
on Internet, 23–24, 34
of IS, 33–34
F
Facebook, 155
Fault tolerance, 103
Federal Bureau of Investigation (FBI), 55, 57
Law Enforcement Bulletin, 272
Federal Emergency Management Agency (FEMA), 41
Feedback loops, 265–267, 266f–267f
Fertilizer terrorist plot
assessing, 27–29
development of, 24–25
executive action against, 26
international element of, 25–26
vulnerabilities exposed with, 27
Filtering, of Big Data for military, 88, 98–100
Financial motivated cyberattacks, 121
Flexibility, for real-time Big Data systems, 104
Formal concept analysis (FCA), 162, 162f, 166
Four P’s, 32–33
Frost, Mitchell, 120
Fukushima disaster, 71
Functional equivalence, 255
Fusion
MSP and, 19
SAS for, 18, 18f
G
Garcia, Anthony, 25–27
Gartner hype cycle, 146
GentleBoost, 48
Geocoding, 48, 51–52
Geographic information, maximizing, 48
Geospatial analytics, 10–11
Global analytics vision subclass, 85, 86f
Global positioning systems (GPS), 8, 173
Google BigQuery, 11
Google Earth, heat map on, 49–50
Governance and compliance, information, 152–153
Gulf War syndrome, 62
H
Hackers, 114, 116
Hactivism, 111, 113
Hadoop, 16, 95
velocity and volume addressed with, 145
Hadoop distributed file system (HDFS), 95
for Big Data architectures, 133
capacity planning and, 137
interconnections of nodes in, 136
Hallett, Heather, 30–31
Harm and well-being spectrum, 73–74, 75f
Hash Table (HT), 200
HashMap, 99
Hashtags, Twitter and, 45, 49
Hatcher, Alan, 26
HBase, 134
Health care, Big Data and, 9
Heat map, on Google Earth, 49–50
Hexamethonium, 201–202
High-performance analytics (HPA)
See also Big Data analytics
age of, 14–15
benefits of, 16
example scenarios using, 19
flexibility of, 18
intelligence from, 16–18, 18f
technology challenges and, 15–18, 17f
visualization with, 17f
Historical data, 174
Honan, Mat, 116
Hotz, George, 114
Howard County Police Department (HCPD), 41
HT, See Hash Table
Human rights, Big Data for law enforcement in EU and, 231–233
Human trafficking model, 109–110, 110f
Human-Computer Interaction (HCI), 262–263, 262f–263f
Humanistic inquiry, 65
Huntley, Ian, 235
Hussain, Hasib, 29–30
Hussain, Nabeel, 25–27
Hussein, Sadam, 57
Hype cycle, Gartner, 146
Hypothesis generation, 184
I
IBM Infosphere BigInsights, 11
Ideological motivated cyberattacks, 120
Image sharing, on Twitter, 46
ImageNet, 48
Implementation, 145–146
of ARIANA for biomedical applications, 193–200
automatic heading selection, 196–198, 196t
data stratification and POLSA, 195–198, 196t
MGD creation, 195
OM and MGD creation, 194–195
POLSA encoding fine-tuning, 198
reverse OM and I&V, 200
issues for, 146
new technology introduction and expectation, 146, 147t–148t
IT project reference class and, 148
project initiation and launch, 146–150
project success mitigating factors for, 149, 149t
project success user factors for, 149–150, 149t
Implicit biographical memory recall, 217
In-database analytics techniques, 137
Information governance and compliance, 152–153
Information retrieval, 163, 185
Information sharing, 42, 44–46
Information technology (IT), 17
initiation and launch of, 146–150
introduction and expectations for, 146, 147t–148t
project reference class for, 148
Informational/promotional motivated cyberattacks, 121
In-memory computing, 137
Insider threats
See also Amerithrax case
Big Data analysis and, 64–65
data collection needs and, 64–65
ethics and, 56
safeguards against, 55–56
Integration, of data, 132, 132t, 134–135
Intelligence
See also Open source intelligence
for battlefield success, 81
from HPA, 16–18, 18f
sense making and, 176–177
on terrorism, 23–25, 27–28, 31, 34
Interface and visualization (I&V), 192–193, 193f, 200
Internet
challenges created by, 155
counterterrorism challenges with, 34–35
radicalization and, 33–34
security concerns with, 33–34
terrorism threat landscape and, 33–34
violent extremism on, 34
Internet Encyclopedia of Personal Construct Psychology, 218–219
Internet of Things (IOT), 4, 6–7
Interpretation, Big Data, 250, 254–257
Investigation Systems, 244
Investigations, 241, 244
Iraq War, 57
Islamic State (IS), 33–34
Italy
data protection legal framework in, 242–245
Authority for Personal Data Protection, 242
Privacy Code, 242–243
police forces in, 243–244
opportunities and constraints for, 245–247
processing and privacy with, 244–245
Ivins, Bruce E., 55–64
J
Japan, 71
JavaScript Object Notation (JSON), 192–193, 193f, 200
Jenkin, Bernard, 235
Johns Hopkins Applied Physics Laboratory (JHU/APL), 41–42
Joint Terrorism Analysis Centre (JTAC), 27–28
Jurisdiction, 231–234
K
Khan, Mohammed Siddique, 29–31
Khawaja, Mohammed Momin, 25–27
Khyam, Omar, 25–27
fertilizer plot of, 24–25
training camp of, 24–33
Knowledge database (KDB), 167, 170f
Knowledge discovery (KD), 184, 187, 201–202
Knowledge relativity threads (KRT), 212
multi-step process of, 222, 222f
for sentiment analysis, 223–224, 224f
Korzybski, Alfred, 219
L
Lamberth, Royce C., 59
Latent semantic analysis (LSA), 185
Law enforcement, 23–24, 31, 34
See also Big Data for law enforcement case study and workshop
Big Data for, 39–41, 108
dilemmas of, 230, 236–237
human rights in EU and, 231–233
legal framework in EU for, 230–236
potential of, 51–52
public trust and confidence in EU with, 234–236
purpose limitation in EU and, 233–234
sources for, 39–40
UK integration of, 109
budget cuts and, 109
CONTEST uses for, 32, 35
history used for, 43–44
Italian police forces and, 243–244
opportunities and constraints for, 245–247
processing and privacy with, 244–245
military working with, 83
situation awareness and, 43–45
social media for public interactions with, 44
suicide terrorism challenges for, 33
Law enforcement agencies (LEAs), 108, 230
Legislation
in EU for data protection
Data Protection Directive 95/46/EC, 239–241
Data Retention Directive 2006/24/EC, 241–242
in EU for law enforcement and Big Data, 230–236
in Italy for data protection, 242–245
Authority for Personal Data Protection, 242
Privacy Code, 242–243
Lethal drug interaction, KD case study, 201–202
Lexical analysis, 159–160, 160f
Lindsay, Jermaine, 29–30
LinkedIn, 155
Literature mining, 184–185, 204–205
Long-term memories (LTMs), 211–212, 213f, 216–217, 216f
M
Machine translation, 163
Mahmood, Shujah, 24, 26–27
Mahmood, Waheed, 24, 27
Malware, 115
Manning, Bradley, 121
Manning, Chelsea, 121
MapReduce, 84, 95–96, 145
Apache Hive framework for, 96
Apache Pig framework for, 96
capacity planning and, 137
Cascalog framework for, 96
frameworks, 132–133, 133f
interconnections of nodes in, 136
Tez for, 96
Marz, Nathan, 88–89
Massively parallel processing (MPP), 132, 141
Matrix metalloproteinase (MMP), 202
Measure equivalence, 254
Medical Subject Headings (MeSH), 186, 194–195, 198
MEDIE, 203
Memcached, 103
Metadata, quality of, 90–91
Michigan Intelligence Operations Center (MIOC), 19
Michigan State Police (MSP), 19
Military, 81–82
See also Real-time Big Data systems
actionable information and, 81
Big Data and, 82–83
canonic use cases for, 87–89, 87t
correlation over space and time for, 88–89
digitalized world and, 89–91
filtering for, 88, 98–100
quality of data, metadata, and content for, 90–91
real-time requirement of, 84–85
simple to complex use cases of, 83–86, 86f
subclasses for, 85–86, 86f
BML for, 180–182, 181f
law enforcement working with, 83
NCW and, 82
situation awareness in, 176
Ministry of Sound nightclub terror plot, See Fertilizer terrorist plot
Model uncertainty, 175
Moore’s law, 6
MQ-9 Reaper, 82–84
Multi-gram dictionary (MGD), 186, 194–195
Muthana, Nasser, 34
N
Named entity recognition, 161, 177
National security
See also Counterterrorism, Security
cyberattacks issues for, 117–118
cybercrime threat to, 111–113
cyberterrorism threats for, 120
financial transactions and, 8
SAS solution for Middle East intelligence and, 19
sentiment analysis and, 122–123
terrorism threats and, 14
National Security Agency (NSA), 247
Natural disasters, CI and, 71
Natural language data
historical, 174
for situation awareness, 176
structuring, 178
unstructured, 173–174
Natural language processing (NLP), 158, 177
See also Bayesian networks
for Big Data analysis, 143–144
BML and, 180–182, 181f
general architecture and components of, 159–164, 160f, 177
concept extraction, 162, 162f
co-reference resolution, 161, 177
document classification, 177
event extraction, 177
lexical analysis, 159–160, 160f
machine translation, 163
named entity recognition, 161, 177
parsing, 160–161, 161f
pattern recognition, 177
POS tagging, 160
relation extraction, 161, 177
semantic analysis, 163
sentiment analysis, 163, 177
topic recognition, 163
methods for, 158–159
connectionist approach, 159
statistical approach, 159
symbolic approach, 158
weaknesses of, 179–180
Network centric warfare (NCW), 82
Next-Generation 911 system, 48
Normalized approach, to DW, 134
North Atlantic Treaty Organization (NATO), 179
NoSQL databases, 135
Nuclear industry, security and, 71
O
“On the fly” processing, 84–85, 90
Online analytical processing (OLAP), 134
Online clustering, into “streaming layer”, 100
Online Mendelian Inheritance in Man (OMIM), 188
Online transaction processing (OLTP), 137
Ontologies, 178
Ontology mapping (OM), 187–188
See also Adaptive Robust Integrative Analysis for Finding Novel Associations
biomedical application steps for, 189, 190f
MGD creation and, 194–195
objective of, 189
reverse, 192, 200
Open source intelligence (OSINT), 90
counterterrorism using, 35–36
cybercrime and cyberterrorism motivation detection with, 122–123
Operation CREVICE, 24
executive action phase of, 26
international dimension of, 25–26
success and vulnerabilities of, 27
threat assessed by, 27–29
Operation STEPFORD, 30
Operations management, 9
Organization, data, 131–132, 132t, 135
O’Tate, Anne, 203
P
Parameter optimized latent semantic analysis (POLSA), 186
data stratification and, 189–190, 191f, 195–198, 196t
fine-tuning encoding for, 198
Parsing, 160–161, 161f
Parson, Jeffrey Lee, 113–114
PATRIOT Act, See USA PATRIOT Act
Pattern analysis, for Amerithrax case, 63–64
Pattern recognition, 177
Pearson, Edward, 116
Personal Construct Psychology, Constructivism, and Postmodern Thought (Botella), 219
Personal motivated cyberattacks, 121–122
Phishing attacks, 116, 157
Phone phreakers, 115
Pictorial information fragments (PIFs), 217, 218f
Pirates, 114
Planning
fallacy, 146
importance of, 72
for risks, 76
shared safety, security, and resilience, 78, 79f
Political motivated cyberattacks, 118
POS tagging, 160
Powell, Colin, 57
Practical Big Data security solutions, 221–224, 222f, 224f
Pre-attentive processing, 263–264, 263f, 268–269
Predictive analytics, Big Data and, 238–239
Predictive modeling, Big Data and, 10
Predictive policing, 246–247
Prepare strategy, 32–33
Prevent strategy, 32–33
Privacy
data protection and, 152–153, 232, 236–237
Italian police forces and, 244–245
Privacy Code, Italian, 242–243
Private data, 58
Processing, 3–4, 83
See also Massively parallel processing, Natural language processing
of AIS memory
challenges for, 131–132
Italian police forces and, 244–245
“on the fly”, 84–85, 90
real-time, 4, 6, 84–85
signal, 144
attentive, 263f, 264, 269–270
Big Data and security applications of, 267–271, 272f
pre-attentive, 263–264, 263f, 268–269
reflexive, 263, 263f, 268
trustworthiness and, 8
Project initiation and launch, 146–150
Project success, 148–150, 149t
Protect strategy, 32
Public data, 58
Public Order Policing Model, COP and, 265–267, 266f–267f
Public safety, 39, 51
Public security, 243–244
Public trust, in Big Data for law enforcement in EU, 234–236
“Publishing layer,” real-time Big Data systems, 93, 94f, 98
PubMed, 184, 187–188
Purpose limitation, 233–234
Pursue strategy, 32–33
Q
Al Qaeda, 24, 29–30, 33
Quantum mechanics, 212
R
Radicalization, Internet and, 33–34
Raw data, 3–4, 7
Real-time
data streaming, 8
information subclass, 86, 86f
military and Big Data requirements with, 84–85
processing, 4, 6, 84–85
Real-time Big Data systems, 91–93
analytic tools for, 102–104
fault tolerance and, 103
flexibility and adaptation, 104
security and, 103
application principles and constraints of, 91–93, 92f
“batch layer” of, 93, 94f
batch processing into, 95
machine learning and filtering into, 99–100
processing into, 95–96
battlefield in, 91–93, 92f
building layers for, 101–102
implementing, 95–102
logical view of, 93, 94f
“publishing layer” of, 93, 94f
alerts and notifications into, 98
results publication for, 100–101
“streaming layer” of, 93, 94f
data stream processing into, 97–98
filtering processing into, 98–99
machine learning and filtering into, 99–100
online clustering into, 100
Reflexive processing, 263, 263f, 267–270
Relation extraction, 161, 177
Relation resolution, 161
Relevance model (RM), 187, 190–192, 192f, 198–200
Resilient distributed dataset (RDD), 96–98
Reverse OM, 192, 200
Risk, 8
Big Data analysis of, 9
management, 9
planning for, 76
Robotics, autonomous, 209–210
Roche, Ellen, 201
Rowling, J. K., 61
S
Sample equivalence, 253
Schrier, Tyler, 122
Script kiddies, 113–114
Security, 23
See also Counterterrorism, National security
assessing, 27–29
Big Data practical solutions for, 221–224, 222f, 224f
of CI and communities, 70–71
CONTEST uses for, 32, 35
Internet concerns for, 33–34
liberty sacrifices for, 36–37
nuclear industry and, 71
processing stages administered to, 267–271, 272f
public, 243–244
real-time Big Data systems and, 103
SCAF and Big Data for shared, 78, 79f
Security Service (MI5), 25–28, 30
See also Operation CREVICE, Operation STEPFORD
public scrutiny of, 31
7/7 bombings accountability and lessons for, 30–32
Self-calibration, 272
Semantic analysis, 163
Semi-structured data, 5
Sense making
intelligence and, 176–177
situation awareness and, 176–177
uncertainty in, 175
Sensitive data protection, 238, 243
Sensory memories, in AIS, 210, 211f
Sentiment analysis
for Amerithrax case, 61–62
for Big Data analysis, 144
KRTs for, 223–224, 224f
national security and, 122–123
for NLP, 163, 177
social media and, 122–123
September 11th attacks, 23, 25–26, 32, 57
7/7 bombings, 29–32
Short-term memories (STMs)
in AIS, 211, 212f
processing and encoding, 212–216, 214f–215f
attention loop of, 212–215, 214f
Signal processing, 144
Singular Value Decomposition (SVD), 185
Situation awareness, 40, 47–49
See also Big Data for law enforcement case study and workshop
law enforcement and, 43–45
in military, 176
natural language data for, 176
sense making and, 176–177
Smart, Gary, 26
SMSHy Information Content Management, 221
Snowden, Edward, 57–58, 121, 152, 236
Social engineering, 116
Social media, 6, 110
See also Twitter
Big Data analytics challenges with, 40–41
content-based image retrieval with, 47–48
crime detection with BN extraction from, 167–169, 168f
example of, 170, 169
general architecture for, 167–169, 170f
for crisis management, 40–41, 51
crisis response dashboard, 50–51, 50f
detecting anomalies with, 49, 49f
geographic information maximization with, 48
influence and reach of messaging on, 49–50
interpreting, 40
law enforcement public interactions with, 44
sentiment analysis and, 122–123
as source, 40
word cloud visualization for active shooter event and, 46, 47f
Social networks
See also Twitter
change and adaptation in, 156
cybercrime with, 157
mining for crime with, 157–158
mutual relationships within, 155, 156f
Sociopolitical-economic systems, optimization of, 223–224, 224f
Source uncertainty, 175
Sources
of Big Data, 4–5
for law enforcement, 39–40
for Big Data analytics, 150–151
cloudsourcing and crowdsourcing, 150
corporate systems, 150–151
quality and imperfection of, 90–91
searching, 16
semi-structured data, 5
social media for, 40
structured data, 5
Twitter as, 45
unstructured data, 5
Spark, 103
internal components of, 97
RDD structure and, 96–98
stack, 97
Spark Streaming, 97–98
Spectral decomposition mapping, 217
Spouts, 97–98
Spyware, 116
SQL databases, 135
Standish Group Chaos report, 145–146, 148, 149t
Statistical analysis software (SAS)
for fusion centers, 18, 18f
MSP and, 19
national security Middle East intelligence with, 19
Storm, 99–100, 103
for complex event processing, 97–98
Trident over, 98
Strategic community architecture framework (SCAF), 74–75
Big Data application with, 77–78
for building trust and common purpose, 77f, 78
for cohesion and coherence and interoperability, 78, 78f
for shared safety, security, and resilience plan, 78, 79f
Strategic community requirement (SCR), 73–75
architecture framework for, 74–75, 77–78, 77f–79f
CI and, 72
combined effect for, 74–75, 75f
“protection from harm and promotion of well-being” for, 73–74, 75f
underpinning elements of, 76, 76f
Streaming, See Data streaming
“Streaming layer,” real-time Big Data systems, 93, 94f
data stream processing into, 97–98
filtering processing into, 98–99
machine learning and filtering into, 99–100
online clustering into, 100
Structured data, 5
Stylometric analysis, for Amerithrax case, 61–62
Suicide terrorism, 29–30, 33
Sunk-cost fallacy, 146
Superscalar datacenter, on battlefield, 104, 105f
Supply side, of Big Data analytics, 252–256, 253f
Surveillance, USA PATRIOT Act expanding, 58
Synthesis, for analytics, 151–152
T
Tanweer, Shehzad, 29–30
TB, See Tuberculosis
Techniques, for 3’Vs data challenges, 141–142
Technology, introduction and expectations of, 146, 147t–148t
Term-Document Matrix, 185
Terrorism
See also Counterterrorism, Cyberterrorism, Fertilizer terrorist plot, Operation CREVICE
assessing threats of, 27–29
Boston Marathon bombings, 40
evolution of, 112
intelligence on, 23–25, 27–28, 31, 34
Internet changing landscape of, 33–34
national security and, 14
September 11th attacks, 23
7/7 bombings coordination of, 29–30
suicide, 29–30, 33
training camps for, 24–33
UK and, 23–24, 37
Text analytics, 16–17, 143–144
cybercrime motivation detection with, 122–123
Text mining, 158
Tez, 96
3’Vs data, 82–83, 89, 104, 140–141
See also Variety, Velocity, Volume
techniques for challenges of, 141–142
Topic recognition, 163
Translation equivalence, 255–256
Trident, 98
Triple stores, 178
Trojans, 116
Trust, in Big Data, 150
Tuberculosis (TB), 202
Tufte, Edward, 64
Twitter, 40, 155
See also Social media, Social networks
API of, 45–46
Big Data for law enforcement case study and workshop analysis of, 45–46
Boston Police Department using, 40
detecting anomalies with, 49, 49f
dynamic classification of, 46–47
geographic information maximization with, 48
hashtags used with, 45, 49
image sharing on, 46
influence and reach of messaging on, 49–50
“retweeting” on, 45, 49–50
scale challenges with, 43
as source, 45
word cloud visualization and, 46, 47f
U
Uncertainty, in sense making, 175
United Kingdom (UK)
See also Security Service
counterterrorism strategic approach of, 32–33, 36
law enforcement integrating Big Data in, 109
terrorism and, 23–24, 37
Unmanned aerial systems (UAS), 82
Unstructured data
See also Natural language data
Big Data and, 174–175
challenges of, 173
sources, 5
USA PATRIOT Act, 57–58
V
Vaihinger, Hans, 219
Validity, of Big Data analytics, 250, 256, 258
Value, 7, 151
Value management, 9
Variety, 6–7, 140–142
See also 3’Vs data
Velocity, 6, 39
See also 3’Vs data
challenges of, 140
management techniques for, 141–142
Hadoop addressing, 145
Veracity, 7, 151
Violence
See also Terrorism
crime and, 39
alerting and prediction of, 44–45
extremism and, 23–24
challenges confronting, 32
on Internet, 34
of IS, 33–34
Viruses, 115
Visualization, 9
for Amerithrax case and behavioral analysis, 63–64
for Big Data analysis, 144
with HPA, 17f
word cloud, 46, 47f
Voice and video analytics, 10
Volume, 6
See also 3’Vs data
challenges of, 140
representation and storage techniques for, 141–142
Hadoop addressing, 145
W
Web defacers, 114
Web services (WS), 186–187
Weigman, Matthew, 115
Weiser, Mark, 261
Well-being and harm spectrum, 73–74, 75f
Word cloud visualization, 46, 47f
Worms, 115
WS, See Web services
X
XplorMed, 203
Y
YARN, 95
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