Big Data Business Intelligence for Criminal Intelligence Analysis Training Course

=====
Day 01
=====
Overview of Big Data Business Intelligence for Criminal Intelligence Analysis

• Case Studies from Law Enforcement - Predictive Policing
• Big Data adoption rate in Law Enforcement Agencies and how they are aligning their future operation around Big Data Predictive Analytics
• Emerging technology solutions such as gunshot sensors, surveillance video and social media
• Using Big Data technology to mitigate information overload
• Interfacing Big Data with Legacy data
• Basic understanding of enabling technologies in predictive analytics
• Data Integration & Dashboard visualization
• Fraud management
• Business Rules and Fraud detection
• Threat detection and profiling
• Cost benefit analysis for Big Data implementation

Introduction to Big Data

• Main characteristics of Big Data -- Volume, Variety, Velocity and Veracity.
• MPP (Massively Parallel Processing) architecture
• Data Warehouses – static schema, slowly evolving dataset
• MPP Databases: Greenplum, Exadata, Teradata, Netezza, Vertica etc.
• Hadoop Based Solutions – no conditions on structure of dataset.
• Typical pattern : HDFS, MapReduce (crunch), retrieve from HDFS
• Apache Spark for stream processing
• Batch- suited for analytical/non-interactive
• Volume : CEP streaming data
• Typical choices – CEP products (e.g. Infostreams, Apama, MarkLogic etc)
• Less production ready – Storm/S4
• NoSQL Databases – (columnar and key-value): Best suited as analytical adjunct to data warehouse/database

NoSQL solutions

• KV Store - Keyspace, Flare, SchemaFree, RAMCloud, Oracle NoSQL Database (OnDB)
• KV Store - Dynamo, Voldemort, Dynomite, SubRecord, Mo8onDb, DovetailDB
• KV Store (Hierarchical) - GT.m, Cache
• KV Store (Ordered) - TokyoTyrant, Lightcloud, NMDB, Luxio, MemcacheDB, Actord
• KV Cache - Memcached, Repcached, Coherence, Infinispan, EXtremeScale, JBossCache, Velocity, Terracoqua
• Tuple Store - Gigaspaces, Coord, Apache River
• Object Database - ZopeDB, DB40, Shoal
• Document Store - CouchDB, Cloudant, Couchbase, MongoDB, Jackrabbit, XML-Databases, ThruDB, CloudKit, Prsevere, Riak-Basho, Scalaris
• Wide Columnar Store - BigTable, HBase, Apache Cassandra, Hypertable, KAI, OpenNeptune, Qbase, KDI

Varieties of Data: Introduction to Data Cleaning issues in Big Data

• RDBMS – static structure/schema, does not promote agile, exploratory environment.
• NoSQL – semi structured, enough structure to store data without exact schema before storing data
• Data cleaning issues

Hadoop

• When to select Hadoop?
• STRUCTURED - Enterprise data warehouses/databases can store massive data (at a cost) but impose structure (not good for active exploration)
• SEMI STRUCTURED data – difficult to carry out using traditional solutions (DW/DB)
• Warehousing data = HUGE effort and static even after implementation
• For variety & volume of data, crunched on commodity hardware – HADOOP
• Commodity H/W needed to create a Hadoop Cluster

Introduction to Map Reduce /HDFS

• MapReduce – distribute computing over multiple servers
• HDFS – make data available locally for the computing process (with redundancy)
• Data – can be unstructured/schema-less (unlike RDBMS)
• Developer responsibility to make sense of data
• Programming MapReduce = working with Java (pros/cons), manually loading data into HDFS

=====
Day 02
=====
Big Data Ecosystem -- Building Big Data ETL (Extract, Transform, Load) -- Which Big Data Tools to use and when?

• Hadoop vs. Other NoSQL solutions
• For interactive, random access to data
• Hbase (column oriented database) on top of Hadoop
• Random access to data but restrictions imposed (max 1 PB)
• Not good for ad-hoc analytics, good for logging, counting, time-series
• Sqoop - Import from databases to Hive or HDFS (JDBC/ODBC access)
• Flume – Stream data (e.g. log data) into HDFS

Big Data Management System

• Moving parts, compute nodes start/fail :ZooKeeper - For configuration/coordination/naming services
• Complex pipeline/workflow: Oozie – manage workflow, dependencies, daisy chain
• Deploy, configure, cluster management, upgrade etc (sys admin) :Ambari
• In Cloud : Whirr

Predictive Analytics -- Fundamental Techniques and Machine Learning based Business Intelligence

• Introduction to Machine Learning
• Learning classification techniques
• Bayesian Prediction -- preparing a training file
• Support Vector Machine
• KNN p-Tree Algebra & vertical mining
• Neural Networks
• Big Data large variable problem -- Random forest (RF)
• Big Data Automation problem – Multi-model ensemble RF
• Automation through Soft10-M
• Text analytic tool-Treeminer
• Agile learning
• Agent based learning
• Distributed learning
• Introduction to Open source Tools for predictive analytics : R, Python, Rapidminer, Mahut

Predictive Analytics Ecosystem and its application in Criminal Intelligence Analysis

• Technology and the investigative process
• Insight analytic
• Visualization analytics
• Structured predictive analytics
• Unstructured predictive analytics
• Threat/fraudstar/vendor profiling
• Recommendation Engine
• Pattern detection
• Rule/Scenario discovery – failure, fraud, optimization
• Root cause discovery
• Sentiment analysis
• CRM analytics
• Network analytics
• Text analytics for obtaining insights from transcripts, witness statements, internet chatter, etc.
• Technology assisted review
• Fraud analytics
• Real Time Analytic

=====
Day 03
=====
Real Time and Scalable Analytics Over Hadoop

• Why common analytic algorithms fail in Hadoop/HDFS
• Apache Hama- for Bulk Synchronous distributed computing
• Apache SPARK- for cluster computing and real time analytic
• CMU Graphics Lab2- Graph based asynchronous approach to distributed computing
• KNN p -- Algebra based approach from Treeminer for reduced hardware cost of operation

Tools for eDiscovery and Forensics

• eDiscovery over Big Data vs. Legacy data – a comparison of cost and performance
• Predictive coding and Technology Assisted Review (TAR)
• Live demo of vMiner for understanding how TAR enables faster discovery
• Faster indexing through HDFS – Velocity of data
• NLP (Natural Language processing) – open source products and techniques
• eDiscovery in foreign languages -- technology for foreign language processing

Big Data BI for Cyber Security – Getting a 360-degree view, speedy data collection and threat identification

• Understanding the basics of security analytics -- attack surface, security misconfiguration, host defenses
• Network infrastructure / Large datapipe / Response ETL for real time analytic
• Prescriptive vs predictive – Fixed rule based vs auto-discovery of threat rules from Meta data

Gathering disparate data for Criminal Intelligence Analysis

• Using IoT (Internet of Things) as sensors for capturing data
• Using Satellite Imagery for Domestic Surveillance
• Using surveillance and image data for criminal identification
• Other data gathering technologies -- drones, body cameras, GPS tagging systems and thermal imaging technology
• Combining automated data retrieval with data obtained from informants, interrogation, and research
• Forecasting criminal activity

=====
Day 04
=====
Fraud prevention BI from Big Data in Fraud Analytics

• Basic classification of Fraud Analytics -- rules-based vs predictive analytics
• Supervised vs unsupervised Machine learning for Fraud pattern detection
• Business to business fraud, medical claims fraud, insurance fraud, tax evasion and money laundering

Social Media Analytics -- Intelligence gathering and analysis

• How Social Media is used by criminals to organize, recruit and plan
• Big Data ETL API for extracting social media data
• Text, image, meta data and video
• Sentiment analysis from social media feed
• Contextual and non-contextual filtering of social media feed
• Social Media Dashboard to integrate diverse social media
• Automated profiling of social media profile
• Live demo of each analytic will be given through Treeminer Tool

Big Data Analytics in image processing and video feeds

• Image Storage techniques in Big Data -- Storage solution for data exceeding petabytes
• LTFS (Linear Tape File System) and LTO (Linear Tape Open)
• GPFS-LTFS (General Parallel File System -  Linear Tape File System) -- layered storage solution for Big image data
• Fundamentals of image analytics
• Object recognition
• Image segmentation
• Motion tracking
• 3-D image reconstruction

Biometrics, DNA and Next Generation Identification Programs

• Beyond fingerprinting and facial recognition
• Speech recognition, keystroke (analyzing a users typing pattern) and CODIS (combined DNA Index System)
• Beyond DNA matching: using forensic DNA phenotyping to construct a face from DNA samples

Big Data Dashboard for quick accessibility of diverse data and display :

• Integration of existing application platform with Big Data Dashboard
• Big Data management
• Case Study of Big Data Dashboard: Tableau and Pentaho
• Use Big Data app to push location based services in Govt.
• Tracking system and management

=====
Day 05
=====
How to justify Big Data BI implementation within an organization:

• Defining the ROI (Return on Investment) for implementing Big Data
• Case studies for saving Analyst Time in collection and preparation of Data – increasing productivity
• Revenue gain from lower database licensing cost
• Revenue gain from location based services
• Cost savings from fraud prevention
• An integrated spreadsheet approach for calculating approximate expenses vs. Revenue gain/savings from Big Data implementation.

Step by Step procedure for replacing a legacy data system with a Big Data System

• Big Data Migration Roadmap
• What critical information is needed before architecting a Big Data system?
• What are the different ways for calculating Volume, Velocity, Variety and Veracity of data
• How to estimate data growth
• Case studies

Review of Big Data Vendors and review of their products.

• Accenture
• APTEAN (Formerly CDC Software)
• Cisco Systems
• Cloudera
• Dell
• EMC
• GoodData Corporation
• Guavus
• Hitachi Data Systems
• Hortonworks
• HP
• IBM
• Informatica
• Intel
• Jaspersoft
• Microsoft
• MongoDB (Formerly 10Gen)
• MU Sigma
• Netapp
• Opera Solutions
• Oracle
• Pentaho
• Platfora
• Qliktech
• Quantum
• Rackspace
• Revolution Analytics
• Salesforce
• SAP
• SAS Institute
• Sisense
• Software AG/Terracotta
• Soft10 Automation
• Splunk
• Sqrrl
• Supermicro
• Tableau Software
• Teradata
• Think Big Analytics
• Tidemark Systems
• Treeminer
• VMware (Part of EMC)

Q/A session