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• Day 01
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  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
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  Day 02
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  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