Duration
35 hours (usually 5 days including breaks)
Requirements
- Knowledge of law enforcement processes and data systems
- Basic understanding of SQL/Oracle or relational database
- Basic understanding of statistics (at Spreadsheet level)
Overview
Advances in technologies and the increasing amount of information are transforming how law enforcement is conducted. The challenges that Big Data pose are nearly as daunting as Big Data’s promise. Storing data efficiently is one of these challenges; effectively analyzing it is another.
In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.
By the end of this training, participants will be able to:
- Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation
- Implement industrial big data storage and processing solutions for data analysis
- Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation
Audience
- Law Enforcement specialists with a technical background
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Course Outline
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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
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Day 03
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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
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Day 04
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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
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Day 05
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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
