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Each session is 2 hours

Day-1: Session -1: Business Overview of Why Big Data Business Intelligence in Govt.

• Case Studies from NIH, DoE
• Big Data adaptation rate in Govt. Agencies & and how they are aligning their future operation around Big Data Predictive Analytics
• Broad Scale Application Area in DoD, NSA, IRS, USDA etc.
• Interfacing Big Data with Legacy data
• Basic understanding of enabling technologies in predictive analytics
• Data Integration & Dashboard visualization
• Fraud management
• Business Rule/ Fraud detection generation
• Threat detection and profiling
• Cost benefit analysis for Big Data implementation

Day-1: Session-2 : Introduction of Big Data-1

• Main characteristics of Big Data-volume, variety, velocity and veracity. MPP architecture for volume.
• Data Warehouses – static schema, slowly evolving dataset
• MPP Databases like Greenplum, Exadata, Teradata, Netezza, Vertica etc.
• Hadoop Based Solutions – no conditions on structure of dataset.
• Typical pattern : HDFS, MapReduce (crunch), retrieve from HDFS
• 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

Day-1 : Session -3 : Introduction to Big Data-2

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 issue in Big Data

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

Day-1 : Session-4 : Big Data Introduction-3 : 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 – tough to do with 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-2: Session-1: Big Data Ecosystem-Building Big Data ETL: universe of Big Data Tools-which one 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

Day-2: Session-2: 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

Day-2: Session-3: Predictive analytics in Business Intelligence -1: Fundamental Techniques & Machine learning based BI :

• Introduction to Machine learning
• Learning classification techniques
• Bayesian Prediction-preparing training file
• Support Vector Machine
• KNN p-Tree Algebra & vertical mining
• Neural Network
• 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, Rapidminer, Mahut

Day-2: Session-4 Predictive analytics eco-system-2: Common predictive analytic problems in Govt.

• Insight analytic
• Visualization analytic
• Structured predictive analytic
• Unstructured predictive analytic
• Threat/fraudstar/vendor profiling
• Recommendation Engine
• Pattern detection
• Rule/Scenario discovery –failure, fraud, optimization
• Root cause discovery
• Sentiment analysis
• CRM analytic
• Network analytic
• Text Analytics
• Technology assisted review
• Fraud analytic
• Real Time Analytic

Day-3 : Sesion-1 : Real Time and Scalable Analytic Over Hadoop

• Why common analytic algorithms fail in Hadoop/HDFS
• Apache Hama- for Bulk Synchronous distributed computing
• Apache SPARK- for cluster computing for 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

Day-3: Session-2: 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 a Tar product ( vMiner) to understand how TAR works for faster discovery
• Faster indexing through HDFS –velocity of data
• NLP or Natural Language processing –various techniques and open source products
• eDiscovery in foreign languages-technology for foreign language processing

Day-3 : Session 3: Big Data BI for Cyber Security –Understanding whole 360 degree views of speedy data collection to threat identification

• Understanding 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

Day-3: Session 4: Big Data in USDA : Application in Agriculture

• Introduction to IoT ( Internet of Things) for agriculture-sensor based Big Data and control
• Introduction to Satellite imaging and its application in agriculture
• Integrating sensor and image data for fertility of soil, cultivation recommendation and forecasting
• Agriculture insurance and Big Data
• Crop Loss forecasting

Day-4 : Session-1: Fraud prevention BI from Big Data in Govt-Fraud analytic:

• Basic classification of Fraud analytics- rule based vs predictive analytics
• Supervised vs unsupervised Machine learning for Fraud pattern detection
• Vendor fraud/over charging for projects
• Medicare and Medicaid fraud- fraud detection techniques for claim processing
• Travel reimbursement frauds
• IRS refund frauds
• Case studies and live demo will be given wherever data is available.

Day-4 : Session-2: Social Media Analytic- Intelligence gathering and analysis

• 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.

Day-4 : Session-3: Big Data Analytic in image processing and video feeds

• Image Storage techniques in Big Data- Storage solution for data exceeding petabytes
• LTFS and LTO
• GPFS-LTFS ( Layered storage solution for Big image data)
• Fundamental of image analytics
• Object recognition
• Image segmentation
• Motion tracking
• 3-D image reconstruction

Day-4: Session-4: Big Data applications in NIH:

• Emerging areas of Bio-informatics
• Meta-genomics and Big Data mining issues
• Big Data Predictive analytic for Pharmacogenomics, Metabolomics and Proteomics
• Big Data in downstream Genomics process
• Application of Big data predictive analytics in Public health

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-5 : Session-1: How to justify Big Data BI implementation within an organization:

• Defining ROI for Big Data implementation
• Case studies for saving Analyst Time for collection and preparation of Data –increase in productivity gain
• Case studies of revenue gain from saving the licensed database cost
• Revenue gain from location based services
• Saving from fraud prevention
• An integrated spreadsheet approach to calculate approx. expense vs. Revenue gain/savings from Big Data implementation.

Day-5 : Session-2: Step by Step procedure to replace legacy data system to Big Data System:

• Understanding practical Big Data Migration Roadmap
• What are the important information needed before architecting a Big Data implementation
• What are the different ways of calculating volume, velocity, variety and veracity of data
• How to estimate data growth
• Case studies

Day-5: Session 4: Review of Big Data Vendors and review of their products. Q/A session:

• 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)