Big Data Business Intelligence for Govt. Agencies Training Course

Each session lasts 2 hours

Day-1: Session -1: Business Perspective on Big Data BI in Government

• Case studies from NIH, DoE
• Adoption rates of Big Data in government agencies and strategies for aligning future operations with Big Data Predictive Analytics
• Broad application areas in DoD, NSA, IRS, USDA, etc.
• Integration of Big Data with legacy data systems
• Fundamental understanding of enabling technologies in predictive analytics
• Data Integration & Dashboard visualization
• Fraud management
• Generation of Business Rules & Fraud detection
• Threat detection and profiling
• Cost-benefit analysis for Big Data implementation

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

• Core characteristics of Big Data: volume, variety, velocity, and veracity. MPP architecture for handling volume.
• Data Warehouses – static schema, slowly evolving datasets
• MPP Databases such as Greenplum, Exadata, Teradata, Netezza, Vertica, etc.
• Hadoop Based Solutions – no restrictions on dataset structure.
• Typical pattern: HDFS, MapReduce (crunch), retrieval from HDFS
• Batch processing – suited for analytical/non-interactive tasks
• Volume handling: CEP streaming data
• Common choices – CEP products (e.g. Infostreams, Apama, MarkLogic, etc)
• Less production-ready options – Storm/S4
• NoSQL Databases – (columnar and key-value): Best suited as an analytical adjunct to data warehouses/databases

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

Data Varieties: Introduction to Data Cleaning Challenges in Big Data

• RDBMS – static structure/schema, does not foster an agile, exploratory environment.
• NoSQL – semi-structured, sufficient structure to store data without a predefined exact schema
• Data cleaning issues

Day-1 : Session-4 : Big Data Introduction-3 : Hadoop

• Criteria for selecting Hadoop
• STRUCTURED - Enterprise data warehouses/databases can store massive data (at a cost) but impose structure (limiting active exploration)
• SEMI-STRUCTURED data – challenging to handle with traditional solutions (DW/DB)
• Warehousing data = significant effort and static even after implementation
• For variety & volume of data, processed on commodity hardware – HADOOP
• Commodity Hardware needed to establish a Hadoop Cluster

Introduction to Map Reduce /HDFS

• MapReduce – distributing computing tasks across multiple servers
• HDFS – making data locally available for computing processes (with redundancy)
• Data – can be unstructured/schema-less (unlike RDBMS)
• Developer responsibility to interpret data
• Programming MapReduce = working with Java (pros/cons), manual loading of data into HDFS

Day-2: Session-1: Big Data Ecosystem-Building Big Data ETL: The Universe of Big Data Tools-When to use which?

• Hadoop vs. Other NoSQL solutions
• Requirements for interactive, random access to data
• Hbase (column-oriented database) on top of Hadoop
• Random access to data with restrictions (max 1 PB)
• Suitable for logging, counting, time-series; less ideal for ad-hoc analytics
• Sqoop - Importing data from databases to Hive or HDFS (JDBC/ODBC access)
• Flume – Streaming data (e.g. log data) into HDFS

Day-2: Session-2: Big Data Management System

• Component movement, compute node start/fail :ZooKeeper - For configuration/coordination/naming services
• Complex pipeline/workflow: Oozie – managing workflows, dependencies, daisy chains
• Deployment, configuration, cluster management, upgrades, etc. (sys admin) :Ambari
• Cloud-based solutions : 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 files
• 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, Rapidminer, Mahut

Day-2: Session-4 Predictive Analytics Ecosystem-2: Common Predictive Analytic Problems in Government

• Insight analytics
• Visualization analytics
• Structured predictive analytics
• Unstructured predictive analytics
• Threat/fraud star/vendor profiling
• Recommendation Engines
• Pattern detection
• Rule/Scenario discovery –failure, fraud, optimization
• Root cause discovery
• Sentiment analysis
• CRM analytics
• Network analytics
• Text Analytics
• Technology Assisted Review
• Fraud analytics
• Real-Time Analytics

Day-3 : Sesion-1 : 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 in real-time analytics
• CMU Graphics Lab2- Graph-based asynchronous approach to distributed computing
• KNN p-Algebra-based approach from Treeminer for reduced hardware operational costs

Day-3: Session-2: Tools for eDiscovery and Forensics

• eDiscovery over Big Data vs. Legacy data – comparison of cost and performance
• Predictive coding and technology-assisted review (TAR)
• Live demo of a TAR product (vMiner) to illustrate how TAR accelerates 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 – Comprehensive 360-Degree View from Data Collection to Threat Identification

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

Day-3: Session 4: Big Data in USDA : Applications 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 soil fertility, cultivation recommendation, and forecasting
• Agriculture insurance and Big Data
• Crop Loss forecasting

Day-4 : Session-1: Fraud Prevention BI from Big Data in Government-Fraud Analytics:

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

Day-4 : Session-2: Social Media Analytics- 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 profiles
• Live demos of each analytic will be conducted through the Treeminer Tool.

Day-4 : Session-3: Big Data Analytics in Image Processing and Video Feeds

• Image Storage techniques in Big Data- Storage solutions for data exceeding petabytes
• LTFS and LTO
• GPFS-LTFS (Layered storage solution for Big image data)
• Fundamentals 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 analytics for Pharmacogenomics, Metabolomics, and Proteomics
• Big Data in downstream Genomics processes
• Application of Big Data predictive analytics in Public health

Big Data Dashboard for Quick Accessibility and Display of Diverse Data :

• Integration of existing application platforms with Big Data Dashboards
• Big Data management
• Case Study of Big Data Dashboards: Tableau and Pentaho
• Using Big Data apps to push location-based services in Government
• Tracking systems and management

Day-5 : Session-1: Justifying Big Data BI Implementation Within an Organization:

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

Day-5 : Session-2: Step-by-Step Procedure to Replace Legacy Data Systems with Big Data Systems:

• Understanding a practical Big Data Migration Roadmap
• Essential information needed before architecting a Big Data implementation
• Methods for calculating volume, velocity, variety, and veracity of data
• Estimating data growth
• Case studies

Day-5: Session 4: Review of Big Data Vendors and 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)