1、培训过程中，如有部分内容理解不透或消化不好，可免费在以后培训班中重听；
        2、培训结束后,授课老师留给学员联系方式,保障培训效果,免费提供课后技术支持。
        3、培训合格学员可享受免费推荐就业机会。

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