Big Data Business Intelligence for Criminal Intelligence Analysis 교육 과정

Course Code

bigdatabicriminal

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

기술의 발전과 증가하는 정보의 양은 법 집행이 수행되는 방식을 변화시키고 있습니다. 과제 Big Data 포즈는 거의 어려운 있습니다 Big Data 의 약속. 데이터를 효율적으로 저장하는 것은 이러한 과제 중 하나입니다. 효과적으로 분석하는 것도 또 다른 방법입니다.

이 강사 주도형 라이브 교육에서 참가자는 Big Data 기술에 접근하고 기존 프로세스 및 정책에 미치는 영향을 평가하고 범죄 활동을 식별하고 범죄를 예방할 목적으로 이러한 기술을 구현할 수있는 사고 방식을 배우게됩니다. 전 세계의 법 집행 기관의 사례 연구를 살펴보고 채택 방식, 과제 및 결과에 대한 통찰력을 얻습니다.

이 교육이 끝나면 참가자는 다음을 수행 할 수 있습니다.

  • Big Data 기술과 기존 데이터 수집 프로세스를 결합하여 조사 중 스토리를 구성합니다.
  • 데이터 분석을위한 산업용 빅 데이터 스토리지 및 처리 솔루션 구현
  • 범죄 조사에 대한 데이터 중심 접근 방식을 가능하게하는 가장 적절한 도구 및 프로세스 채택을위한 제안서 준비

청중

  • 기술적 배경이있는 법 집행 전문가

과정의 형식

  • 강의, 강의, 연습 및 실습

Machine Translated

Course Outline

=====
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

회원 평가

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