Virtual Module Algorithms for InfoSphere MDM V11 - SPVC

 

Quem deve participar

This intermediate course is for Business and Technical Specialist working with the Matching, Linking, and Search services of InfoSphere MDM Virtual module.

Pré- requisitos

It is recommended that you take the following course prior to enrolling in this course:

  • (1Z801) or experience with InfoSphere MDM

Descrição do produto

Do you want to find match member records, link member records, and perfect a search algorithm for your InfoSphere MDM Virtual implementation? Then this course is designed for you. The InfoSphere MDM Virtual Module Algorithms v11 course prepares students to work with and customize the algorithm configurations deployed to the InfoSphere MDM Probabilistic Matching Engine (PME) for a Virtual MDM implementations. The PME is the heart of all Matching, Linking, and Searching for entities (Person, Organization, etc) that exist in InfoSphere MDM. This course has a heavy emphasis on the exercises, where the students will implement the customization discussed in the course to perform matching, linking, and searching on fields not provided by the default implementation. At the end of this course it is expected students will feel comfortable customizing an algorithm for the PME for a Virtual implementations.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.

http://www.ibm.com/training/terms

Outline

PME and Virtual Overview

  • Virtual MDM Overview
  • Terminology (Source, Entity, Member, Attributes)
  • PME and Virtual MDM ( Algorithms, Weights, Comparison Scores, Thresholds)
  • Virtual MDM Linkages and Tasks

Virtual MDM Algorithms

  • Standardization
  • Bucketing
  • Comparison Functions
  • Exercise: Creating a new Algorithm

Virtual PME Data Model

  • Algorithm configuration tables
  • Member Derived Data
  • Bucketing Data
  • Exercise: Loading Members and viewing Algorithm and Derived data

Bucket Analysis

  • Analysis Overview
  • Attribute Completeness
  • Bucket Analysis
  • Exercise: Analyzing our Buckets

Weights

  • Weights Overview (Frequency-based weights, Edit Distance weights and Parameterize weights)
  • The weight formula
  • Running weight generation
  • Analyzing weights
  • Bulk Cross Match process
  • Pair Manager
  • Threshold calculations
  • Exercise: Generate Weights and analyzing weight distribution
  • Exercise: Pair Manager and Threshold Calculations
  • Exercise: Testing our algorithm
E-Learning IBM Self-Paced Virtual Class (SPVC)

Subscription duration: 30 dias
Grace period: 1 ano