We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published bySean Schroeder
Modified over 4 years ago
© 2002 page 1 Data Mining Tools For ZLE Copying and Use Restrictions: Material under this presentation is the Intellectual Property of HP Corporation and Genus Software. Any use of the this material, in part or whole, except in context of Genus Data Mining Integrator and Data Mart Builder, without written permission from HP and Genus is prohibited.
© 2002 page 2 agenda data mining in ZLE solutions ZLE data mining toolkit toolkit demonstration agenda
© 2002 page 3 title text Meta Group process of identifying and/or extracting previously unknown, non-trivial, unanticipated, important information from large sets of data Gartner Group process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies, statistical and mathematical techniques
© 2002 page 4 title text role –determine most effective responses to business events ZLE facilitates mining by providing –a rich, integrated, current data source –an integrated operational environment into which models can be deployed data mining helps to realize the full business value of a ZLE system
© 2002 page 5 derive attributes identify and define business opportunity create case set deploy model profile data transform data assess performance train models typically about 75% of process ZLE data mining process understand the opportunity –identify and define business opportunity prepare data –profile and understand data –derive attributes –transform data –create case set build models –train models –assess model performance use models –deploy model –monitor model performance monitor model performance
© 2002 page 6 agenda data mining in ZLE solutions ZLE data mining toolkit toolkit demonstration agenda
© 2002 page 7 the ZLE data mining toolkit goal: –provide tools that facilitate ZLE data mining –reduce process cycle times dramatically three tools being developed by Genus Software: –data preparation –data transfer –model deployment partners: Genus, MicroStrategy, SAS product names: –Genus Mining Integrator for NonStop SQL (all three tools) –Genus Mart Builder for NonStop SQL (first two tools only)
© 2002 page 8 part of Genus toolkit ZLE data mining analytical cycle Data Store (NonStop SQL) Data Preparation (profiling/transforming data) Model Deployment (written to DB tables) Data Transfer (fast parallel streams) Mining Mart (Tru64/Windows) Scoring Engine Rules Engine Agg. Engine Interaction Manager Real-Time Scoring (using the Recommender) part of ZDK 3 Modeling (SAS Enterprise Miner) available from SAS
© 2002 page 9 agenda data mining in ZLE solutions ZLE data mining toolkit toolkit demonstration agenda
© 2002 page 10 toolkit demonstration credit card fraud detection example opportunity: use ZLE data store data to predict, in real-time, which credit card purchases are likely to be fraudulent use tools to: –build a case set table with one row describing each purchase –transfer table to SAS server for modeling –deploy predictive model to ZLE data store –execute model in real-time to make fraud predictions steps described, including many tool screen shots
© 2002 page 11 based on the MicroStrategy (MSI) Business Intelligence toolset, leverages GUI, logical data model support, SQL generation, etc. uses NonStop SQL/MX DBMS, leverages sampling, TRANSPOSE, statistical functions, … custom tool developed by Genus using MSI SDK for NonStop SQL operations and functionality not supported by MSI tools toolkit data preparation solution
© 2002 page 12 two main ZLE data preparation tasks 1.profile tables –column names and types –partitioning information, attributes, key structure, … –column values 2.transform source tables –derive new attributes –aggregate to appropriate level –clean data –pivot –combine to form case set
© 2002 page 13 the MicroStrategy desktop
© 2002 page 14 MSI profile report: fraud vs. billing state
© 2002 page 15 NonStop SQL/MX sampling source table sampling –insert into CustSamp select * from Cust sample random 1 percent clusters of 10 blocks union select * from Cust where CardNo in (select CardNo from FrdFlg) enables interactive and exploratory data prep cleanly integrated into SQL performed efficiently in DP2 easily accessible through Genus tool
© 2002 page 16 creating a materialized sample table using the Genus Data Mart Builder
© 2002 page 17 identifying source and sample method
© 2002 page 18 specifying materialized sample table
© 2002 page 19 transforming source data Billions of Purchase s Millions of Accounts Purchase StoreAccount Purchase History Item Summary Fraud Aggregate and Pivot
© 2002 page 20 result: a case set for modeling Hundreds of Attributes One Row Per Purchase Mix of Fraud and No-Fraud Purchases
© 2002 page 21 MSI Datamart report summarizing items
© 2002 page 22 data transfer tool Data StoreMining Mart NonStop SQL/MX ASCII filesSAS data set data transfer tool task: transfer case set from data store to mining mart coordinator –design HTML HTTP JDBC Web browser client Web server Web App. receive SAS import transferreceive SAS import transferreceive SAS import transfer receive SAS import transfer
© 2002 page 23 data transfer specification screen
© 2002 page 24 transfer monitoring
© 2002 page 25 modeling in SAS enterprise miner
© 2002 page 26 body copy model export score converter node generates Java model code reporter node exports code and HTML report to project directory
© 2002 page 27 NonStop SQL/MX Data Store SAS Open Metadata server File/SAS server SAS Enterpris e Miner Mining Mart model deployment tool task –copy model information to a ZLE Data Store Model export/registration –design HTML HTTP JDBC access Web browser client File/registry access Web Server Web App.
© 2002 page 28 starting the model deployment tool
© 2002 page 29 connecting to a Data Store
© 2002 page 30 a list of models in the Data Store
© 2002 page 31 viewing a deployed model
© 2002 page 32 selecting a SAS report directory
© 2002 page 33 viewing available reports
© 2002 page 34 viewing an Enterprise Miner report
© 2002 page 35 deploying a model
© 2002 page 36 deployment confirmation
© 2002 page 37 real-time scoring using the Recommender Scoring Engine Aggregation Engine Rules Engine Model Aggregates Model Scores Deployed Models Business Rules Aggregate Definitions Offers / Advice Customer Data Interaction Manager
© 2002 page 38 how to get the data mining tools Product Names –Genus Mining Integrator for NonStop SQL (Data Preparation, Data Transfer, and Model Deployment tools) –Genus Mart Builder for NonStop SQL (first two tools only) Can be ordered through HP, support provided by Genus Availability: calendar Q4 2002 For more information, contact –email@example.com (Product Manager)firstname.lastname@example.org –email@example.com (Program Manager)firstname.lastname@example.org –email@example.com (Development)firstname.lastname@example.org
2011年上半年 我院团学工作活动图片展播 2011年8月28日.
1 Senn, Information Technology, 3 rd Edition © 2004 Pearson Prentice Hall James A. Senns Information Technology, 3 rd Edition Chapter 7 Enterprise Databases.
1 Vorlesung Informatik 2 Algorithmen und Datenstrukturen (Parallel Algorithms) Robin Pomplun.
Chapter 1: The Database Environment
1 Copyright © 2002 Pearson Education, Inc.. 2 Chapter 2 Getting Started.
© 2008 Pearson Addison Wesley. All rights reserved Chapter Seven Costs.
Copyright © 2003 Pearson Education, Inc. Slide 1 Computer Systems Organization & Architecture Chapters 8-12 John D. Carpinelli.
Chapter 1 The Study of Body Function Image PowerPoint
McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. Extended Learning Module D (Office 2007 Version) Decision Analysis.
Copyright © 2011, Elsevier Inc. All rights reserved. Chapter 6 Author: Julia Richards and R. Scott Hawley.
Author: Julia Richards and R. Scott Hawley
1 Copyright © 2013 Elsevier Inc. All rights reserved. Chapter 3 CPUs.
Properties Use, share, or modify this drill on mathematic properties. There is too much material for a single class, so you’ll have to select for your.
UNITED NATIONS Shipment Details Report – January 2006.
RXQ Customer Enrollment Using a Registration Agent (RA) Process Flow Diagram (Move-In) Customer Supplier Customer authorizes Enrollment ( )
Business Transaction Management Software for Application Coordination 1 Business Processes and Coordination. Introduction to the Business.
18 Copyright © 2005, Oracle. All rights reserved. Distributing Modular Applications: Introduction to Web Services.
1 RA I Sub-Regional Training Seminar on CLIMAT&CLIMAT TEMP Reporting Casablanca, Morocco, 20 – 22 December 2005 Status of observing programmes in RA I.
Properties of Real Numbers CommutativeAssociativeDistributive Identity + × Inverse + ×
© 2018 SlidePlayer.com Inc. All rights reserved.