DW Lecture VI (3)

DW Lecture VI (3) - Data ware housing and Busine I nte...

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By Dr. Atanu Rakshit Data warehousing and Business Intelligence using SAS (Lecture V)
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2 Course Overview The course: what and how 0. Introduction: The Past and The  Problem I. Data Warehousing II. Decision Support and OLAP III. Data Mining IV. Usage of SAS for DW and DM V.  Business Intelligence and its use VI. Looking Ahead
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3 Introduction The Past and The Problem
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Part 3: Data Mining
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5 Data Is Everywhere! Relational database—A commodity of every enterprise Huge data warehouses are under construction POS (Point of Sales): Transactional DBs in terabytes Object-relational databases, distributed, heterogeneous, and legacy  databases Spatial databases (GIS), remote sensing database (EOS), and  scientific/engineering databases Time-series data (e.g., stock trading) and temporal data Text (documents, emails) and multimedia databases WWW: A huge, hyper-linked, dynamic, global information system
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6 Data Mining Is Everywhere, too! A Multi- Dimensional View of Data Mining Databases to be mined Relational, transactional, object-relational, active, spatial, time-series,  text, multi-media, heterogeneous, legacy, WWW, etc. Knowledge to be mined Characterization, discrimination, association, classification, clustering,  trend, deviation and outlier analysis, etc. Techniques utilized Database-oriented, data warehouse (OLAP), machine learning,  statistics, visualization, neural network, etc. Applications adapted Retail, telecommunication, banking, fraud analysis, DNA mining, stock  market analysis, Web mining, etc.
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7 Data Mining: Confluence of Multiple Disciplines Data Mining Database  Technology Statistics Other Disciplines Information Science Machine Visualization High-Performance Computing
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8 Data Mining: A Long vs. Short History? Most scientific discoveries involve “data mining” Kepler’s Law, Newton’s Laws, periodic table of chemical  elements, …, from “big bang” to DNA  Statistics: A discipline dedicated to data analysis Then why data mining? What are the differences? Fast computer—quick response, interactive analysis Multi-dimensional, powerful, thorough analysis High-level, “declarative”—user’s ease and control Automated or semi-automated—mining functions hidden or built- in in many systems
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9 Data Mining works with Warehouse Data Data Warehousing provides  the Enterprise with a memory Data Mining provides the  Enterprise with intelligence
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10 We want to know . .. Given a database of 100,000 names, which persons are the least 
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This note was uploaded on 07/15/2011 for the course ECO 2023 taught by Professor Mr.raza during the Summer '10 term at FAU.

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DW Lecture VI (3) - Data ware housing and Busine I nte...

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