Data Warehousing Introduction to Data Warehousing 1001 DW

Data Warehousing 資料倉儲 Introduction to Data Warehousing 1001 DW 01 MI 4 Tue. 6, 7 (13: 10 -15: 00) B 427 Min-Yuh Day 戴敏育 Assistant Professor 專任助理教授 Dept. of Information Management, Tamkang University 淡江大學 資訊管理學系 http: //mail. im. tku. edu. tw/~myday/ 2011 -09 -06 1

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Knowledge Discovery (KDD) Process l Data Warehouse: fundamental process for Data Mining and Business Intelligence l Data mining: core of knowledge discovery process Pattern Evaluation Data Mining Task-relevant Data Warehouse Selection Data Cleaning Data Integration Databases Source: Han & Kamber (2006) 5

Data Warehouse Data Mining and Business Intelligence Increasing potential to support business decisions Decision Making Data Presentation Visualization Techniques End User Business Analyst Data Mining Information Discovery Data Analyst Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems Source: Han & Kamber (2006) DBA 6

Syllabus 週次 日期 1 100/09/06 2 100/09/13 Intelligence 3 100/09/20 process 4 100/09/27 5 100/10/04 6 100/10/11 7 100/10/18 8 100/10/25 9 100/11/01 內容（Subject/Topics） Introduction to Data Warehousing, Data Mining, and Business Data Preprocessing: Integration and the ETL Data Warehouse and OLAP Technology Data Cube Computation and Data Generation Project Proposal 期中考試週 8

Syllabus 週次 日期 10 100/11/08 11 100/11/15 12 100/11/22 13 100/11/29 14 100/12/06 15 100/12/13 16 100/12/20 17 100/12/27 18 101/01/03 內容（Subject/Topics） Association Analysis Classification and Prediction Cluster Analysis Sequence Data Mining Social Network Analysis Link Mining Text Mining and Web Mining Project Presentation 期末考試週 9

Course Introduction • This course introduces the fundamental concepts and technology of data warehousing. • Topics include data warehousing, data mining, business intelligence, OLAP, data cube, association analysis, classification, cluster analysis, social network analysis, text mining, and web mining. 10

Objective • Students will be able to understand apply the fundamental concepts and technology of data warehousing. 11

Data Mining: Concepts and Techniques (Second Edition) http: //www. amazon. com/Data-Mining-Concepts-Techniques-Management/dp/1558609016 14

Team Term Project • Term Project Topics – Data Warehousing – Business Intelligence – Data mining – Text mining – Web mining – Social Network Analysis – Link Mining • 3 -5 人為一組 – 分組名單於 2011. 09. 20 (二) 課程下課時繳交 – 由班代統一收集協調分組名單 16

Typical framework of a data warehouse Source: Han & Kamber (2006) 17

Multidimensional data cube for data warehousing Drill-down Roll-up Source: Han & Kamber (2006) 18

Example of Star Schema time item time_key day_of_the_week month quarter year Sales Fact Table time_key item_key branch_key branch_name branch_type location_key units_sold dollars_sold avg_sales item_key item_name brand type supplier_type location_key street city state_or_province country Measures Source: Han & Kamber (2006) 19

Architecture of a typical data mining system Graphical User Interface Pattern Evaluation Knowledge-Base Data Mining Engine Database or Data Warehouse Server data cleaning, integration, and selection Database Data Warehouse World-Wide Web Source: Han & Kamber (2006) Other Info Repositories 20

Social Network Analysis Source: http: //www. fmsasg. com/Social. Network. Analysis/ 21

Text Mining Source: http: //www. amazon. com/Text-Mining-Applications-Michael-Berry/dp/0470749822/ 22

Web Mining and Social Networking Source: http: //www. amazon. com/Web-Mining-Social-Networking-Applications/dp/1441977341 23

Mining the Social Web: Analyzing Data from Facebook, Twitter, Linked. In, and Other Social Media Sites Source: http: //www. amazon. com/Mining-Social-Web-Analyzing-Facebook/dp/1449388345 24

Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data Source: http: //www. amazon. com/Web-Data-Mining-Data-Centric-Applications/dp/3540378812 25

NTCIR Project (NII Test Collection for IR Systems) Source: http: //research. nii. ac. jp/ntcir-9/index. html 26

NTCIR-9 RITE Recognizing Inference in TExt @NTCIR 9 Source: http: //artigas. lti. cs. cmu. edu/rite/Main_Page) 27

NTCIR-9 RITE Recognizing Inference in TExt @NTCIR 9 Source: http: //artigas. lti. cs. cmu. edu/rite/Main_Page_(TC) 28

Contact Information 戴敏育 博士 (Min-Yuh Day, Ph. D. ) 　 專任助理教授 淡江大學 資訊管理學系 電話： 02 -26215656 #2347 傳真： 02 -26209737 研究室：I 716 (覺生綜合大樓) 地址： 25137 新北市淡水區英專路 151號 Email： myday@mail. tku. edu. tw 網址：http: //mail. im. tku. edu. tw/~myday/ 29