Datawarehousing and Analytics. Data-Warehouse-, Data-Mining- und OLAP-Technologien. Advanced Information Management
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1 Anwendersoftware a Datawarehousing and Analytics Data-Warehouse-, Data-Mining- und OLAP-Technologien Advanced Information Management Bernhard Mitschang, Holger Schwarz Universität Stuttgart Winter Term 2014/2015
2 Departments of Institute of Parallel and Distributed Systems (IPVS) Applications of Parallel and Distributed Systems Prof. B. Mitschang, Prof. M. Herschel Machine Learning and Robotics Prof. M. Toussaint Parallel Systems Prof. S. Simon Simulation of Large Systems Prof. M. Mehl, Jun.-Prof. D. Pflüger Distributed Systems Prof. K. Rothermel 2 Infrastructure Dipl.-Inf. M. Matthiesen Universität Stuttgart
3 Applications of Parallel and Distributed Systems Data and Metadata Repository Technologies, Data Warehouse and Data Mining, Domain-specific query optimization and processing, Product Data Management, Data Integration Content and Semantics Focussed Semantic Search, Scalable Content Management Information Systems and Applications Database Middleware, Information Services, Generative Application Development, Model-driven Engineering, Technical Information Systems Data in the Cloud Federated Systems / Application Integration Metadata Management Content Management Business Intelligence / Business Processes Query Optimization 3 Universität Stuttgart
4 How to contact us Anwendersoftware a Lecture Bernhard Mitschang Office: Tel.: bernhard.mitschang@ipvs.uni-stuttgart.de Exercises and assignments Holger Schwarz Office: Tel.: holger.schwarz@ipvs.uni-stuttgart.de 4
5 Planned Schedule Anwendersoftware a Monday 9/29/14 Tuesday 9/30/14 Wednesday 10/1/14 Thursday 10/2/14 Tuesday 10/7/14 Wednesday 10/8/14 09:00 11:15 Chapter 1 Introduction Chapter 2 Data Warehouse Architecture Chapter 3 Design Process Conceptual Design Logical Design Chapter 4 Monitoring Extraction Transformation Load Tools Chapter 6 Data Mining Introduction Applications KDD Chapter 7 SQL & OLAP SQL & Mining Database Support Materialized Summary Data Derivability Break 12:00 14:15 Chapter 2 Data Marts Operational Data Store Meta Data Chapter 3 Extended Dimension Table Design Extended Fact Table Design Physical Design Chapter 5 OLAP Architecture Storage of Data Cubes Chapter 6 Assoc. Rules Clustering Classification Regression Tools and Trends Chapter Examples and Miscellaneous Wrap up Intro to Assignments Break 15:00 16:30 Intro SQL and ODPS Issues of data integration Data Warehouse Architecture Conceptual and Logical Data Warehouse Design Monitoring Storage of Data Cubes ETL Transformation Cleansing 15:00 17:15 Data Mining Classification Clustering Association Rules Lectures Exercises 5
6 Exercises and Assignments Anwendersoftware a Type Description Date Exercise Assignment Assignment Detailed discussion of major topics, case studies, examples etc. (all) Hands-on training for topics related to dbms (groups of 2-3 students) Hands-on training for ETL, OLAP and data mining (groups of 2-3 students) September 29 October 8 Introduction on October 8 Due: TBA Introduction on October 8 Due: TBA 6
7 Anwendersoftware a Teaching Materials General information: Slides, exercises, assignments, Login to ILIAS: Search and Join the course "Data Warehousing and Analytics" Repository -> Engineering -> Computer Science -> Lehrveranstaltungen WS 14/15 7
8 Anwendersoftware a Exams IMSE Exam: Friday, December 12 Informatik / Softwaretechnik / Infotech / Wirtschaftsinformatik / Register at your examination office Make an appointment for the oral exam Appointments for oral exams Annemarie Roesler Tel Annemarie.Roesler@ipvs.uni-stuttgart.de 8
9 Anwendersoftware a Books [BG04] A. Bauer, H. Günzel: Data Warehouse Systeme. 2. Aufl., dpunkt, [Len03] [KR+98] W. Lehner: Datenbanktechnologie für Data-Warehouse-Systeme. dpunkt, R. Kimball, L. Reeves, M. Ross, W. Thornthwaite: The Data Warehouse Lifecycle Toolkit. Wiley, [Inm05] W. H. Inmon: Building the Data Warehouse. 4th Edition, Wiley, [HK00] J. Han, M. Kamber: Data Mining Concepts and Techniques. Morgan Kaufmann, 2nd Edition, [JL+02] M. Jarke, M. Lenzerini, Y. Vassiliou, P. Vassiliadis: Fundamentals of Data Warehouses. Springer, [Kim96] R. Kimball: The Data Warehouse Toolkit. Wiley, 1996 [LN07] U. Leser, F. Naumann: Informationsintegration, dpunkt, [Wes01] P. Westerman: Data Warehousing. Morgan Kaufmann
![Kamber: Data Mining Concepts and Techniques. Morgan Kaufmann, 2nd Edition, 2006. [JL+02] M. Jarke, M. Lenzerini, Y. Vassiliou, P. Vassiliadis: Fundamentals of Data Warehouses. Springer, 2002.](/docs-images/42/2063289/images/page_9.jpg "[Kim96] R. Kimball: The Data Warehouse Toolkit. Wiley, 1996 [LN07] U. Leser, F. Naumann: Informationsintegration, dpunkt, 2007. [Wes01] P. Westerman: Data Warehousing. Morgan Kaufmann. 2001. 9")
10 Anwendersoftware a Papers [Ber98] [CD97] [HLV00] [Zeh03] P. Bernstein: Repositories and Object Oriented Databases, SIGMOD Record 27(1):88-96, S. Chaudhuri, U. Dayal: An Overview of Data Warehousing and OLAP Technology, SIGMOD Record 26(1):65-74, B. Hüsemann, J. Lechtenbörger, G. Vossen: Conceptual Data Warehouse Design. Proc. of the Second International Workshop on Design and Management of Data Warehouses, Stockholm, T. Zeh: Data Warehousing als Organisationskonzept des Datenmanagements. In: Informatik Forschung und Entwicklung, Band 18, Heft 1, August
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DATA MINING TECHNOLOGY. Keywords: data mining, data warehouse, knowledge discovery, OLAP, OLAM.
DATA MINING TECHNOLOGY Georgiana Marin 1 Abstract In terms of data processing, classical statistical models are restrictive; it requires hypotheses, the knowledge and experience of specialists, equations,
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Data Warehousing - Essential Element To Support Decision- Making Process In Industries Ashima Bhasin 1, Mr Manoj Kumar 2 1 Computer Science Engineering Department, 2 Associate Professor, CSE Abstract SGT