Topic outline

  • General

    SGG4653
     
    Lecturer :  Ivin Amri Musliman
    Semester :  Semester II 2011/2012
    		logo database

    Synopsis :

    This course discusses the requirements for advanced database applications and examines the concepts of various emerging database technologies such as distributed and interoperability databases, data warehousing, data mining, data quality, service-oriented architecture, workflows, RDBMS vs. ORDBMS vs. OODBMS, GIS geo-DBMS application integration and optimization.

    Course Outcomes :

    By the end of the course, the student should be able to :

    1. Understand the needs and concepts of object-oriented database, spatial database, web database, data warehousing and data mining.
    2. Be able to analyze, design and evaluate the construct of various advanced databases such as object-oriented, object-relational, semi-structured, unstructured and distributed databases for Geo-DBMS warehouses.
    3. Be able to implement practical solutions to GIS database problems using OO/OR database, spatial database, data warehousing and data mining approaches.
    4. Have the ability to present and discuss issues regarding emerging database technologies.

    Creative Commons License This work, SGG4653 Advance Database System by Ivin Amri Musliman is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License

  • Topic 1

    Introduction to the Course

    An Overview of Basic Concepts and Database System Architecture:
    • The Database Management System (DBMS)
    • Client Server Architecture
    • Distributed DBMS
      • Introduction
      • Distributed Relational Database Design
      • Transparencies in a DDBMS
      • Rules for a DDBMS
    • Distributed Processing
    • Spatial Database:
      • Introduction
      • Spatial Data model
      • Spatial Data Structure
      • Spatial Data Query Language

  • Topic 2

    Introduction and Preliminaries of:

    Object-oriented DBMS
    • The framework for an object data model.
    • The basics of persistent programming languages
    • The main strategies for developing an OODBMS.
    • Various issues underlying OODBMS: Extended transaction models, version management, schema evolution, OODBMS architecture, benchmarking.
    • The advantages and disadvantages of OODBMS.
    • The basics of object-oriented database design.
    • Object Management Group (OMG) and Object Management Architecture (OMA).
    • Common Object Request Broker Architecture (CORBA).
    • ODMG Object Database Standard:
    • Object Model
    • Object Definition Language (ODL)
    • Object Query Language (OQL)

  • Topic 3

    Introduction and Preliminaries of:

    Object-relational DBMS
    • How the relational model has been extended to support advanced database applications.
    • The object-oriented features proposed in the next SQL standard, SQL3 .
    • Extensions required to relational query processing and query optimization to support advanced queries.
    • Some object-oriented extensions to Oracle.
    • How OODBMSs and ORDBMSs compare in terms of data modeling, data access, and data sharing.

  • Topic 4

    Semi-structured Data and XML

    Semi-structured Data
    • XML-Related Technologies
    • XML Schema
    • XML Query Language
    • XML and Database

  • Topic 5

    Data Warehousing

    • Introduction
    • Today’s development environment
    • Types of data and their uses
    • Conceptual Data Architecture
    • Design Techniques
    • Populating Business Data Warehouse
    • Warehouse Maintenance
    • OLAP (OLAP tools, OLAP Extensions to SQL)

  • Topic 6

    Data Mining

    • Introduction
    • The process of knowledge discovery
    • The kinds of data that can be mined
    • Types of data mining tasks
    • Association Rule
    • Classification
    • Clustering

    Papers for Reading:
    1. Rakesh Agrawal, Tomasz Imielinski, and Arun Swami. Mining Associations between Sets of Items in Large Databases. In Proceedings of the ACM SIGMOD International Conference on the Management of Data, pages 207-216, May 1993.
    2. Rakesh Agrawal, Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In Proceedings of the 20th International Conference on Very Large Databases, pages 487-499, September 1994.
    3. Jong Soo Park, Ming-Syan Chen, and Philip S. Yu. An Effective Hash Based Algorithm for Mining Association Rules. In Proceedings of the ACM SIGMOD International Conference on the Management of Data, pages 175-186, May 1995.
    4. R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules in Large Databases, in Proceedings of the 20th International Conference on Very Large Databases (VLDB'94), pages 487-499, Santiago de Chile, Chile, Sep 1994.
    5. S. Sarawagi, S. Thomas, and R. Agrawal, Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications, Data Mining and Knowledge Discovery, Vol. 4, Nos. 2-3, pages 89-125, July 2000.
    6. S. Brin, R. Motwani, J.D. Ullman, and S. Tsur, Dynamic Itemset Counting and Implication Rules for Market Basket Data, in Proceedings of the ACM SIGMOD International Conference on Management of Data, Tucson, AZ, pages 255-264, 1997.
    7. J.S. Park, M.-S. Chan, and P.S. Yu, An Effective Hash-Based Algorithm for Mining Association Rules, in Proceedings of the ACM SIGMOD International Conference on Management of Data, San Jose, CA, pages 175-186, 1995.
    8. D. Tsur, J.D. Ullman, S. Abiteboul, C. Clifton, R. Motwani, S. Nestorov, and A. Rosenthal, Query Flocks: A Generalization of Association-Rule Mining, in Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 1-12, Seattle, WA, 1998.
    9. T. Imielinski and H. Mannila, A Database Perspective on Knowledge Discovery, Communications of the ACM, Vol. 39, No. 11, pages 58-64, Nov 1996.
    10. J. Gehrke, V. Ganti, R. Ramakrishnan, and W.-Y. Loh, BOAT: Optimistic Decision Tree Construction, in Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 169-180, Philadelphia, PA, 1999.
    11. G.H. John, Behind-the-Scenes Data Mining, ACM SIGKDD Explorations, Vol. 1, No. 1, pages 6-8, June 1999.
    12. H. Blockeel, L. De Raedt, N. Jacobs, and B. Demoen, Scaling up ILP by Learning from Interpretations, Data Mining and Knowledge Discovery, Vol. 3, No. 1, pages 59-93, March 1999.
    13. N. Ramakrishnan and C. Bailey-Kellogg, Sampling Strategies for Mining in Data-Scarce Domains, IEEE/AIP Computing in Science and Engineering (CiSE), Vol. 4, No. 4, pages 31-43, July/Aug 2002.

  • Topic 7

    Database Integration: Desktop Customization for GIS

    • GIS application customization using Visual Basic 6.0
    • System design and analysis for for GIS application (networked)
    • Database normalization
    • SQL (query) optimization

  • Topic 8

    Lab Exercises, Assignments & Test Questions