Advanced Concepts In Database Systems

Paper Code: 
CSC-144(B)
Credits: 
04
Max. Marks: 
100.00
Objective: 

This course will enable students:

  • To provide a deeper understanding of Query Processing and Optimization.
  • To introduce the fundamental techniques of various types of databases.
  • To provide understanding of Data warehousing and Data mining concepts.
  • To prepare students for advanced database concepts.

 

12.00

Query Processing, Optimization & Database Tuning: Algorithms for Executing Query Operations, Heuristics for Query Optimizations, Estimations of Query Processing Cost, Join Strategies For Parallel Processors, Database Workloads, Tuning Decisions, DBMS Benchmarks, Clustering & Indexing, Multiple Attribute Search Keys, Query Evaluation Plans, Pipelined Evaluations, System Catalogue In RDBMS.

12.00

Extended Relational Model & Object Oriented Database System: New Data Types, User Defined Abstract Data Types, Structured Types, Object Identity, Containment, Class Hierarchy, Logic Based Data Model, Data Log, Nested Relational Model and Expert Database System.

12.00

Distributed Database System: Structure of Distributed Database, Data Fragmentation, Data Model, Query Processing, Semi Join, Parallel & Pipeline Join, Distributed Query Processing In R * System, Concurrency Control In Distributed Database System, Recovery In Distributed Database System, Distributed Deadlock Detection And Resolution, Commit Protocols.

12.00

Enhanced Data Model For Advanced Applications: Database Operating System, Introduction to Temporal Database Concepts, Spatial and Multimedia Databases, Active Database System, Introduction to Deductive Databases, Database Machines, Basics of Web Databases, Advanced Transaction Models, Issues in Real Time Database Design.

12.00

Data Warehousing and Data Mining: Basic Elements of the Data Warehouse, DBMS vs. data warehouse, Data Warehouse and OLTP Database Design, Differences, Data Warehouse Features, Manage Data, Decision Support System (DSS), Data Warehousing Process.

Data Mining: KDD versus data mining, data mining techniques, functionalities, tools and applications, Data mining query, data and knowledge specification, hierarchy specification, pattern presentation & visualization specification, data mining techniques, tools and applications.

Data Mining Tools and Techniques: Association rules, Clustering techniques, Decision tree knowledge discovery, Introduction to Data Mining Tools.

 

REFERENCES: 

1.      Majumdar & Bhattacharya,” Database Management System”, TMH.

2.      Korth, Silbertz, Sudarshan,” Database Concepts”, McGraw Hill.

3.      Elmasri, Navathe,” Fundamentals of Database Systems”, Addison Wesley.

4.      Data C J,” An Introduction to Database System”, Addison Wesley.

5.      Ramakrishnan, Gehrke,” Database Management System”, McGraw Hill.

6.      Bernstein, Hadzilacous, Goodman,” Concurrency Control & Recovery”, Addison Wesley.

7.      Ceri & Palgatti,” Distributed Databases”, McGraw Hill.

8.      Peter Rob, Carlos Coronel,” Database systems: design, implementation, and management”, Thomson Learning, 2009.

9.      Berson, Stephen J. Smith,” Data Warehousing, Data Mining, & Olap”, Tata McGraw-Hill, 2004.

10.  C.S.R. Prabhu, Data warehousing:” Concepts, Techniques, Products and Applications”, PHI, 2nd edition, 2006.

11.  Jiawei Han & Micheline Kamber,” Data Mining – Concepts & Techniques”, Morgan Kaufmann, 2006.

12.  Soumen Chakrabarti, Earl Cox, Ian H. Witten, “Data mining: know it all”, Morgan Kaufmann, 2009.

13.  Pang-Ning Tan, Michael Steinbach, and Vipin Kumar,” Introduction to Data Mining”, Addison-Wesley, 2005

 

Academic Year: 
2016-17
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