II Semester M.E. (Web Technology) Degree Examination, July 2014 (2K13 Scheme) WT 24 : DATA WAREHOUSE AND WEB MINING Time : 3 Hours
Max. Marks : 100
Instruction : Answer any five full questions from eight questions. 1. a) What is Data Warehouse ? Differentiate between operational and informational systems. 10 b) Discuss in detail continued growth in Data Warehousing. 2. a) List and explain different development phases in the life cycle of Data Warehouse Projects. b) With a neat diagram explain the 3-tier architecture of Data Warehouse System.
10 10 10
3. a) What is Multidimensional Data Model ? Explain the significance of Data Cubes and Lattice of cuboids with examples. 10 b) Why is the entity-relationship modelling technique not suitable for the Data Warehouse ? How is dimensional modelling different ? Discuss in detail.
10
4. a) Discuss the advantages and limitations of the three different schema with examples in Data Warehouse.
10
b) What is the need of DMQL ? Write the DMQL for three schema for any database in the multidimensional data model.
10
5. a) Explain in detail all the possible operations of OLAP with an example for each operation. 10 b) List all types of OLAP servers in data warehouse and explain the main features of all OLAP servers. 10 6. a) What are different types of indexing techniques used in data warehouse ? Explain in detail bitmap indexing with examples. b) With a neat diagram explain the integrated OLAM and OLAP architecture.
10 10 P.T.O.
*JEP1008*
JEP – 1008
7. a) Explain the working principle of large-scale crawler with a neat sketch.
10
b) Discuss in detail the boolean queries and inverted index for wed search and information retrieval. 10 8. a) Discuss the bottom-up and top-down partitioning paradigms in Data Mining System.
10
b) What is an authoritative web page ? Explain in detail mining web’s links structures to identity authoritative web pages.
8. a) Discuss the bottom-up and top-down partitioning paradigms in Data Mining. System. 10. b) What is an authoritative web page ? Explain in detail mining ...
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