IT6702 Important Questions Data Warehousing and Data Mining Regulation 2013 Anna University

IT6702 Important Questions Data Warehousing and Data Mining

IT6702 Important Questions Data Warehousing and Data Mining Regulation 2013 Anna University free download. Data Warehousing and Data Mining IT6702 Important Questions pdf free download.

Sample IT6702 Important Questions Data Warehousing and Data Mining

1 With a neat sketch, Describe in detail about Data warehouse architecture. (Nov/Dec
2012. (OR) List and discuss the characteristics and main functions performed by the
components of a data warehouse. Give diagrammatic illustration. (May/June
2014,May/June 2012)
2 List and discuss the steps involved in building a data warehouse. (Nov/Dec 2012) IT6702 Important Questions Data Warehousing and Data Mining
3 Give detailed information about Meta data in data warehousing. (May/June 2014)
4 List and discuss the steps involved in mapping the data warehouse to a
multiprocessor architecture. (May/June 2014, Nov/Dec 2011)
5 i) Explain the role played by sourcing, acquisition, clean up and transformation tools IT6702 Important Questions Data Warehousing and Data Mining
in data warehousing. (May/June 2013)
ii) Explain about STAR Join and STAR Index. (Nov/Dec 2012)
6 Describe in detail about DBMS schemas for decision support.
7 Explain about data extraction, clean up and transformation tools.
8 Explain the following:
i) Implementation considerations in building data warehouse
ii) Database architectures for parallel processing.

13. Differentiate data mining and data ware housing. (Nov/Dec 2011) IT6702 Important Questions Data Warehousing and Data Mining
 Data mining refers to extracting or “mining” knowledge from large amounts of
data. The term is actually a misnomer. Remember that the mining of gold from rocks
or sand is referred to as gold mining rather than rock or sand mining. Thus, data
mining should have been more appropriately named “knowledge mining from data,”
 A data warehouse is usually modeled by a multidimensional database structure,
where each dimension corresponds to an attribute or a set of attributes in the schema,
and each cell stores the value of some aggregate measure, such as count or sales
amount.