MIT401– Data Warehousing and Data Mining

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ASSIGNMENT

PROGRAM	Master of Science in Information Technology(MSc IT)Revised Fall 2011
SEMESTER	4
SUBJECT CODE & NAME	MIT401– Data Warehousing and Data Mining
CREDIT	4
BK ID	B1633
MAX.MARKS	60

Note: Answer all questions. Kindly note that answers for 10 marks questions should be approximately of 400 words. Each question is followed by evaluation scheme.

Q1. Explain the Functionalities and advantages of Data Warehouses.

Answer: A common way of introducing data warehousing is to refer to the characteristics of a data warehouse.

·         Subject Oriented

·         Integrated

·         Nonvolatile

·         Time Variant

Q2 Explain the Data Warehouse Kimball life cycle.

Answer : The Kimball Lifecycle methodology was conceived during the mid-1980s by members of the Kimball Group and other colleagues at Metaphor Computer Systems, a pioneering decision support company. Since then, it has been successfully utilized by thousands of data warehouse and business intelligence (DW/BI) project teams across virtually every industry, application area, business function, and technical platform.

Q3 Describe about Hyper Cube and Multicube.

Answer: Multidimensional databases can present their data to an application using two types of cubes: hypercubes and multicubes. In the hypercube model, as shown in the following illustration, all data appears logically as a single cube. All parts of the manifold represented by this hypercube have identical dimensionality.

Q4. List and explain the Strategies for data reduction.

Answer: Data reduction is the process of minimizing the amount of data that needs to be stored in a data storage environment. Data reduction can increase storage efficiency and reduce costs.

Strategies for data reduction:

TAKE ADVANTAGE OF EXISTING INFORMATION: First of all, we don't want to reinvent the wheel. There's a lot of existing information out there for community health coalitions to take advantage of. Know your community's history! Has this initiative or something similar been tried here before? Even a failed attempt has valuable information to offer.

Q5 Describe K-means method for clustering. List its advantages and drawbacks.

Answer: k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.

Q6 Describe about Multilevel Databases and Web Query Systems.

Answer: Multilevel Databases: The main idea behind this approach is that the lowest level of the database contains semi-structured information stored in various Web repositories, such as hypertext documents. At the higher level(s) meta data or generalizations are extracted from lower levels and organized in structured collections, i.e. relational or object oriented databases. For example, Han, et. al. use a multilayered database where each layer is obtained via generalization and transformation operations performed on the lower layers. Kholsa, et. al. propose the creation and maintenance of meta-databases at each information providing domain and

Dear students get fully solved assignments

Send your semester & Specialization name to our mail id :

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