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.

1 Differentiate between OLTP and Data Warehouses.

Answer : The data warehouse and the OLTP data base are both relational databases. However, the objectives of both these databases are different.

The OLTP database records transactions in real time and aims to automate clerical data entry processes of a business entity. Addition, modification and deletion of data in the OLTP database is essential and the semantics of the application used in the front end impact on the organization of the data in the database.

The data warehouse on the other hand does not cater to real time operational requirements of the enterprise. It is more a storehouse of current and historical

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

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.

In the multicube model, data is segmented into a set of smaller cubes, each of which is composed of a subset of the available dimensions, as shown in the following illustration:

Hypercubes and multicubes differ in terms of available

Q.4 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:

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Q.5 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.

The problem is computationally difficult (NP-hard);

Q.6 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 the use of a global schema for the meta-database. King & Novak propose the incremental integration of a portion of the schema from each information source, rather than relying on a global heterogeneous database schema. The

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