Mit401 data warehousing and data mining
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This document is a communication to students regarding assignments for the Master of Science in Information Technology program, specifically for semester 4, subject code MIT401. It outlines tasks related to data warehousing and data mining, providing various topics including OLTP versus data warehouses, the Kimball life cycle, and clustering methods like k-means. Students are instructed to submit their semester and specialization details to receive fully solved assignments via email or phone.
Mit401 data warehousing and data mining
- 1. Dear students get fully solved assignments Send your semester & Specialization name to our mail id : “ help.mbaassignments@gmail.com ” or Call us at : 08263069601 ASSIGNMENT PROGRAM Master of Science inInformation Technology(MScIT)RevisedFall 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 questionis followedby evaluationscheme. 1 Differentiate betweenOLTPand Data Warehouses. Answer : The data warehouse and the OLTP data base are both relational databases. However, the objectivesof boththese databasesare 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.Itismore astorehouse of currentand historical Q2 Explainthe Data Warehouse Kimball life cycle.
- 2. 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) projectteamsacrossvirtually 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 subsetof the available dimensions,asshowninthe followingillustration: Hypercubesandmulticubesdifferintermsof available Q.4 List and explainthe 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.Datareductioncanincrease storage efficiencyandreduce costs. Strategiesfor 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. Take advantage of existing knowledge on risk reduction before youworklike crazyto come up withstrategiesand Q.5 Describe K-meansmethodfor 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
- 3. clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.Thisresultsinapartitioningof the dataspace intoVoronoi cells. The problemiscomputationallydifficult(NP-hard); Q.6 Describe about Multilevel Databasesand WebQuery 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 informationsource,ratherthanrelyingonaglobal heterogeneousdatabase schema.The Dear students get fully solved assignments Send your semester & Specialization name to our mail id : “ help.mbaassignments@gmail.com ” or Call us at : 08263069601