Alliance Rules for Data Warehouse Cleansing
Rajiv Arora, Payal Pahwa, Shubha Bansal
Abstract
Rajiv Arora, Payal Pahwa, Shubha Bansal
Abstract
Data cleansing is an activity performed on the data sets of data warehouse to enhance and maintain the quality and consistency of the data. This paper addresses the problems related with dirty data, entrance of dirty data and detection of dirty data in the data warehouse. The paper perceives the procedure of data cleansing from a different perspective. It provides an algorithm for the detection of errors and dirty data in the data sets of an already existing data warehouse. The paper characterizes the alliance rules based on the concept of mathematical association rules to determine the dirty and faulty data in data warehouse. The research marks the use of q-grams to determine the errors in a prominent way.
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Data cleansing is an activity performed on the data sets of data warehouse to enhance and maintain the quality and consistency of the data. This paper addresses the problems related with dirty data, entrance of dirty data and detection of dirty data in the data warehouse. The paper perceives the procedure of data cleansing from a different perspective. It provides an algorithm for the detection of errors and dirty data in the data sets of an already existing data warehouse. The paper characterizes the alliance rules based on the concept of mathematical association rules to determine the dirty and faulty data in data warehouse. The research marks the use of q-grams to determine the errors in a prominent way.
Key concepts: Data cleansing, Data warehouse, Computer science, Data mining, Data quality, Consistency (knowledge bases), Database, Data consistency