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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/4685

Title: Development of Algorithm to improve Data Quality at Extraction Transformation and Loading ETL Stage of Data Warehousing
Keywords: Data Warehouse (DW), Data Quality (DQ), Data Analysis, Extract, Transform and Load (ETL), Data Purgation (DP)
Researcher: JOLLY SAKSHI
Guide(s): DR. NEHA GUPTA
Registration Date: 26/09/2014
Abstract: A Data Warehouse, DW, is a collection of technologies that aimed at enabling the decision maker to make better and faster decisions. Due to rapid growth of wired and wireless networks, data quality has become challenging issue in Data Warehouse and Business Intelligence Solutions. Many data warehouse projects have been failed due to poor quality of the data. Data purgation is the process of improving the data quality during ETL process in data warehouse. Without a Data Purgation, DP, process, the Data Warehouses, DWHs, suffers from lack of data quality. During the ETL process data is extracted, transformed to match the data warehouse schematic and loaded into the data warehouse database. The quality of the data can be achieved by cleaning the dirty data, which is collected from the different heterogeneous data sources and then loaded into the data warehouse. Pre processing and cleansing of dirty data yields reliable decision making. The data quality helps in maintaining the accuracy, integrity, consistency, non redundancy and timely delivery of data. In this proposal, an algorithm will be developed to implement reliable data quality which may provide consistent data to be loaded into data warehouse. The experimental result will try to demonstrate the efficiency of algorithm, and to improve the data quality during the ETL process in data warehouse. Apart from achieving data quality, the algorithm will also try to identify cryptic values, dummy values and the contradicting data. newline
Language: English
Appears in Department:Department of Computer Applications

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