Abstract
Data warehouse is a collective entity of data from various data sources. Data are prone to several complications and irregularities in data warehouse. Data cleaning service is non trivial activity to ensure data quality. Data cleaning service involves identification of errors, removing them and improve the quality of data. One of the common methods is duplicate elimination. This research focuses on the service of duplicate elimination on local data. It initially surveys data quality focusing on quality problems, cleaning methodology, involved stages and services within data warehouse environment. It also provides a comparison through some experiments on local data with different cases, such as different spelling on different pronunciation, misspellings, name abbreviation, honorific prefixes, common nicknames, splitted name and exact match. All services are evaluated based on the proposed quality of service metrics such as performance, capability to process the number of records, platform support, data heterogeneity, and price; so that in the future these services are reliable to handle big data in data warehouse.
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