Analysis of Data Cleansing Approaches regarding Dirty Data A Comparative Study

Abstract

Data Cleansing is an activity involving a process of detecting and correcting the errors and inconsistencies in data warehouse. It deals with identification of corrupt and duplicate data inherent in the data sets of a data warehouse to enhance the quality of data. The research was directed at investigating some existing approaches and frameworks to data cleansing. That attempted to solve the data cleansing problem and came up with their strengths and weaknesses which led to the identification of gabs in those frameworks and approaches. A comparative analysis of the four frameworks was conducted and by using standard testing parameters a proposed feature was discussed to fit in the gaps.

“How Committed Are You to Your New Year Goals?”: A Quantitative Study on the Connection of Commitment and Performance with New Year Resolutions

“…The collected data were cleansed. Data cleansing involves detecting and correcting errors and inconsistencies in a data set (Sarpong & Arthur, 2013). Missing data could be an indication of a data entry error or could be the result of incomplete responses (Meyers et al, 2013).…”