TimeCleanser: A Visual Analytics Approach for Data Cleansing of Time-Oriented Data
Poor data quality leads to unreliable results of any kind of data processing and has profound economic impact. Although there are tools to help users with the task of data cleansing, support for dealing with the specifics of time-oriented data is rather poor. However, the time dimension has very specific characteristics which introduce quality problems, that are different from other kinds of data. We present TimeCleanser, an interactive Visual Analytics system to support the task of data cleansing of ime-oriented data. In order to help the user to deal with these special characteristics and quality problems, TimeCleanser combines semi-automatic quality checks, visualizations, and directly editable data tables. The evaluation of the TimeCleanser system within a focus group (two target users, one developer, and two Human Computer Interaction experts) shows that (a) our proposed method is suited to detect hidden quality problems of time-oriented data and (b) that it facilitates the complex task of data cleansing.
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14th International Conference on Knowledge Technologies and Data-driven Business (i-KNOW 2014)
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