A Taxonomy of Dirty Time-Oriented Data

TitleA Taxonomy of Dirty Time-Oriented Data
Publication TypeConference Paper
Year of Publication2012
AuthorsGschwandtner, T., J. Gärtner, W. Aigner, and S. Miksch
EditorsQuirchmayr, G., J. Basl, I. You, L. Xu, and E. Weippl
Conference NameLecture Notes in Computer Science (LNCS 7465): Multidisciplinary Research and Practice for Information Systems (Proceedings of the CD-ARES 2012)
Pages58 -- 72
PublisherSpringer, Berlin / Heidelberg
Conference LocationPrague, Czech Republic
ISBN Number978-3-642-32497-0
Keywordsdata cleansing, data quality, dirty data, taxonomy, Time-Oriented Data

Data quality is a vital topic for business analytics in order to gain accurate insight and make correct decisions in many data-intensive industries. Albeit systematic approaches to categorize, detect, and avoid data quality problems exist, the special characteristics of time-oriented data are hardly considered. However, time is an important data dimension with distinct characteristics which a ffords special consideration in the context of dirty data. Building upon existing taxonomies of general data quality problems, we address `dirty' time-oriented data, i.e., time-oriented data with potential quality problems. In particular, we investigated empirically derived problems that emerge with di fferent types of time-oriented data (e.g., time points, time intervals) and provide various examples of quality problems of time-oriented data. By providing categorized information related to existing taxonomies, we establish a basis for further research in the field of dirty time-oriented data, and for the formulation of essential quality checks when preprocessing time-oriented data.



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