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T. Gschwandtner, J. Gärtner, W. Aigner, S. Miksch:
"A Taxonomy of Dirty Time-Oriented Data";
Talk: International Cross Domain Conference and Workshop on Availability, Reliability and Security (CD-ARES 2012), Prag; 2012-08-20 - 2012-08-24; in: "Lecture Notes in Computer Science (LNCS 7465): Multidisciplinary Research and Practice for Information Systems (Proceedings of the CD-ARES 2012)", Lecture Notes in Computer Science (LNCS) / Springer Berlin / Heidelberg, 7465 (2012), ISBN: 978-3-642-32497-0; 58 - 72.

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 affords 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 different 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.

dirty data, time-oriented data, data cleansing, data quality, taxonomy

http://dx.doi.org/10.1007/978-3-642-32498-7_5

http://publik.tuwien.ac.at/files/PubDat_209199.pdf

Project Head Silvia Miksch:
CVAST: Centre for Visual Analytics Science and Technology (Laura Bassi Centre of Expertise)