A Taxonomy of Dirty Time-Oriented Data

  • Theresia Gschwandtner
  • Johannes Gärtner
  • Wolfgang Aigner
  • Silvia Miksch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7465)


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 


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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Theresia Gschwandtner
    • 1
  • Johannes Gärtner
    • 2
  • Wolfgang Aigner
    • 1
  • Silvia Miksch
    • 1
  1. 1.Institute of Software Technology and Interactive Systems (ISIS)Vienna University of TechnologyViennaAustria
  2. 2.XIMES GmbHViennaAustria

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