Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Availability, Reliability, and Security

CD-ARES 2012: Multidisciplinary Research and Practice for Information Systems pp 58–72Cite as

  1. Home
  2. Multidisciplinary Research and Practice for Information Systems
  3. Conference paper
A Taxonomy of Dirty Time-Oriented Data

A Taxonomy of Dirty Time-Oriented Data

  • Theresia Gschwandtner21,
  • Johannes Gärtner22,
  • Wolfgang Aigner21 &
  • …
  • Silvia Miksch21 
  • Conference paper
  • 2537 Accesses

  • 32 Citations

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7465)

Abstract

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.

Keywords

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

Download conference paper PDF

References

  1. Rahm, E., Do, H.H.: Data Cleaning: Problems and Current Approaches. IEEE Techn. Bulletin on Data Engineering 31 (2000)

    Google Scholar 

  2. Kim, W., Choi, B.-J., Hong, E.-K., Kim, S.-K., Lee, D.: A Taxonomy of Dirty Data. Data Mining and Knowledge Discovery 7, 81–99 (2003)

    CrossRef  MathSciNet  Google Scholar 

  3. Müller, H., Freytag, J.-C.: Problems, Methods, and Challenges in Comprehensive Data Cleansing. Technical report HUB-IB-164, Humboldt University Berlin (2003)

    Google Scholar 

  4. Oliveira, P., Rodrigues, F., Henriques, P.: A Formal Definition of Data Quality Problems. In: International Conference on Information Quality (MIT IQ Conference) (2005)

    Google Scholar 

  5. Barateiro, J., Galhardas, H.: A Survey of Data Quality Tools. Datenbankspektrum 14, 15–21 (2005)

    Google Scholar 

  6. Sadiq, S., Yeganeh, N., Indulska, M.: 20 Years of Data Quality Research: Themes, Trends and Synergies. In: 22nd Australasian Database Conference (ADC 2011), pp. 1–10. Australian Computer Society, Sydney (2011)

    Google Scholar 

  7. Madnick, S., Wang, R., Lee, Y., Zhu, H.: Overview and Framework for Data and Information Quality Research. Journal of Data and Information Quality (JDIQ) 1(1), 1–22 (2009)

    Google Scholar 

  8. Neely, M., Cook, J.: A Framework for Classification of the Data and Information Quality Literature and Preliminary Results (1996-2007). In: 14th Americas Conference on Information Systems 2008 (AMICS 2008), pp. 1–14 (2008)

    Google Scholar 

  9. Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Springer, London (2011)

    CrossRef  Google Scholar 

  10. Andrienko, N., Andrienko, G.: Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach. Springer, Berlin (2006)

    MATH  Google Scholar 

  11. Shneiderman, B.: The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In: IEEE Symposium on Visual Languages, pp. 336–343. IEEE Computer Society Press (1996)

    Google Scholar 

  12. Allen, J.: Towards a General Model of Action and Time. Artificial Intelligence 23(2), 123–154 (1984)

    CrossRef  MATH  Google Scholar 

  13. XIMES GmbH: Time Intelligence Solutions – [TIS], http://www.ximes.com/en/software/products/tis (accessed March 30, 2012)

  14. XIMES GmbH: Qmetrix, http://www.ximes.com/en/ximes/qmetrix/background.php (accessed March 30, 2012)

  15. Microsoft: Excel, http://office.microsoft.com/en-us/excel/ (accessed March 30, 2012)

  16. Corbin, J., Strauss, A.: Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory, 3rd edn. Sage Publications, Los Angeles (2008)

    Google Scholar 

  17. Card, S., Mackinlay, J., Shneiderman, B.: Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  18. Raman, V., Hellerstein, J.: Potter’s Wheel: An Interactive Data Cleaning System. In: 27th International Conference on Very Large Data Bases (VLDB 2001), pp. 381–390. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  19. Kandel, S., Paepcke, A., Hellerstein, J., Heer, J.: Wrangler: Interactive Visual Specification of Data Transformation Scripts. In: ACM Human Factors in Computing Systems (CHI 2011), pp. 3363–3372. ACM, New York (2011)

    Google Scholar 

  20. Huynh, D., Mazzocchi, S.: Google Refine, http://code.google.com/p/google-refine (accessed March 30, 2012)

Download references

Author information

Authors and Affiliations

  1. Institute of Software Technology and Interactive Systems (ISIS), Vienna University of Technology, Favoritenstrasse 9-11/188, A-1040, Vienna, Austria

    Theresia Gschwandtner, Wolfgang Aigner & Silvia Miksch

  2. XIMES GmbH, Hollandstraße 12/12, A-1020, Vienna, Austria

    Johannes Gärtner

Authors
  1. Theresia Gschwandtner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Johannes Gärtner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wolfgang Aigner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Silvia Miksch
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of IT, Engineering and Environment, University of South Australia, Mawson Lakes Campus, 5001, Adelaide, SA, Australia

    Gerald Quirchmayr

  2. Department of Information Technologies, University of Economics, W. Churchill Sq. 4, 130 67, Prague 3, Czech Republic

    Josef Basl

  3. School of Information Science, Korean Bible University, 16 Danghyun 2-gil, Nowon-gu, 139-791, Seoul, Korea

    Ilsun You

  4. Information Technology and Decision Sciences, Old Dominion University, 2076 Constant Hall, 23529, Norfolk, VA, USA

    Lida Xu

  5. Institute of Software Technology and Interactive Systems, Vienna University of Technology and SBA Research, Favoritenstrsse 9-11, 1040, Vienna, Austria

    Edgar Weippl

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 IFIP International Federation for Information Processing

About this paper

Cite this paper

Gschwandtner, T., Gärtner, J., Aigner, W., Miksch, S. (2012). A Taxonomy of Dirty Time-Oriented Data. In: Quirchmayr, G., Basl, J., You, I., Xu, L., Weippl, E. (eds) Multidisciplinary Research and Practice for Information Systems. CD-ARES 2012. Lecture Notes in Computer Science, vol 7465. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32498-7_5

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-32498-7_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32497-0

  • Online ISBN: 978-3-642-32498-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature