• DocumentCode
    2496997
  • Title

    E-research event data quality

  • Author

    Weisi Chen

  • Author_Institution
    Univ. of New South Wales, Sydney, NSW, Australia
  • fYear
    2013
  • fDate
    8-12 April 2013
  • Firstpage
    314
  • Lastpage
    317
  • Abstract
    One of the most important data types e-Researchers use to conduct analysis processes is “event data”, which records information of some timed events in a particular domain. However, real-world event data is usually of poor quality, resulting in large amounts of money and labour to tackle the ensuing problems. Existing solutions to event data quality are very limited, mostly supporting merely data quality in general without facilitating the ease of event pattern detection; existing event processing systems, on the other hand, are very inefficient in dealing with data quality issues. In this research, we have summarised the criteria to address event data quality issues and compared possible solutions including knowledge-based systems and event processing systems. We conclude by proposing an approach that combines a rule-based system with an event processing system in a novel way.
  • Keywords
    data analysis; knowledge based systems; pattern recognition; analysis process; e-research event data quality; event pattern detection; event processing system; knowledge-based system; rule-based system; Boolean functions; Data structures; Databases; Knowledge based systems; Pattern matching; Prototypes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Engineering Workshops (ICDEW), 2013 IEEE 29th International Conference on
  • Conference_Location
    Brisbane, QLD
  • Print_ISBN
    978-1-4673-5303-8
  • Electronic_ISBN
    978-1-4673-5302-1
  • Type

    conf

  • DOI
    10.1109/ICDEW.2013.6547472
  • Filename
    6547472