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
Link To Document :
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