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