Improving Data Quality Through Effective Use of Data Semantics

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/3861

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DC Field	Value	Language
dc.creator	Madnick, Stuart E.	-
dc.date	2003-12-13T19:23:34Z	-
dc.date	2003-12-13T19:23:34Z	-
dc.date	2004-01	-
dc.date.accessioned	2013-10-09T02:32:51Z	-
dc.date.available	2013-10-09T02:32:51Z	-
dc.date.issued	2013-10-09	-
dc.identifier	http://hdl.handle.net/1721.1/3861	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Data quality issues have taken on increasing importance in recent years. In our research, we have discovered that many “data quality” problems are actually “data misinterpretation” problems – that is, problems with data semantics. In this paper, we first illustrate some examples of these problems and then introduce a particular semantic problem that we call “corporate householding.” We stress the importance of “context” to get the appropriate answer for each task. Then we propose an approach to handle these tasks using extensions to the COntext INterchange (COIN) technology for knowledge storage and knowledge processing.	-
dc.description	Singapore-MIT Alliance (SMA)	-
dc.format	227013 bytes	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	Computer Science (CS);	-
dc.subject	data quality	-
dc.subject	data semantics	-
dc.subject	corporate householding	-
dc.subject	COntext INterchange	-
dc.subject	knowledge management.	-
dc.title	Improving Data Quality Through Effective Use of Data Semantics	-
dc.type	Article	-
Appears in Collections:	MIT Items

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