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Improving Data Quality Through Effective Use of Data Semantics

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dc.contributor.author Madnick, Stuart E.
dc.date.accessioned 2003-12-13T19:23:34Z
dc.date.available 2003-12-13T19:23:34Z
dc.date.issued 2004-01
dc.identifier.uri http://hdl.handle.net/1721.1/3861
dc.description.abstract 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. en
dc.description.sponsorship Singapore-MIT Alliance (SMA) en
dc.format.extent 227013 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartofseries Computer Science (CS);
dc.subject data quality en
dc.subject data semantics en
dc.subject corporate householding en
dc.subject COntext INterchange en
dc.subject knowledge management. en
dc.title Improving Data Quality Through Effective Use of Data Semantics en
dc.type Article en


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