Title
Towards privacy preserving data reconciliation for criminal justice chains
Abstract
In many organizations different databases contain different kind of data concerning the same entity. This may have several good reasons. However, to have an integral and unified view of an entity, data reconciliation is of crucial importance. In this paper, we present an approach for data reconciliation that is based on available schemata of data sources and the content of the sources. The different schemata of data sources are used to determine what parts of the schemata pertain to the same entity type. The content of the sources is used to determine the association between different attributes stored in different sources. In establishing the relationships between different attributes, we have exploited the knowledge of domain experts as well. On the basis of the collected information with regard to a set of attributes, we assign a similarity measure to these attributes. Once we have identified the set of attributes that is similar, we reconcile two entities on the basis of the similarity measure. We illustrate the effectiveness of our approach by means of a real-life case in the field of police and justice.
Year
Venue
Keywords
2009
DG.O
data reconciliation,criminal justice chain,different schema,organizations different databases,entity type,different source,different kind,similarity measure,towards privacy,schemata pertain,data source,different attribute,criminal justice
Field
DocType
Citations 
Information retrieval,Similarity measure,Computer science,Knowledge management,Criminal justice,Schema (psychology)
Conference
4
PageRank 
References 
Authors
0.97
6
2
Name
Order
Citations
PageRank
Sunil Choenni1309111.82
Jan van Dijk235227.66