Affiliations: [a] Ministry of Justice, WODC, PO Box 20301, 2500 EH, The
Hague, The Netherlands | City University of New York, College of Staten Island | University of Massachusetts, Amherst | Universidad Autónoma del Estado de México | University of Southern California – Information
Science Institute
Abstract: For many standard as well as emerging criminal law Web 2.0
applications, such as the development of mashups and dataspace systems, privacy
preserving data integration is of crucial importance. In many organizations
different databases contain different kinds 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, we identify a common set of attributes with regard to the data
sources. A similarity function is associated to each attribute, which takes a
record from each data source as input and computes a similarity value as output
expressing how "similar" the records are. Depending on the similarity value,
we decide whether or not to reconcile two entities. We illustrate the
effectiveness of our approach by means of a real-life case in the field of
police and justice. Our approach can be applied to support the development of a
wide variety of criminal law applications, such as data warehouses, mashups,
and dataspace systems.
Keywords: criminal law, data integration, data reconciliation, similarity measure, privacy preserving, mashups
