Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Investigative Data Mining for Security and Criminal Detection
Investigative Data Mining for Security and Criminal Detection
Design and Implementation of a Genetic-Based Algorithm for Data Mining
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fundamentals of Database Systems, Fourth Edition
Fundamentals of Database Systems, Fourth Edition
Building the Data Warehouse
Enterprise information mashups: integrating information, simply
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Intel Mash Maker: join the web
ACM SIGMOD Record
A citizen privacy protection model for e-government mashup services
dg.o '08 Proceedings of the 2008 international conference on Digital government research
Towards privacy preserving data reconciliation for criminal justice chains
Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government
Public safety mashups to support policy makers
EGOVIS'10 Proceedings of the First international conference on Electronic government and the information systems perspective
The "dark side" of information technology: a survey of IT-related complaints from citizens
Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times
Proceedings of the 13th Annual International Conference on Digital Government Research
A case study for integrating public safety data using semantic technologies
Information Polity - Special issue on Public Engagement and Government Collaboration: Theories, Strategies and Case Studies
Exploring process barriers to release public sector information in local government
Proceedings of the 6th International Conference on Theory and Practice of Electronic Governance
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There is a growing demand from different groups of users to make crime statistics accessible online for several good reasons: with online statistics, combining and analyzing data will become much easier and may give a better insight into certain phenomena. However, applying data mining or Web 2.0 technology, such as mash ups on crime data online, confronts us with undesired effects. These undesired effects are an increase of chances to misinterpret results with regard to crime and law enforcement, violation of the privacy law, and disclosure of the identity of groups of people. Moreover, bad management of crime statistics online may also lead to undesired effects such as breakpoints in series. In this paper, we focus on the potentials and undesired effects entailed by making crime statistics accessible on the web. In addition, we discuss two approaches - a data warehouse approach and a data space approach - to implement crime statistics online as such that the undesired effects mentioned above are minimized. We argue that a data space approach is better suited for the implementation of crime statistics online, since in this approach (highly) aggregated data are used instead of micro data and therefore the risk of violation of privacy is minimized. Moreover, in the data space approach relationships between databases are stored, which minimizes the chance of undesired effects.