Temporal reasoning based on semi-intervals
Artificial Intelligence
The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A framework for testing database applications
Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis
Maintaining knowledge about temporal intervals
Communications of the ACM
A Framework for Generating Network-Based Moving Objects
Geoinformatica
On the Generation of Time-Evolving Regional Data
Geoinformatica
Data Mining and Knowledge Discovery
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Simple and realistic data generation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A parallel general-purpose synthetic data generator
ACM SIGMOD Record
On generalizing orientation information in OPRAm
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
An Introduction to Duplicate Detection
An Introduction to Duplicate Detection
Editorial: BeAware!-Situation awareness, the ontology-driven way
Data & Knowledge Engineering
Towards duplicate detection for situation awareness based on spatio-temporal relations
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems: Part II
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Benchmarking the quality of duplicate detection methods requires comprehensive knowledge on duplicate pairs in addition to sufficient size and variability of test data sets. While extending real-world data sets with artificially created data is promising, current approaches to such synthetic data generation, however, work solely on a quantitative level, which entails that duplicate semantics are only implicitly represented, leading to only insufficiently configurable variability. In this paper we propose SemGen, a semantics-driven approach to synthetic data generation. SemGen first diversifies real-world objects on a qualitative level, before in a second step quantitative values are generated. To demonstrate the applicability of SemGen, we propose how to define duplicate semantics for the domain of road traffic management. A discussion of lessons learned concludes the paper.