• Corpus ID: 17241706

A Collective, Probabilistic Approach to Schema Mapping: Appendix

@article{Kimmig2017ACP,
  title={A Collective, Probabilistic Approach to Schema Mapping: Appendix},
  author={Angelika Kimmig and Alex Memory and Ren{\'e}e J. Miller and Lise Getoor},
  journal={ArXiv},
  year={2017},
  volume={abs/1702.03447}
}
In this appendix we provide additional supplementary material to "A Collective, Probabilistic Approach to Schema Mapping." We include an additional extended example, supplementary experiment details, and proof for the complexity result stated in the main paper. 

Figures from this paper

A Collective, Probabilistic Approach to Schema Mapping

Collective Mapping Discovery (CMD), the solution to the problem of mapping selection using state-of- the-art probabilistic reasoning techniques, which allows for inconsistencies and incompleteness.

References

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A Collective, Probabilistic Approach to Schema Mapping

Collective Mapping Discovery (CMD), the solution to the problem of mapping selection using state-of- the-art probabilistic reasoning techniques, which allows for inconsistencies and incompleteness.

Clio: Schema Mapping Creation and Data Exchange

This chapter presents algorithms for both schema mapping creation via query discovery, and for query generation for data exchange that can be used in pure relational, pure XML, nested relational, or mixed relational and nested contexts.

The iBench Integration Metadata Generator

iBench is the first metadata generator that can be used to evaluate a wide-range of integration tasks ( data exchange, mapping creation, mapping composition, schema evolution, among many others) and is believed to raise the bar for empirical evaluation and comparison of data integration systems.

STBenchmark: towards a benchmark for mapping systems

STBenchmark, a solution towards a much needed benchmark for mapping systems, is presented and a basic suite of mapping scenarios that should be readily supported by any mapping system is described.