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Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
ISBN: 978-1-4244-5445-7
pp: 1137-1140
Mohamed A. Soliman , School of Computer Science, University of Waterloo, Canada
Mina Saleeb , School of Computer Science, University of Waterloo, Canada
Ihab F. Ilyas , School of Computer Science, University of Waterloo, Canada
ABSTRACT
Mashups are situational applications that build data flows to link the contents of multiple Web sources. Often times, ranking the results of a mashup is handled in a materialize-then-sort fashion, since combining multiple data sources usually destroys their original rankings. Moreover, although uncertainty is ubiquitous on the Web, most mashup tools do not reason about or reflect such uncertainty. We introduce MashRank, a mashup tool that treats ranking as a first-class citizen in mashup construction, and allows for rank-joining Web sources with uncertain information. To the best of our knowledge, no current tools allow for similar functionalities. MashRank encapsulates a new probabilistic model reflecting uncertainty in ranking, a set of techniques implemented as pipelined operators in mashup plans, and a probabilistic ranking infrastructure based on Monte-Carlo sampling.
CITATION
Mohamed A. Soliman, Mina Saleeb, Ihab F. Ilyas, "MashRank: Towards uncertainty-aware and rank-aware mashups", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 1137-1140, doi:10.1109/ICDE.2010.5447757
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