Quality-driven Integration of Heterogenous Information Systems
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Using Probabilistic Information in Data Integration
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Mapping PDB chains to UniProtKB entries
Bioinformatics
Query planning in the presence of overlapping sources
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Using medians to generate consensus rankings for biological data
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
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Data integration projects in the life sciences often gather data on a particular subject from multiple sources. Some of these sources overlap to a certain degree. Therefore, integrated search results may be supported by one, few, or all data sources. To reflect these differences, results should be ranked according to the number of data sources that support them. How such a ranking should look like is not clear per se. Either, results supported by only few sources are ranked high because this information is potentially new, or such results are ranked low because the strength of evidence supporting them is limited. We present two scoring schemes to rank search results in the integrated protein annotation database Columba. We define a surprisingness score, preferring results supported by few sources, and a confidence score, preferring frequently encountered information. Unlike many other scoring schemes our proposal is purely data-driven and does not require users to specify preferences among sources. Both scores take the concrete overlaps of data sources into account and do not presume statistical independence. We show how our schemes have been implemented efficiently using SQL.