Abstract
This paper concerns querying in large scale virtual organizations. Such organizations are characterized by a challenging data context involving a large number of distributed data sources with strong heterogeneity and uncontrolled data overlapping. In that context, data source selection during query evaluation is particularly important and complex. To cope with this task, we propose OptiSource, an original strategy for source selection using combinatorial optimization techniques combined to organizational knowledge of the virtual organization. Experiment numerical results show that OptiSource is a robust strategy that improves the precision and the recall of the source selection process. This paper presents the data and knowledge models, the definition of OptiSource, the related mathematical model, the prototype and an extensive experimental study.
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Pomares, A., Roncancio, C., Cung, VD., Villamil, MdP. (2011). Improving Source Selection in Large Scale Mediation Systems through Combinatorial Optimization Techniques. In: Hameurlain, A., Küng, J., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems III. Lecture Notes in Computer Science, vol 6790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23074-5_6
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DOI: https://doi.org/10.1007/978-3-642-23074-5_6
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