José Abásolo’s research while affiliated with Los Andes University (Colombia) and other places

This paper presents OptiSource, a novel approach of source selection that reduces the number of data sources accessed during query evaluation in complex large scale distributed data contexts in virtual organizations (VO). In these contexts autonomous organizations share data about a group of domain concepts (e.g. patient, client, gene). The instances of such concepts are constructed from non-disjointed fragments provided by several local data sources. This fact, in addition to the absence of reliable statistics on source contents and the large number of sources, make current proposals unsuitable in terms of response quality and/or response time. OptiSource optimizes data source selection in query evaluation using a combinatorial optimization model to distinguish the sets of sources that maximize benefits and minimize the number of sources to contact to while satisfying resource constraints. The precision and recall of source selection during query planning is highly improved as demonstrated by the tests performed with the OptiSource prototype. Furthermore, tests with the optimization model confirmed that the approach can handle different levels of precision on the benefit prediction.

This paper presents OptiSource, a novel approach of source selection that reduces the number of data sources accessed during query evaluation in large scale distributed data contexts. These contexts are typical of large scale Virtual Organizations (VO) where autonomous organizations share data about a group of domain concepts (e.g. patient, gene). The instances of such concepts are constructed from nondisjointed fragments provided by several local data sources. Such sources overlap in a non mastered way making data location uncertain. This fact, in addition to the absence of reliable statistics on source contents and the large number of sources, make current proposals unsuitable in terms of response quality and/or response time. OptiSource optimizes source selection by taking advantage of organizational aspects of VOs to predict the benefit of using a source. It uses an optimization model to distinguish the sets of sources that maximize benefits and minimize the number of sources to contact to while satisfying resource constraints. The precision and recall of source selection is highly improved as demonstrated by the tests performed with the OptiSource prototype.

This paper proposes to apply Multiple Kernel Learning and Indefinite Kernels (IK) to combine and tune Similarity Measures within the context of Ontology Instance Matching. We explain why MKL can be used in parameter selection and similarity measure combination; argue that IK theory is required in order to use MKL within this context; propose a configuration that makes use of both concepts; and present, using the IIMB bechmark, results of a prototype to show the feasibility of this idea in comparison with other matching tools.

This work concerns query processing to support data sharing in large scale Virtual Organizations(VO). Characterization of VO’s data sharing contexts reflects the coexistence of factors like sources overlapping, uncertain data location, and fuzzy copies in dynamic large scale environments that hinder query processing. Existing results on distributed query evaluation are useful for VOs, but there is no appropriate solution combining high semantic level and dynamic large scale environments required by VOs. This paper proposes a characterization of VOs data sources, called Data Profile, and a query processing strategy (called QPro2e) for large scale VOs with complex data profiles. QPro2e uses an evolving distributed knowledge base describing data sources roles w.r.t shared domain concepts. It allows the identification of logical data source clusters which improve query evaluation in presence of a very large number of data sources.

This paper proposes ORS, an original strategy to reduce the number of data sources to access during query evaluation in large scale mediation systems. ORS proceeds first selecting sources using extensional (data) information to discard useless sources and then validates the intentional (schema) information that each one is able to provide. The first step is based on location queries on some ”well chosen” data sources, that previously had made a consolidation integration effort. This paper proposes ORS to improve querying semantic virtual objects whose instances are distributed across numerous data sources. Cost analysis and implementation in a grid context are also presented.

This paper presents an approach of data integration in Health Virtual Organization (HVO). It targets large scale contexts where high distribution and autonomy of sources produce complex integration scenarios. The principle is to provide a high conceptual level composed of Virtual Data Objects (VDO) that can be queried independently of the data sources that are behind them and without requiring technical skills. The approach uses a mediation architecture improved to be viable in large scale contexts through the concept of query cartography and applying a semantic caching strategy specific for VDOs that reduces network latency.