Configuring Intelligent Mediators Using Ontologies

Bruno, Gennaro; Collet, Christine; Vargas-Solar, Genoveva

doi:10.1007/11896548_41

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Sybase allows access to external data stores, in Sybase called specialty data stores, and other types of database systems. ADEMS [21,32] proposes mediation cooperative components or services that can broker and integrate data coming from heterogeneous sources. The cooperative brokers allow to build an extensible data mediation system.…”

Section: Database Middlewarementioning

confidence: 99%

Big Data Management: What to Keep from the Past to Face Future Challenges?

2017

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The emergence of new hardware architectures, and the continuous production of data open new challenges for data management. It is no longer pertinent to reason with respect to a predefined set of resources (i.e., computing, storage and main memory). Instead, it is necessary to design data processing algorithms and processes considering unlimited resources via the ''pay-as-you-go'' model. According to this model, resources provision must consider the economic cost of the processes versus the use and parallel exploitation of available computing resources. In consequence, new methodologies, algorithms and tools for querying, deploying and programming data management functions have to be provided in scalable and elastic architectures that can cope with the characteristics of Big Data aware systems (intelligent systems, decision making, virtual environments, smart cities, drug personalization). These functions, must respect QoS properties (e.g., security, reliability, fault tolerance, dynamic evolution and adaptability) and behavior properties (e.g., transactional execution) according to application requirements. Mature and novel system architectures propose models and mechanisms for adding these properties to new efficient data management and processing functions delivered as services. This paper gives an overview of the different architectures in which efficient data management functions can be delivered for addressing Big Data processing challenges.

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Section: Database Middlewarementioning

confidence: 99%

Big Data Management: What to Keep from the Past to Face Future Challenges?

2017

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Dynamic Source Selection in Large Scale Mediation Systems

Pomares

Roncancio

Abásolo

et al.

Lecture Notes in Computer Science

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Knowledge Based Query Processing in Large Scale Virtual Organizations

Pomares

Roncancio

Abásolo

et al. 2009

Enterprise Information Systems

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International audienceThis 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

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Configuring Intelligent Mediators Using Ontologies

Cited by 3 publications

References 10 publications

Big Data Management: What to Keep from the Past to Face Future Challenges?

Big Data Management: What to Keep from the Past to Face Future Challenges?

Dynamic Source Selection in Large Scale Mediation Systems

Knowledge Based Query Processing in Large Scale Virtual Organizations

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