Query performance evaluation of an architecture for fine-grained integration of heterogeneous grid data sources

Zamboulis, Lucas; Martin, Nigel; Poulovassilis, Alexandra

doi:10.1016/j.future.2010.05.008

Cited by 3 publications

(1 citation statement)

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“…The increasing number of distributed, autonomous and heterogeneous data sources, (ie: XML documents, relational database and HTML pages among others), has motivated the need for data integration systems, allowing users to query those sources transparently (Roth and Skritek., 2013;Pires et al, 2012). In this sense, dynamic data integration systems, as Peer Data Management Systems (PDMS) (Halevy et al, 2006), Grid (Zamboulis et al, 2010) or systems based on a pay-as-you-go strategy (Halevy et al, 2008), have been used to improve the data sharing of data sources distributed on the Web or on the cloud (Wall and Angryk, 2011). Some of these systems have a dynamic behaviour, i.e., their data sources have autonomy to join/leave the system and to change their own schemas.…”

Section: Introductionmentioning

confidence: 99%

A Proposal to Maintain the Semantic Balance in Cluster-based Data Integration Systems

Silva

Lóscio

Salgado

2014

Proceedings of the 16th International Conference on Enterprise Information Systems

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With the large volume of data sources on the Web, we need a system that integrates them, so that the user can query them transparently. For efficiency in queries, integration systems can group these sources in clusters according to the semantic similarity of their schemas. However, the sources have autonomy to evolve their schema, and to join or to leave the integration system at any time. This autonomy may cause a problem which we define as semantic unbalance of clusters. The semantic unbalance can compromise the formation of clusters and hence the efficiency of the submitted queries. In this paper, we propose a solution to the semantic balance of clusters in dynamic data integration systems based on self-organization. We also introduce a measure to evaluate how much the clusters are semantically unbalanced.

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Section: Introductionmentioning

confidence: 99%