An MAS Negotiation Support Tool for Schema Matching

Database schema matching is the process of establishing correspondences between attributes of schemas for data integration purpose. Though various commercial tools have been developed, their results are inherently uncertain. In practice, to obtain correct at-tribute correspondences, there is a need for collecting human input, after the use of automatic matching tools, to reconcile erroneous mappings. We present a negotiation support tool that enables not a single expert but an expert team, whose members might have conﬂicting views, can work collaboratively to reconcile the output of the automatic tools. In an attempt to facilitate and support cooperation in team integration, our tool sets the goal to compute all possible decisions from expert inputs as well as explanations for each decision. Moreover, it also shows the foreseeable consequences of choosing a particular decision. Technically, this tool is developed on top of an argumentation framework.

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Karl Aberer, Hoai Xuan Luong, Zoltán Miklós, Quoc Viet Hung Nguyen
2013
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https://infoscience.epfl.ch/record/184822?ln=fr

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Database schema

The database schema is the structure of a database described in a formal language supported typically by a relational database management system (RDBMS). The term "schema" refers to the organization

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The terms schema matching and mapping are often used interchangeably for a database process. For this article, we differentiate the two as follows: schema matching is the process of identifying that

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vignette|Jacques Attali est l'expert le plus invité dans les médias français, il s'exprime sur les sujets d'ordre politique, économique, financier, technologique, médical, scientifique, littéraire ou

Pay-as-you-go Reconciliation in Schema Matching Networks

Karl Aberer, Zoltán Miklós, Quoc Viet Hung Nguyen, Thành Tâm Nguyên

Schema matching is the process of establishing correspondences between the attributes of database schemas for data integration purposes. Although several automatic schema matching tools have been developed, their results are often incomplete or erroneous. To obtain a correct set of correspondences, a human expert is usually required to validate the generated correspondences. We analyze this reconciliation process in a setting where a number of schemas needs to be matched, in the presence of consistency expectations about the network of attribute correspondences. We develop a probabilistic model that helps to identify the most uncertain correspondences, thus allowing us to guide the expert's work and collect his input about the most problematic cases. As the availability of such experts is often limited, we develop techniques that can construct a set of good quality correspondences with a high probability, even if the expert does not validate all the necessary correspondences. We demonstrate the efficacy of our techniques through extensive experimentation using real-world datasets.

2014

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Reconciling Matching Networks of Conceptual Models

Karl Aberer, Zoltán Miklós, Quoc Viet Hung Nguyen, Thành Tâm Nguyên

Conceptual models such as database schemas, ontologies or process models have been established as a means for effective engineering of information systems. Yet, for complex systems, conceptual models are created by a variety of stakeholders, which calls for techniques to manage consistency among the different views on a system. Techniques for model matching generate correspondences between the elements of conceptual models, thereby supporting effective model creation, utilization, and evolution. Although various automatic matching tools have been developed for different types of conceptual models, their results are often incomplete or erroneous. Automatically generated correspondences, therefore, need to be reconciled, i.e., validated by a human expert. We analyze the reconciliation process in a network setting, where a large number of conceptual models need to be matched. Then, the network induced by the generated correspondences shall meet consistency expectations in terms of mutual reinforcing relations between the correspondences. We develop a probabilistic model to identify the most uncertain correspondences in order to guide the expert's validation work. We also show how to construct a set of high-quality correspondences, even if the expert does not validate all generated correspondences. We demonstrate the efficiency of our techniques for real-world datasets in the domains of schema matching and ontology alignment.

2019

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