Schema matching

Résumé

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 two objects are semantically related (scope of this article) while mapping refers to the transformations between the objects. For example, in the two schemas DB1.Student (Name, SSN, Level, Major, Marks) and DB2.Grad-Student (Name, ID, Major, Grades); possible matches would be: DB1.Student ≈ DB2.Grad-Student; DB1.SSN = DB2.ID etc. and possible transformations or mappings would be: DB1.Marks to DB2.Grades (100-90 A; 90-80 B: etc.). Automating these two approaches has been one of the fundamental tasks of data integration. In general, it is not possible to determine fully automatically the different correspondences between two schemas — primarily because of the differing and often not explicated or documented semantics of the two schemas. Impediments Among others, common challenges

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https://en.wikipedia.org/wiki/Schema_matching

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Collaborative Schema Matching Reconciliation

Karl Aberer, Hoai Xuan Luong, Zoltán Miklós, Quoc Viet Hung Nguyen

Schema matching is the process of establishing correspondences between the attributes of database schemas for data integration purpose. Although several schema matching tools have been developed, their results are often incomplete or erroneous. To obtain correct attribute correspondences, in practice, human experts edit the mapping results and fix the mapping problems. As the scale and complexity of data integration tasks have increased dramatically in recent years, the reconciliation phase becomes more and more a bottleneck. Moreover, one often needs to establish the correspondences in not only between two but a network of schemas simultaneously. In such reconciliation settings, it is desirable to involve several experts. In this paper, we propose a tool that supports a group of experts to collaboratively reconcile a set of matched correspondences. The experts might have conflicting views whether a given correspondence is correct or not. As one expects global consistency conditions in the network, the conflict resolution might require discussion and negotiation among the experts to resolve such disagreements. We have developed techniques and a tool that allow approaching this reconciliation phase in a systematic way. We represent the expert's views as arguments to enable formal reasoning on the assertions of the experts. We detect complex dependencies in their arguments, guide and present them the possible consequences of their decisions. These techniques thus can greatly help them to overlook the complex cases and work more effectively.

2013

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, , ,

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.

2013

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Team Integration

Son Thanh Do

We leverage theoretical advances and the multi-user nature of \emph{argumentation}. The overall contributions of our work are as follows. We model the schema matching network and the reconciliation process, where we relate the experts' assertions and the constraints of the matching network to an \emph{argumentation framework}. Our representation not only captures the experts' belief and their explanations, but also enables to reason about these captured inputs. On top of this representation, we develop support techniques for experts to detect conflicts in a set of their assertions. Then we guide the conflict resolution by offering two primitives: \emph{conflict-structure interpretation} and \emph{what-if analysis}. While the former presents meaningful interpretations for the conflicts and various heuristic metrics, the latter can greatly help the experts to understand the consequences of their own decisions as well as those of others. Last but not least, we implement an argumentation-based negotiation support tool for schema matching (ArgSM), which realizes our methods to help the experts in the collaborative task.

2015

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