Muse: Mapping Understanding and deSign by Example

Alexe, Bogdan; Chiticariu, Laura; Miller, Renée J.; Tan, Wang-Chiew

doi:10.1109/icde.2008.4497409

Cited by 59 publications

(56 citation statements)

References 20 publications

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“…We hope that incomplete mappings can help them in understanding the schemas and data better, and that the mappings can be refined over time as need arises, for example, as new data appears, or the application needs change. This particular aspect of our approach was explored in more detail in our work on data-driven mapping refinement [51] and in work on mapping debugging [3], but will not be emphasized in this chapter. This ability to evolve mappings incrementally has more recently been coined pay-as-you-go [23].…”

Section: Schema Mappingmentioning

confidence: 99%

“…An et al [5] consider how to use conceptual schemas to further automate mapping discovery. Yan et al [51] and Alexe et al [3] consider how to use data examples to help a user interactively design and refine mappings for relational and nested schemas respectively. Hernández et al [29] consider the generation of mappings that use data-metadata translations [50].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Clio: Schema Mapping Creation and Data Exchange

Fagin¹,

Haas²,

Hernández³

et al. 2009

Conceptual Modeling: Foundations and Applications

165

118

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Abstract.The Clio project provides tools that vastly simplify information integration. Information integration requires data conversions to bring data in different representations into a common form. Key contributions of Clio are the definition of non-procedural schema mappings to describe the relationship between data in heterogeneous schemas, a new paradigm in which we view the mapping creation process as one of query discovery, and algorithms for automatically generating queries for data transformation from the mappings. Clio provides algorithms to address the needs of two major information integration problems, namely, data integration and data exchange. In this chapter, we present our algorithms for both schema mapping creation via query discovery, and for query generation for data exchange. These algorithms can be used in pure relational, pure XML, nested relational, or mixed relational and nested contexts.

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Section: Schema Mappingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Clio: Schema Mapping Creation and Data Exchange

Fagin¹,

Haas²,

Hernández³

et al. 2009

Conceptual Modeling: Foundations and Applications

165

118

View full text Add to dashboard Cite

show abstract

“…Our proposal for designing and refining schema mappings is inspired to some extent by the work by Yan et al [57] and Alexe et al [3], in that we use information provided by the source schemas. However, their approach is different from ours.…”

Section: Designing and Refining Schema Mappingsmentioning

confidence: 99%

“…In particular, Amalgam is composed of 4 databases that were developed independently to cater for bibliographical information [45]. Finally, the two datasets, Mondial and Amalgam, are widely used in schema mappings literature for testing and validation purposes, e.g., [3], [9].…”

Section: Cardinal Annotationsmentioning

confidence: 99%

Incrementally improving dataspaces based on user feedback

Belhajjame

Paton

Embury

et al. 2013

Information Systems

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One aspect of the vision of dataspaces has been articulated as providing various benefits of classical data integration with reduced up-front costs. In this paper, we present techniques that aim to support schema mapping specification through interaction with end users in a pay-as-you-go fashion. In particular, we show how schema mappings, that are obtained automatically using existing matching and mapping generation techniques, can be annotated with metrics estimating their fitness to user requirements using feedback on query results obtained from end users.Using the annotations computed on the basis of user feedback, and given user requirements in terms of precision and recall, we present a method for selecting the set of mappings that produce results meeting the stated requirements. In doing so, we cast mapping selection as an optimization problem. Feedback may reveal that the quality of schema mappings is poor. We show how mapping annotations can be used to support the derivation of better quality mappings from existing mappings through refinement. An evolutionary algorithm is used to efficiently and effectively explore the large space of mappings that can be obtained through refinement.User feedback can also be used to annotate the results of the queries that the user poses against an integration schema. We show how estimates for precision and recall can be computed for such queries. We also investigate the problem of propagating feedback about the results of (integration) queries down to the mappings used to populate the base relations in the integration schema.

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“…Kimmig Metadata in the form of query logs has been used to select mappings that are most consistent with frequently asked queries [4]. Many different approaches use data to refine a mapping or to select a mapping from among a set of schema mappings [5], [6], [7], [8], [9], [10], [11], [12].…”

mentioning

confidence: 99%

A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence

Kimmig

Memory

Miller

et al. 2019

IEEE Trans. Knowl. Data Eng.

Self Cite

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Abstract-We propose a probabilistic approach to the problem of schema mapping. Our approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of schema mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings as well as inconsistencies and incompleteness in the input. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using state-of-the-art probabilistic reasoning techniques. Our evaluation on a wide range of integration scenarios, including several real-world domains, demonstrates that CMD effectively combines data and metadata information to infer highly accurate mappings even with significant levels of noise.

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Muse: Mapping Understanding and deSign by Example

Cited by 59 publications

References 20 publications

Clio: Schema Mapping Creation and Data Exchange

Clio: Schema Mapping Creation and Data Exchange

Incrementally improving dataspaces based on user feedback

A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence

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