Data integration by bi-directional schema transformation rules

McBrien, Peter; Poulovassilis, Alexandra

doi:10.1109/icde.2003.1260795

Cited by 69 publications

(56 citation statements)

References 18 publications

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“…The local-as-view (LAV) schema mapping approaches are more suitable in order to support the required level of malleability [65,30]. In the LAV approach, plugging new data sources requires defining a mapping of the new source schemata to the global schema, without affecting the existing mappings.…”

Section: Discussionmentioning

confidence: 99%

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A Unified View of Data-Intensive Flows in Business Intelligence Systems: A Survey

Jovanovic

Romero

Abelló

2016

Lecture Notes in Computer Science

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Abstract. Data-intensive flows are central processes in today's business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysisready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of dataintensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today's research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.

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Section: Discussionmentioning

confidence: 99%

“…Several approaches further worked on generalizing the concept of schema mappings by supporting the expressive power of both LAV and GAV, i.e., bothas-view (BAV) [65], and global-and-local-as-view (GLAV) [30].…”

Section: Malleabilitymentioning

confidence: 99%

A Unified View of Data-Intensive Flows in Business Intelligence Systems: A Survey

Jovanovic

Romero

Abelló

2016

Lecture Notes in Computer Science

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“…Once the current transformation network has been extended in this way, the actions taken to evolve the rest of the transformation network and schemas, and any materialised derived data, are localised to just those schema constructs that are a ected by t h e e v olution. We refer the reader to 23,24,15] for details of how this can be achieved in both virtual 23,24] and materialised 15] integration scenarios. The algorithms used are mainly automatic, except for input of domain or expert human knowledge regarding the semantics of new schema constructs added to a local or global schema which a r e not semantically equivalent t o a n y existing constructs in the schema.…”

Section: The Automed Toolkitmentioning

confidence: 99%

Cluster Based Integration of Heterogeneous Biological Databases Using the AutoMed Toolkit

Maibaum

Zamboulis

Rimon

et al. 2005

Lecture Notes in Computer Science

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Abstract. This paper presents an extensible architecture that can be used to support the integration of heterogeneous biological data sets. In our architecture, a clustering approach has been developed to support distributed biological data sources with inconsistent identi cation of biological objects. The architecture uses the AutoMed data integration toolkit to store the schemas of the data sources and the semiautomatically generated transformations from the source data into the data of an integrated warehouse. AutoMed supports bi-directional, extensible transformations which can be used to update the warehouse data as entities change, are added, or are deleted in the data sources.

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“…The ModelGen operator realizes a schema translation from a source data model M s to a target data model M t of schemas conforming to M s , for instance, a translation of a schema from the ER to an XML-based data model. Several approaches to this problem have been proposed in the last years [1,7,15,17]. In this paper, we provide a novel contribution to this problem by studying a framework for schema translation with a clear and precise semantics, that can be at the basis of an innovative tool supporting an important activity of model management.…”

Section: Introductionmentioning

confidence: 99%

Schema Exchange: A Template-Based Approach to Data and Metadata Translation

Papotti¹,

Torlone²

2007

Conceptual Modeling - ER 2007

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Abstract. We study the schema exchange problem, a natural extension of the data exchange problem to an intensional level. To this end, we first introduce the notion of schema template, a tool for the representation of a class of schemas sharing the same structure. We then define the schema exchange notion as the problem of (i) taking a schema that matches a source template, and (ii) generating a new schema for a target template, on the basis of a set of dependencies defined over the two templates. This framework allows the definition, once for all, of generic transformations that work for several schemas. A method for the generation of a "correct" solution of the schema exchange problem is proposed and a number of general results are given. We also show how it is possible to automatically generate a data exchange setting from a schema exchange solution. This allows the definition of queries to migrate data from a source database into the one obtained as a result of a schema exchange.

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Data integration by bi-directional schema transformation rules

Cited by 69 publications

References 18 publications

A Unified View of Data-Intensive Flows in Business Intelligence Systems: A Survey

A Unified View of Data-Intensive Flows in Business Intelligence Systems: A Survey

Cluster Based Integration of Heterogeneous Biological Databases Using the AutoMed Toolkit

Schema Exchange: A Template-Based Approach to Data and Metadata Translation

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