Automatic direct and indirect schema mapping

Embley, David W.; Xu, Li; Ding, Yihong

doi:10.1145/1041410.1041413

Cited by 50 publications

(43 citation statements)

References 13 publications

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“…In [21], the authors apply knowledge from domain ontology snippets and data frames to detect 1:n schema mappings. These techniques do not suit geospatial data since patterns are not sufficient in detecting attributes.…”

Section: Automatic Schema Matchingmentioning

confidence: 99%

An Automatic Matcher and Linker for Transportation Datasets

Masri

Zeitouni

Kedad

et al. 2017

IJGI

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Multimodality requires the integration of heterogeneous transportation data to construct a broad view of the transportation network. Many new transportation services are emerging while being isolated from previously-existing networks. This leads them to publish their data sources to the web, according to linked data principles, in order to gain visibility. Our interest is to use these data to construct an extended transportation network that links these new services to existing ones. The main problems we tackle in this article fall in the categories of automatic schema matching and data interlinking. We propose an approach that uses web services as mediators to help in automatically detecting geospatial properties and mapping them between two different schemas. On the other hand, we propose a new interlinking approach that enables the user to define rich semantic links between datasets in a flexible and customizable way.

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Section: Automatic Schema Matchingmentioning

confidence: 99%

An Automatic Matcher and Linker for Transportation Datasets

Masri

Zeitouni

Kedad

et al. 2017

IJGI

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“…Although it provides modeling whole integration process rapidly, it does not consider details of each integration process modeling such as data mapping. Furthermore, several automated data merging approaches are also researched in order to reduce human intervention for data merging through extraction of combined meta-data from source data or source meta-data in (Konigs, 2005) and (Embley et al, 2004). Particularly, (Fabro et al, 2008) and (Marcos et al, 2006) describes semi-automated model transformation using matching transformations and weaving models which can be applied on generation of merging model as well.…”

Section: Related Workmentioning

confidence: 99%

Automatic Generation of Data Merging Program Codes

Kim¹,

Oussena²,

Zhang³

et al. 2010

Proceedings of the 5th International Conference on Software and Data Technologies

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Abstract:Data merging is an essential part of ETL (Extract-Transform-Load) processes to build a data warehouse system. To avoid rewheeling merging techniques, we propose a Data Merging Meta-model (DMM) and its transformation into executable program codes in the manner of model driven engineering. DMM allows defining relationships of different model entities and their merging types in conceptual level. Our formalized transformation described using ATL (ATLAS Transformation Language) enables automatic generation of PL/SQL packages to execute data merging in commercial ETL tools. With this approach data warehouse engineers can be relieved from the burden of repetitive complex script coding and the pain of maintaining consistency of design and implementation.

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“…Decision trees have been used in ontology matching for discovering hidden mappings among entities [17]. Their approach is based on learning rules for matching terms in Wordnet.…”

Section: Related Workmentioning

confidence: 99%

A Flexible Approach for Planning Schema Matching Algorithms

Duchateau

Bellahsène

Coletta

2008

On the Move to Meaningful Internet Systems: OTM 2008

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Abstract. Most of the schema matching tools are assembled from multiple match algorithms, each employing a particular technique to improve matching accuracy and making matching systems extensible and customizable to a particular domain. Recently, it has been pointed out that the main issue is how to select the most suitable match algorithms to execute for a given domain and how to adjust the multiple knobs (e.g. threshold, performance, quality, etc.). The solutions provided by current schema matching tools consist in aggregating the results obtained by several match algorithms to improve the quality of the discovered matches. However, aggregation entails several drawbacks. In this article, we present a novel method for combining schema matching algorithms. The matching engine makes use of a decision tree to combine the most appropriate match algorithms. As a first consequence of using the decision tree, the performance of the system is improved since the complexity is bounded by the height of the decision tree. Thus, only a subset of these match algorithms is used during the matching process. The second advantage is the improvement of the quality of matches. Indeed, for a given domain, only the most suitable match algorithms are used. The experiments show the effectiveness of our approach w.r.t. other matching tools.

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Automatic direct and indirect schema mapping

Cited by 50 publications

References 13 publications

An Automatic Matcher and Linker for Transportation Datasets

An Automatic Matcher and Linker for Transportation Datasets

Automatic Generation of Data Merging Program Codes

A Flexible Approach for Planning Schema Matching Algorithms

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