research-article

Meta-mappings for schema mapping reuse

Authors:

Luigi Bellomarini,

Riccardo TorloneAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 12, Issue 5

Pages 557 - 569

https://doi.org/10.14778/3303753.3303761

Published: 01 January 2019 Publication History

Abstract

The definition of mappings between heterogeneous schemas is a critical activity of any database application. Existing tools provide high level interfaces for the discovery of correspondences between elements of schemas, but schema mappings need to be manually specified every time from scratch, even if the scenario at hand is similar to one that has already been addressed. The problem is that schema mappings are precisely defined over a pair of schemas and cannot directly be reused on different scenarios. We tackle this challenge by generalizing schema mappings as meta-mappings: formalisms that describe transformations between generic data structures called meta-schemas. We formally characterize schema mapping reuse and explain how meta-mappings are able to: (i) capture enterprise knowledge from previously defined schema mappings and (ii) use this knowledge to suggest new mappings. We develop techniques to infer meta-mappings from existing mappings, to organize them into a searchable repository, and to leverage the repository to propose to users mappings suitable for their needs. We study effectiveness and efficiency in an extensive evaluation over real-world scenarios and show that our system can infer, store, and search millions of meta-mappings in seconds.

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Cited By

Meduri VQuamar ALei CQin XReinwald B(2024)Alfa: active learning for graph neural network-based semantic schema alignmentThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00822-z33:4(981-1011)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00778-023-00822-z
Gilad AMiao ZRoy SYang JIves ZBonifati AEl Abbadi A(2022)Understanding Queries by Conditional InstancesProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517898(355-368)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517898
Ponniah AAgarwal S(2022)WDA: A Domain-Aware Database Schema Analysis for Improving OBDA-Based Event Log ExtractionsAdvanced Data Mining and Applications10.1007/978-3-031-22137-8_22(297-309)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-22137-8_22
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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 12, Issue 5

January 2019

163 pages

ISSN:2150-8097

Editors:
Lei Chen
HKUST
,
Fatma Özcan
IBM Research - Almaden

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VLDB Endowment

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Published: 01 January 2019

Published in PVLDB Volume 12, Issue 5

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Cited By

Meduri VQuamar ALei CQin XReinwald B(2024)Alfa: active learning for graph neural network-based semantic schema alignmentThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00822-z33:4(981-1011)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00778-023-00822-z
Gilad AMiao ZRoy SYang JIves ZBonifati AEl Abbadi A(2022)Understanding Queries by Conditional InstancesProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517898(355-368)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517898
Ponniah AAgarwal S(2022)WDA: A Domain-Aware Database Schema Analysis for Improving OBDA-Based Event Log ExtractionsAdvanced Data Mining and Applications10.1007/978-3-031-22137-8_22(297-309)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-22137-8_22
Bellomarini LMagnanimi DNissl MSallinger E(2020)Neither in the Programs Nor in the Data: Mining the Hidden Financial Knowledge with Knowledge Graphs and ReasoningMining Data for Financial Applications10.1007/978-3-030-66981-2_10(119-134)Online publication date: 18-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-66981-2_10

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