research-article

TRAMP: understanding the behavior of schema mappings through provenance

Editors: Elisa Bertino, Paolo Atzeni, Kian Lee Tan, Yi Chen, Y. C. Tay Authors:

Gustavo Alonso,

Renée J. Miller,

Laura M. HaasAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 3, Issue 1-2

Pages 1314 - 1325

https://doi.org/10.14778/1920841.1921003

Published: 01 September 2010 Publication History

Abstract

Though partially automated, developing schema mappings remains a complex and potentially error-prone task. In this paper, we present TRAMP (TRAnsformation Mapping Provenance), an extensive suite of tools supporting the debugging and tracing of schema mappings and transformation queries. TRAMP combines and extends data provenance with two novel notions, transformation provenance and mapping provenance, to explain the relationship between transformed data and those transformations and mappings that produced that data. In addition we provide query support for transformations, data, and all forms of provenance. We formally define transformation and mapping provenance, present an efficient implementation of both forms of provenance, and evaluate the resulting system through extensive experiments.

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Cited By

Li YRadev DRafiei DLi YRadev DRafiei D(2023)InteractivityNatural Language Interfaces to Databases10.1007/978-3-031-45043-3_7(177-229)Online publication date: 25-Nov-2023
https://doi.org/10.1007/978-3-031-45043-3_7
Rani AGoyal NGadia S(2022)Provenance Framework for Multi-Depth Querying Using Zero-Information Loss DatabaseInternational Journal of Information Technology & Decision Making10.1142/S021962202250084522:05(1693-1742)Online publication date: 30-Nov-2022
https://doi.org/10.1142/S0219622022500845
Hu JJoung JJacobs MGajos KSeltzer MRothermel GBae D(2020)Improving data scientist efficiency with provenanceProceedings of the ACM/IEEE 42nd International Conference on Software Engineering10.1145/3377811.3380366(1086-1097)Online publication date: 27-Jun-2020
https://dl.acm.org/doi/10.1145/3377811.3380366
Show More Cited By

TRAMP: understanding the behavior of schema mappings through provenance
1. Information systems
  1. Data management systems
    1. Database management system engines
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 3, Issue 1-2

September 2010

1658 pages

ISSN:2150-8097

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VLDB Endowment

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Published: 01 September 2010

Published in PVLDB Volume 3, Issue 1-2

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Cited By

Li YRadev DRafiei DLi YRadev DRafiei D(2023)InteractivityNatural Language Interfaces to Databases10.1007/978-3-031-45043-3_7(177-229)Online publication date: 25-Nov-2023
https://doi.org/10.1007/978-3-031-45043-3_7
Rani AGoyal NGadia S(2022)Provenance Framework for Multi-Depth Querying Using Zero-Information Loss DatabaseInternational Journal of Information Technology & Decision Making10.1142/S021962202250084522:05(1693-1742)Online publication date: 30-Nov-2022
https://doi.org/10.1142/S0219622022500845
Hu JJoung JJacobs MGajos KSeltzer MRothermel GBae D(2020)Improving data scientist efficiency with provenanceProceedings of the ACM/IEEE 42nd International Conference on Software Engineering10.1145/3377811.3380366(1086-1097)Online publication date: 27-Jun-2020
https://dl.acm.org/doi/10.1145/3377811.3380366
Bonifati AComignani UCoquery EThion R(2019)Interactive Mapping Specification with Exemplar TuplesACM Transactions on Database Systems10.1145/332148544:3(1-44)Online publication date: 5-Jun-2019
https://dl.acm.org/doi/10.1145/3321485
Deutch DMoskovitch YRinetzky NBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Hypothetical Reasoning via Provenance AbstractionProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3300084(537-554)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3300084
Deutch DGilad AMoskovitch Y(2018)Efficient provenance tracking for datalog using top-k queriesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-018-0496-727:2(245-269)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s00778-018-0496-7
Interlandi MEkmekji AShah KGulzar MTetali SKim MMillstein TCondie T(2018)Adding data provenance support to Apache SparkThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-017-0474-527:5(595-615)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s00778-017-0474-5
Bonifati AComignani UCoquery EThion RChirkova RYang JSuciu D(2017)Interactive Mapping Specification with Exemplar TuplesProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3064028(667-682)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3064028
Herschel MDiestelkämper RBen Lahmar H(2017)A survey on provenanceThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-017-0486-126:6(881-906)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s00778-017-0486-1
Dignös ABöhlen MGamper JJensen C(2016)Extending the Kernel of a Relational DBMS with Comprehensive Support for Sequenced Temporal QueriesACM Transactions on Database Systems10.1145/296760841:4(1-46)Online publication date: 2-Nov-2016
https://dl.acm.org/doi/10.1145/2967608
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