article

Data-driven understanding and refinement of schema mappings

Authors:

Renée J. Miller,

Ronald FaginAuthors Info & Claims

ACM SIGMOD Record, Volume 30, Issue 2

Pages 485 - 496

https://doi.org/10.1145/376284.375729

Published: 01 May 2001 Publication History

Abstract

At the heart of many data-intensive applications is the problem of quickly and accurately transforming data into a new form. Database researchers have long advocated the use of declarative queries for this process. Yet tools for creating, managing and understanding the complex queries necessary for data transformation are still too primitive to permit widespread adoption of this approach. We present a new framework that uses data examples as the basis for understanding and refining declarative schema mappings. We identify a small set of intuitive operators for manipulating examples. These operators permit a user to follow and refine an example by walking through a data source. We show that our operators are powerful enough both to identify a large class of schema mappings and to distinguish effectively between alternative schema mappings. These operators permit a user to quickly and intuitively build and refine complex data transformation queries that map one data source into another.

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Cited By

Fathollahzadeh SBoehm M(2023)GIO: Generating Efficient Matrix and Frame Readers for Custom Data Formats by ExampleProceedings of the ACM on Management of Data10.1145/35892651:2(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589265
周晓(2022)Data Transformation from Hierarchical Model to Relational Model Based on Example ProgrammingHans Journal of Data Mining10.12677/HJDM.2022.12403212:04(334-350)Online publication date: 2022
https://doi.org/10.12677/HJDM.2022.124032
Ghadiri Bashardoost BLyons KMiller R(2021)Towards Knowledge Exchange: State-of-the-Art and Open ProblemsSOFSEM 2021: Theory and Practice of Computer Science10.1007/978-3-030-67731-2_2(13-27)Online publication date: 11-Jan-2021
https://doi.org/10.1007/978-3-030-67731-2_2
Show More Cited By

Index Terms

Data-driven understanding and refinement of schema mappings
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
2. Mathematics of computing
  1. Discrete mathematics

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Data-driven understanding and refinement of schema mappings
SIGMOD '01: Proceedings of the 2001 ACM SIGMOD international conference on Management of data

At the heart of many data-intensive applications is the problem of quickly and accurately transforming data into a new form. Database researchers have long advocated the use of declarative queries for this process. Yet tools for creating, managing and ...
Quasi-inverses of schema mappings

Schema mappings are high-level specifications that describe the relationship between two database schemas. Two operators on schema mappings, namely the composition operator and the inverse operator, are regarded as especially important. Progress on the ...
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Special Issue: SIGMOD/PODS 2004

A schema mapping is a specification that describes how data structured under one schema (the source schema) is to be transformed into data structured under a different schema (the target schema). A fundamental problem is composing schema mappings: given ...

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Information & Contributors

Information

Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 30, Issue 2

June 2001

625 pages

ISSN:0163-5808

DOI:10.1145/376284

Editors:
Timos Sellis
National Technical Univ. of Athens
,
Sharad Mehrotra
Univ. of California at Irvine

Issue’s Table of Contents

SIGMOD '01: Proceedings of the 2001 ACM SIGMOD international conference on Management of data
May 2001
630 pages
ISBN:1581133324
DOI:10.1145/375663
Editors:
Timos Sellis,
Sharad Mehrotra

Copyright © 2001 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 May 2001

Published in SIGMOD Volume 30, Issue 2

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Cited By

Fathollahzadeh SBoehm M(2023)GIO: Generating Efficient Matrix and Frame Readers for Custom Data Formats by ExampleProceedings of the ACM on Management of Data10.1145/35892651:2(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589265
周晓(2022)Data Transformation from Hierarchical Model to Relational Model Based on Example ProgrammingHans Journal of Data Mining10.12677/HJDM.2022.12403212:04(334-350)Online publication date: 2022
https://doi.org/10.12677/HJDM.2022.124032
Ghadiri Bashardoost BLyons KMiller R(2021)Towards Knowledge Exchange: State-of-the-Art and Open ProblemsSOFSEM 2021: Theory and Practice of Computer Science10.1007/978-3-030-67731-2_2(13-27)Online publication date: 11-Jan-2021
https://doi.org/10.1007/978-3-030-67731-2_2
Bashardoost BMiller RLyons KNargesian F(2020)Knowledge translationProceedings of the VLDB Endowment10.14778/3407790.340780613:12(2018-2032)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.14778/3407790.3407806
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
Yaghmazadeh NWang XDillig I(2018)Automated migration of hierarchical data to relational tables using programming-by-exampleProceedings of the VLDB Endowment10.1145/3187009.317773511:5(580-593)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1145/3187009.3177735
Yaghmazadeh NWang XDillig I(2018)Automated migration of hierarchical data to relational tables using programming-by-exampleProceedings of the VLDB Endowment10.1145/3177732.317773511:5(580-593)Online publication date: 5-Oct-2018
https://dl.acm.org/doi/10.1145/3177732.3177735
Li DHuang MLi XRuan YYao L(2018)MfeCNN: Mixture Feature Embedding Convolutional Neural Network for Data MappingIEEE Transactions on NanoBioscience10.1109/TNB.2018.284105317:3(165-171)Online publication date: Jul-2018
https://doi.org/10.1109/TNB.2018.2841053
Cate BKolaitis PQian KTan W(2017)Approximation Algorithms for Schema-Mapping Discovery from Data ExamplesACM Transactions on Database Systems10.1145/304471242:2(1-41)Online publication date: 28-Apr-2017
https://dl.acm.org/doi/10.1145/3044712
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
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