research-article

Sample-driven schema mapping

Authors:

Michael J. Cafarella,

H. V. JagadishAuthors Info & Claims

SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

Pages 73 - 84

https://doi.org/10.1145/2213836.2213846

Published: 20 May 2012 Publication History

Abstract

End-users increasingly find the need to perform light-weight, customized schema mapping. State-of-the-art tools provide powerful functions to generate schema mappings, but they usually require an in-depth understanding of the semantics of multiple schemas and their correspondences, and are thus not suitable for users who are technically unsophisticated or when a large number of mappings must be performed.

We propose a system for sample-driven schema mapping. It automatically constructs schema mappings, in real time, from user-input sample target instances. Because the user does not have to provide any explicit attribute-level match information, she is isolated from the possibly complex structure and semantics of both the source schemas and the mappings. In addition, the user never has to master any operations specific to schema mappings: she simply types data values into a spreadsheet-style interface. As a result, the user can construct mappings with a much lower cognitive burden.

In this paper we present Mweaver, a prototype sample-driven schema mapping system. It employs novel algorithms that enable the system to obtain desired mapping results while meeting interactive response performance requirements. We show the results of a user study that compares Mweaver with two state-of-the-art mapping tools across several mapping tasks, both real and synthetic. These suggest that the Mweaver system enables users to perform practical mapping tasks in about 1/5th the time needed by the state-of-the-art tools.

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Cited By

Chang JCui BNargesian FAsudeh AJagadish H(2024)Data distribution tailoring revisited: cost-efficient integration of representative dataThe VLDB Journal10.1007/s00778-024-00849-w33:5(1283-1306)Online publication date: 12-Apr-2024
https://doi.org/10.1007/s00778-024-00849-w
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
Gong YZhu ZGalhotra SFernandez R(2023)Ver: View Discovery in the Wild2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00045(503-516)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00045
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Index Terms

Sample-driven schema mapping
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Heterogeneous (hybrid) systems
2. Information systems
  1. Data management systems

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cover image ACM Conferences

SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

May 2012

886 pages

ISBN:9781450312479

DOI:10.1145/2213836

General Chairs:
K. Sel�uk Candan
Arizona State University
,
Yi Chen
Arizona State University
,
Richard Snodgrass
University of Arizona
,
Program Chair:
Luis Gravano
Columbia University
,
Publications Chair:
Ariel Fuxman
Microsoft Research

Copyright � 2012 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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Published: 20 May 2012

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SIGMOD/PODS '12: International Conference on Management of Data

May 20 - 24, 2012

Arizona, Scottsdale, USA

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SIGMOD '12 Paper Acceptance Rate 48 of 289 submissions, 17%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Chang JCui BNargesian FAsudeh AJagadish H(2024)Data distribution tailoring revisited: cost-efficient integration of representative dataThe VLDB Journal10.1007/s00778-024-00849-w33:5(1283-1306)Online publication date: 12-Apr-2024
https://doi.org/10.1007/s00778-024-00849-w
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
Gong YZhu ZGalhotra SFernandez R(2023)Ver: View Discovery in the Wild2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00045(503-516)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00045
Asudeh ANargesian F(2022)Towards distribution-aware query answering in data marketsProceedings of the VLDB Endowment10.14778/3551793.355185815:11(3137-3144)Online publication date: 29-Sep-2022
https://dl.acm.org/doi/10.14778/3551793.3551858
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
Nargesian FAsudeh AJagadish H(2021)Tailoring data source distributions for fairness-aware data integrationProceedings of the VLDB Endowment10.14778/3476249.347629914:11(2519-2532)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476299
Wu FSong HYin JGao LBaldi MAnand N(2021)NEMA: Automatic Integration of Large Network Management DatabasesIEEE Transactions on Network and Service Management10.1109/TNSM.2020.303641418:3(3783-3797)Online publication date: Sep-2021
https://doi.org/10.1109/TNSM.2020.3036414
Zeimetz TSchenkel R(2021)FiLiPo: A Sample Driven Approach for Finding Linkage Points Between RDF Data and APIsAdvances in Databases and Information Systems10.1007/978-3-030-82472-3_18(244-259)Online publication date: 16-Aug-2021
https://doi.org/10.1007/978-3-030-82472-3_18
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
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