Schema matching prediction with applications to data source discovery and dynamic ensembling

Schema matching prediction with applications to data source discovery and dynamic ensembling Web-scale data integration involves fully automated efforts which lack knowledge of the exact match between data descriptions. In this paper, we introduce schema matching prediction , an assessment mechanism to support schema matchers in the absence of an exact match . Given attribute pair-wise similarity measures, a predictor predicts the success of a matcher in identifying correct correspondences. We present a comprehensive framework in which predictors can be defined, designed, and evaluated. We formally define schema matching evaluation and schema matching prediction using similarity spaces and discuss a set of four desirable properties of predictors, namely correlation, robustness, tunability, and generalization. We present a method for constructing predictors, supporting generalization, and introduce prediction models as means of tuning prediction toward various quality measures. We define the empirical properties of correlation and robustness and provide concrete measures for their evaluation. We illustrate the usefulness of schema matching prediction by presenting three use cases: We propose a method for ranking the relevance of deep Web sources with respect to given user needs. We show how predictors can assist in the design of schema matching systems. Finally, we show how prediction can support dynamic weight setting of matchers in an ensemble, thus improving upon current state-of-the-art weight setting methods. An extensive empirical evaluation shows the usefulness of predictors in these use cases and demonstrates the usefulness of prediction models in increasing the performance of schema matching. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Schema matching prediction with applications to data source discovery and dynamic ensembling

Loading next page...
 
/lp/springer_journal/schema-matching-prediction-with-applications-to-data-source-discovery-52HzZJuUSG
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2013 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-013-0325-y
Publisher site
See Article on Publisher Site

Abstract

Web-scale data integration involves fully automated efforts which lack knowledge of the exact match between data descriptions. In this paper, we introduce schema matching prediction , an assessment mechanism to support schema matchers in the absence of an exact match . Given attribute pair-wise similarity measures, a predictor predicts the success of a matcher in identifying correct correspondences. We present a comprehensive framework in which predictors can be defined, designed, and evaluated. We formally define schema matching evaluation and schema matching prediction using similarity spaces and discuss a set of four desirable properties of predictors, namely correlation, robustness, tunability, and generalization. We present a method for constructing predictors, supporting generalization, and introduce prediction models as means of tuning prediction toward various quality measures. We define the empirical properties of correlation and robustness and provide concrete measures for their evaluation. We illustrate the usefulness of schema matching prediction by presenting three use cases: We propose a method for ranking the relevance of deep Web sources with respect to given user needs. We show how predictors can assist in the design of schema matching systems. Finally, we show how prediction can support dynamic weight setting of matchers in an ensemble, thus improving upon current state-of-the-art weight setting methods. An extensive empirical evaluation shows the usefulness of predictors in these use cases and demonstrates the usefulness of prediction models in increasing the performance of schema matching.

Journal

The VLDB JournalSpringer Journals

Published: Oct 1, 2013

References

  • A comparative analysis of methodologies for database schema integration
    Batini, C; Lenzerini, M; Navathe, SB

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off