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  1. Transactions on Database Systems (TODS)
  2. ACM Transactions on Database Systems (TODS) : Volume 38
  3. Issue 4(Invited papers issue), November 2013
  4. Learning schema mappings
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ACM Transactions on Database Systems (TODS) : Volume 42
ACM Transactions on Database Systems (TODS) : Volume 41
ACM Transactions on Database Systems (TODS) : Volume 40
ACM Transactions on Database Systems (TODS) : Volume 39
ACM Transactions on Database Systems (TODS) : Volume 38
Issue 4(Invited papers issue), November 2013
Foreword to invited papers issue
High-performance complex event processing over hierarchical data
Consistent thinning of large geographical data for map visualization
XLynx—An FPGA-based XML filter for hybrid XQuery processing
The complexity of regular expressions and property paths in SPARQL
Static analysis and optimization of semantic web queries
Mergeable summaries
Validating XML documents in the streaming model with external memory
Learning schema mappings
Issue 3, August 2013
Issue 2, June 2013
Issue 1, April 2013
ACM Transactions on Database Systems (TODS) : Volume 37
ACM Transactions on Database Systems (TODS) : Volume 36
ACM Transactions on Database Systems (TODS) : Volume 35
ACM Transactions on Database Systems (TODS) : Volume 34
ACM Transactions on Database Systems (TODS) : Volume 33
ACM Transactions on Database Systems (TODS) : Volume 32
ACM Transactions on Database Systems (TODS) : Volume 31
ACM Transactions on Database Systems (TODS) : Volume 30
ACM Transactions on Database Systems (TODS) : Volume 29
ACM Transactions on Database Systems (TODS) : Volume 28
ACM Transactions on Database Systems (TODS) : Volume 27
ACM Transactions on Database Systems (TODS) : Volume 26
ACM Transactions on Database Systems (TODS) : Volume 25
ACM Transactions on Database Systems (TODS) : Volume 24
ACM Transactions on Database Systems (TODS) : Volume 23
ACM Transactions on Database Systems (TODS) : Volume 22
ACM Transactions on Database Systems (TODS) : Volume 21
ACM Transactions on Database Systems (TODS) : Volume 20
ACM Transactions on Database Systems (TODS) : Volume 19
ACM Transactions on Database Systems (TODS) : Volume 18
ACM Transactions on Database Systems (TODS) : Volume 17
ACM Transactions on Database Systems (TODS) : Volume 16
ACM Transactions on Database Systems (TODS) : Volume 15
ACM Transactions on Database Systems (TODS) : Volume 14
ACM Transactions on Database Systems (TODS) : Volume 13
ACM Transactions on Database Systems (TODS) : Volume 12
ACM Transactions on Database Systems (TODS) : Volume 11
ACM Transactions on Database Systems (TODS) : Volume 10
ACM Transactions on Database Systems (TODS) : Volume 9
ACM Transactions on Database Systems (TODS) : Volume 8
ACM Transactions on Database Systems (TODS) : Volume 7
ACM Transactions on Database Systems (TODS) : Volume 6
ACM Transactions on Database Systems (TODS) : Volume 5
ACM Transactions on Database Systems (TODS) : Volume 4
ACM Transactions on Database Systems (TODS) : Volume 3
ACM Transactions on Database Systems (TODS) : Volume 2
ACM Transactions on Database Systems (TODS) : Volume 1

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Learning schema mappings

Content Provider ACM Digital Library
Author Dalmau, VĂ­ctor Kolaitis, Phokion G. Cate, Balder Ten
Copyright Year 2013
Abstract A schema mapping is a high-level specification of the relationship between a source schema and a target schema. Recently, a line of research has emerged that aims at deriving schema mappings automatically or semi-automatically with the help of data examples, that is, pairs consisting of a source instance and a target instance that depict, in some precise sense, the intended behavior of the schema mapping. Several different uses of data examples for deriving, refining, or illustrating a schema mapping have already been proposed and studied. In this article, we use the lens of computational learning theory to systematically investigate the problem of obtaining algorithmically a schema mapping from data examples. Our aim is to leverage the rich body of work on learning theory in order to develop a framework for exploring the power and the limitations of the various algorithmic methods for obtaining schema mappings from data examples. We focus on GAV schema mappings, that is, schema mappings specified by GAV (Global-As-View) constraints. GAV constraints are the most basic and the most widely supported language for specifying schema mappings. We present an efficient algorithm for learning GAV schema mappings using Angluin's model of exact learning with membership and equivalence queries. This is optimal, since we show that neither membership queries nor equivalence queries suffice, unless the source schema consists of unary relations only. We also obtain results concerning the learnability of schema mappings in the context of Valiant's well-known PAC (Probably-Approximately-Correct) learning model, and concerning the learnability of restricted classes of GAV schema mappings. Finally, as a byproduct of our work, we show that there is no efficient algorithm for approximating the shortest GAV schema mapping fitting a given set of examples, unless the source schema consists of unary relations only.
Starting Page 1
Ending Page 31
Page Count 31
File Format PDF
ISSN 03625915
e-ISSN 15574644
DOI 10.1145/2539032.2539035
Volume Number 38
Issue Number 4
Journal ACM Transactions on Database Systems (TODS)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-12-04
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Schema mappings Computational learning theory Data exchange
Content Type Text
Resource Type Article
Subject Information Systems
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