Paper Info
Title
Mining Abstract XML Data-Types.
Abstract
Schema integration has been a long-standing challenge for the data-engineering community that has received steady attention over the past three decades. General-purpose integration approaches construct unified schemas that encompass all schema elements. Schema integration has been revisited in the past decade in service-oriented computing since the input/output data-types of service interfaces are heterogeneous XML schemas. However, service integration differs from the traditional integration problem, since it should generalize schemas (mining abstract data-types) instead of unifying all schema elements. To mine well-formed abstract data-types, the fundamental Liskov Substitution Principle (LSP), which generally holds between abstract data-types and their subtypes, should be followed. However, due to the heterogeneity of service data-types, the strict employment of LSP is not usually feasible. On top of that, XML offers a rich type system, based on which data-types are defined via combining type patterns (e.g., composition, aggregation). The existing integration approaches have not dealt with the challenges of a defining subtyping relation between XML type patterns. To address these challenges, we propose a relaxed version of LSP between XML type patterns and an automated generalization process for mining abstract XML data-types. We evaluate the effectiveness and the efficiency of the process on the schemas of two datasets against two representative state-of-the-art approaches.
Year
DOI
Venue
2019
10.1145/3267467
TWEB
Keywords
Field
DocType
Type pattern, embedded subtree, pruning, subtyping relation
XML,Information retrieval,Computer science,Xml data,Liskov substitution principle,XML schema,Subtyping,Schema (psychology)
Journal
Volume
Issue
ISSN
13
1
1559-1131
Citations 
PageRank 
References 
0
0.34
45
Authors
2
Name
Order
Citations
PageRank
Dionysis Athanasopoulos1475.19
Apostolos Zarras229330.20