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Comparing Machine Learning Approaches for Context-Aware Composition
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics. (Software Technology Group)
Linköping University, Department for Computer and Information Science .
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics. (Software Technology Group)ORCID iD: 0000-0002-7565-3714
2011 (English)In: Software Composition: 10th International Conference, SC 2011, Zurich, Switzerland, June 30 - July 1, 2011, Proceedings / [ed] Sven Apel, Ethan Jackson, Berlin: Springer, 2011, Vol. 6708, p. 18-33Chapter in book (Refereed)
Abstract [en]

Context-Aware Composition allows to automatically select optimal variants of algorithms, data-structures, and schedules at runtime using generalized dynamic Dispatch Tables. These tables grow exponentially with the number of significant context attributes. To make Context-Aware Composition scale, we suggest four alternative implementations to Dispatch Tables, all well-known in the field of machine learning: Decision Trees, Decision Diagrams, Naive Bayes and Support Vector Machines classifiers. We assess their decision overhead and memory consumption theoretically and practically in a number of experiments on different hardware platforms. Decision Diagrams turn out to be more compact compared to Dispatch Tables, almost as accurate, and faster in decision making. Using Decision Diagrams in Context-Aware Composition leads to a better scalability, i.e., Context-Aware Composition can be applied at more program points and regard more context attributes than before.

Place, publisher, year, edition, pages
Berlin: Springer, 2011. Vol. 6708, p. 18-33
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; Volume 6708
Keywords [en]
Context-Aware Composition – Autotuning – Machine Learning
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-13442DOI: 10.1007/978-3-642-22045-6_2Scopus ID: 2-s2.0-79960128332ISBN: 978-3-642-22044-9 (print)ISBN: 978-3-642-22045-6 (print)OAI: oai:DiVA.org:lnu-13442DiVA, id: diva2:429718
Conference
International Conference on Software Composition 2012
Available from: 2011-07-05 Created: 2011-07-05 Last updated: 2017-01-27Bibliographically approved

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Publisher's full textScopushttp://dx.doi.org/10.1007/978-3-642-22045-6_2

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Danylenko, AntoninaLöwe, Welf

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CiteExportLink to record
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