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Machine learning for emergent middleware

Bennaceur, Amel; Issarny, Valérie; Sykes, Daniel; Howar, Falk; Isberner, Malte; Steffen, Bernhard; Johansson, Richard and Moschitti, Alessandro (2013). Machine learning for emergent middleware. In: Trustworthy Eternal Systems via Evolving Software, Data and Knowledge, Springer, pp. 16–29.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-642-45260-4_2
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Abstract

Highly dynamic and heterogeneous distributed systems are challenging today's middleware technologies. Existing middleware paradigms are unable to deliver on their most central promise, which is offering interoperability. In this paper, we argue for the need to dynamically synthesise distributed system infrastructures according to the current operating environment, thereby generating "Emergent Middleware'' to mediate interactions among heterogeneous networked systems that interact in an ad hoc way. The paper outlines the overall architecture of Enablers underlying Emergent Middleware, and in particular focuses on the key role of learning in supporting such a process, spanning statistical learning to infer the semantics of networked system functions and automata learning to extract the related behaviours of networked systems.

Item Type: Conference or Workshop Item
Copyright Holders: 2012 Springer
ISBN: 3-642-45260-4, 978-3-642-45260-4
ISSN: 1865-0929
Project Funding Details:
Funded Project NameProject IDFunding Body
Connect: Emergent Connectors for Eternal Software Intensive Networked Systems231167ICT
Extra Information: Communications in Computer and Information Science
Volume 379, 2013
Keywords: machine learning; natural language processing; automata learning; interoperability; automated mediation
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Related URLs:
Item ID: 39468
Depositing User: Amel Bennaceur
Date Deposited: 10 Feb 2014 09:54
Last Modified: 02 May 2018 13:57
URI: http://oro.open.ac.uk/id/eprint/39468
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