Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Attributed Graph Rewriting for Complex Event Processing Self-Management

Attributed Graph Rewriting for Complex Event Processing Self-Management Attributed Graph Rewriting for Complex Event Processing Self-Management WILSON A. HIGASHINO, Western University/University of Campinas ´ CEDRIC EICHLER, INSA CVL MIRIAM A. M. CAPRETZ, Western University LUIZ F. BITTENCOURT, University of Campinas THIERRY MONTEIL, LAAS-CNRS/INSA Toulouse The use of Complex Event Processing (CEP) and Stream Processing (SP) systems to process high-volume, high-velocity Big Data has renewed interest in procedures for managing these systems. In particular, selfmanagement and adaptation of runtime platforms have been common research themes, as most of these systems run under dynamic conditions. Nevertheless, the research landscape in this area is still young and fragmented. Most research is performed in the context of specific systems, and it is difficult to generalize the results obtained to other contexts. To enable generic and reusable CEP/SP system management procedures and self-management policies, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management (AGeCEP) formalism. AGeCEP represents queries in a language- and technologyagnostic fashion using attributed graphs. Query reconfiguration capabilities are expressed through standardized attributes, which are defined based on a novel classification of CEP query operators. By leveraging this representation, AGeCEP also proposes graph rewriting rules to define consistent reconfigurations of queries. To demonstrate AGeCEP feasibility, this http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

Loading next page...
 
/lp/association-for-computing-machinery/attributed-graph-rewriting-for-complex-event-processing-self-gqj9yd00QC
Publisher
Association for Computing Machinery
Copyright
Copyright © 2016 by ACM Inc.
ISSN
1556-4665
DOI
10.1145/2967499
Publisher site
See Article on Publisher Site

Abstract

Attributed Graph Rewriting for Complex Event Processing Self-Management WILSON A. HIGASHINO, Western University/University of Campinas ´ CEDRIC EICHLER, INSA CVL MIRIAM A. M. CAPRETZ, Western University LUIZ F. BITTENCOURT, University of Campinas THIERRY MONTEIL, LAAS-CNRS/INSA Toulouse The use of Complex Event Processing (CEP) and Stream Processing (SP) systems to process high-volume, high-velocity Big Data has renewed interest in procedures for managing these systems. In particular, selfmanagement and adaptation of runtime platforms have been common research themes, as most of these systems run under dynamic conditions. Nevertheless, the research landscape in this area is still young and fragmented. Most research is performed in the context of specific systems, and it is difficult to generalize the results obtained to other contexts. To enable generic and reusable CEP/SP system management procedures and self-management policies, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management (AGeCEP) formalism. AGeCEP represents queries in a language- and technologyagnostic fashion using attributed graphs. Query reconfiguration capabilities are expressed through standardized attributes, which are defined based on a novel classification of CEP query operators. By leveraging this representation, AGeCEP also proposes graph rewriting rules to define consistent reconfigurations of queries. To demonstrate AGeCEP feasibility, this

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS)Association for Computing Machinery

Published: Sep 20, 2016

There are no references for this article.