Adaptive stream query processing approach for Linked Stream Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Over the last few years several stream query processors havebeen proposed for efficient processing of Linked Stream Data (LSD).Multiple aspects can affect the performance and correctness of the re-sults produced by the query processors, including operational semanticsof linked streams, query execution method, and target domain. Howeverexisting approaches to stream query processing lack the adaptability toreact to changing requirements of the applications and properties of theunderlying data streams. The goal of this research is to design a moreflexible and adaptive stream query processing approach which enablesstream query processing approaches to adapt according to the require-ments of the applications and to the characteristics of the data streams.The adaptive approach will support efficient processing of larger amountof data by mitigating computational cost and network traffic and will beable to serve a broader category of real-time applications efficiently dueto its adaptive nature.
Original languageEnglish
Title of host publicationWeb Reasoning and Rule Systems - 8th International Conference, RR 2014, Proceedings
EditorsRoman Kontchakov, Marie-Laure Mugnier
Number of pages2
ISBN (Print)9783319111124
Publication statusPublished - 2014
Event8th International Conference on Web Reasoning and Rule Systems - Athens, Greece
Duration: 15 Sept 201417 Sept 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8741 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Conference on Web Reasoning and Rule Systems
Abbreviated titleRR 2014

Bibliographical note

Funding Information:
★This research has been partially supported by Science Foundation Ireland (SFI) under grant No. SFI/12/RC/2289 and EU FP7 CityPulse Project under grant No.603095.

Copyright 2017 Elsevier B.V., All rights reserved.


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