Article

A framework for resource-aware knowledge discovery in data streams: a holistic approach with its application to clustering

Authors:

Mohamed Medhat Gaber,

Philip S. YuAuthors Info & Claims

SAC '06: Proceedings of the 2006 ACM symposium on Applied computing

Pages 649 - 656

https://doi.org/10.1145/1141277.1141427

Published: 23 April 2006 Publication History

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Abstract

Mining data streams is a field of increase interest due to the importance of its applications and dissemination of data stream generators. Most of the streaming techniques developed so far have not addressed the need of resource-aware computing in data stream analysis. The fact that streaming information is often generated or received onboard resource-constrained computational devices such as sensors and mobile devices motivates the need for resource-awareness in data stream processing systems. In this paper, we propose a generic framework that enables resource-awareness in streaming computation using algorithm granularity settings in order to change the resource consumption patterns periodically. This generic framework is applied to a novel threshold-based micro-clustering algorithm to test its validity and feasibility. We have termed this algorithm as RA-Cluster. RA-Custer is the first stream clustering algorithm that can adapt to the changing availability of different resources. The experimental results showed the applicability of the framework and the algorithm in terms of resource-awareness and accuracy.

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Cited By

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Parenreng JKitagawa AAndayani D(2019)A Study of Limited Resources and Security Adaptation for Extreme Area in Wireless Sensor NetworksJournal of Physics: Conference Series10.1088/1742-6596/1244/1/0120131244(012013)Online publication date: 25-Jun-2019
https://doi.org/10.1088/1742-6596/1244/1/012013
Parenreng JKitagawa A(2018)Resource Optimization Techniques and Security Levels for Wireless Sensor Networks Based on the ARSy FrameworkSensors10.3390/s1805159418:5(1594)Online publication date: 17-May-2018
https://doi.org/10.3390/s18051594
Moreno GCámara JGarlan DKlein MAndersson JWeyns D(2018)Uncertainty reduction in self-adaptive systemsProceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems10.1145/3194133.3194144(51-57)Online publication date: 28-May-2018
https://dl.acm.org/doi/10.1145/3194133.3194144
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Index Terms

A framework for resource-aware knowledge discovery in data streams: a holistic approach with its application to clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
  2. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
2. Information systems

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Published In

SAC '06: Proceedings of the 2006 ACM symposium on Applied computing

April 2006

1967 pages

ISBN:1595931082

DOI:10.1145/1141277

Conference Chair:
Hisham M. Haddad
Kennesaw State University, Kennesaw, Georgia

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 April 2006

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SAC06: The 2006 ACM Symposium on Applied Computing

April 23 - 27, 2006

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Parenreng JKitagawa AAndayani D(2019)A Study of Limited Resources and Security Adaptation for Extreme Area in Wireless Sensor NetworksJournal of Physics: Conference Series10.1088/1742-6596/1244/1/0120131244(012013)Online publication date: 25-Jun-2019
https://doi.org/10.1088/1742-6596/1244/1/012013
Parenreng JKitagawa A(2018)Resource Optimization Techniques and Security Levels for Wireless Sensor Networks Based on the ARSy FrameworkSensors10.3390/s1805159418:5(1594)Online publication date: 17-May-2018
https://doi.org/10.3390/s18051594
Moreno GCámara JGarlan DKlein MAndersson JWeyns D(2018)Uncertainty reduction in self-adaptive systemsProceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems10.1145/3194133.3194144(51-57)Online publication date: 28-May-2018
https://dl.acm.org/doi/10.1145/3194133.3194144
Gaber MAneiba ABasurra SBatty OElmisery AKovalchuk YRehman M(2018)Internet of Things and data mining: From applications to techniques and systemsWIREs Data Mining and Knowledge Discovery10.1002/widm.12929:3Online publication date: 9-Nov-2018
https://doi.org/10.1002/widm.1292
Parenreng JKitagawa A(2017)A Model of Security Adaptation for Limited Resources in Wireless Sensor NetworkJournal of Computer and Communications10.4236/jcc.2017.5300205:03(10-23)Online publication date: 2017
https://doi.org/10.4236/jcc.2017.53002
Han RZhan JLuis J(2016)SARP: Synopsis-Based Approximate Request Processing for Low Latency and Small Correctness Loss in Cloud Online ServicesInternational Journal of Parallel Programming10.1007/s10766-016-0406-944:5(1054-1077)Online publication date: 22-Mar-2016
https://doi.org/10.1007/s10766-016-0406-9
Vildjiounaite EGimel’farb GKyllönen VPeltola J(2015)Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging DomainThe Scientific World Journal10.1155/2015/4348262015:1Online publication date: 10-Sep-2015
https://doi.org/10.1155/2015/434826
Gama J(2015)Challenges in Learning from Streaming Data Extended AbstractICT Innovations 201410.1007/978-3-319-09879-1_1(1-5)Online publication date: 2015
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Rodrigues PGama J(2014)Distributed clustering of ubiquitous data streamsWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11094:1(38-54)Online publication date: 1-Jan-2014
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Cayci AGomes JZanda AMenasalvas EEibe S(2013)Research Challenge of Locally Computed Ubiquitous Data MiningData Mining10.4018/978-1-4666-2455-9.ch101(1960-1978)Online publication date: 2013
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A Framework for Clustering Massive-Domain Data Streams

A Holistic Approach for Resource-aware Adaptive Data Stream Mining

Requirements for clustering data streams

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