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Scalable Framework for Distributed Case-based Reasoning for Big data analytics
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7305-7169
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
2017 (English)In: 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 2017Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a scalable framework for distributed case-based reasoning methodology to provide actionable knowledge based on historical big amount of data. The framework addresses several challenges, i.e., promptly analyse big data, cross-domain, use-case specific data processing, multi-source case representation, dynamic case-management, uncertainty, check the plausibility of solution after adaptation etc. through its’ five modules architectures. The architecture allows the functionalities with distributed data analytics and intended to provide solutions under different conditions, i.e. data size, velocity, variety etc.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Distributed analytics, case-based reasoning
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-37087OAI: oai:DiVA.org:mdh-37087DiVA: diva2:1153138
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
VDM - Vehicle Driver Monitoring
Available from: 2017-10-27 Created: 2017-10-27 Last updated: 2017-10-27Bibliographically approved

Open Access in DiVA

No full text

Authority records BETA

Barua, ShaibalBegum, ShahinaAhmed, Mobyen Uddin

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CiteExportLink to record
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Citation style
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  • fi-FI
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Output format
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