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Data Analytics Service Composition and Deployment on IoT Devices.

Accepted version
Peer-reviewed

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Abstract

Machine Learning (ML) techniques have begun to dominate data analytics applications and services. Recommendation systems are the driving force of online service providers such as Amazon. Finance analytics has quickly adopted ML to harness large volume of data in such areas as fraud detection and risk-management. Deep Neural Network (DNN) is the technology behind voice-based personal assistance, self-driving cars [1], image processing [3], etc. Many popular data analytics are deployed on cloud computing infrastructures. However, they require aggregating users’ data at central server for processing. This architecture is prone to issues such as increased service response latency, communication cost, single point failure, and data privacy concerns.

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Journal Title

MobiSys 2018: The 16th Annual International Conference on Mobile Systems, Applications, and Services

Conference Name

MobiSys 2018: The 16th Annual International Conference on Mobile Systems, Applications, and Services

Journal ISSN

Volume Title

Publisher

Association for Computing Machinery

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Except where otherwised noted, this item's license is described as http://www.rioxx.net/licenses/all-rights-reserved
Sponsorship
EPSRC (via University of Warwick) (RESWM34910001 CONTRIVE)
EPSRC (via Queen Mary University of London (QMUL)) (ECSA1W3R)
Thiswork is funded in part by the EPSRC Databox project (EP/N028260/2), NaaS (EP/K031724/2) and Contrive (EP/N028422/1).