Data Analytics Service Composition and Deployment on IoT Devices.
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Editors
Ott, J
Dressler, F
Saroiu, S
Dutta, P
Publication Date
2018Journal Title
MobiSys
Conference Name
MobiSys 2018: The 16th Annual International Conference on Mobile Systems, Applications, and Services
Publisher
ACM
Pages
502-504
Type
Conference Object
Metadata
Show full item recordCitation
Zhao, J. R., Tiplea, T., Mortier, R., Crowcroft, J., & Wang, L. (2018). Data Analytics Service Composition and Deployment on IoT Devices.. MobiSys, 502-504. https://doi.org/10.1145/3210240.3223570
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.
Sponsorship
Thiswork is funded in part by the EPSRC Databox project (EP/N028260/2),
NaaS (EP/K031724/2) and Contrive (EP/N028422/1).
Funder references
EPSRC (via University of Warwick) (RESWM34910001 CONTRIVE)
EPSRC (via Queen Mary University of London (QMUL)) (ECSA1W3R)
Identifiers
External DOI: https://doi.org/10.1145/3210240.3223570
This record's URL: https://www.repository.cam.ac.uk/handle/1810/279876
Rights
Licence:
http://www.rioxx.net/licenses/all-rights-reserved
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