Data Analytics Service Composition and Deployment on IoT Devices.
Zhao, Jianxin R
MobiSys 2018: The 16th Annual International Conference on Mobile Systems, Applications, and Services
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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
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 , image processing , 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.
Thiswork is funded in part by the EPSRC Databox project (EP/N028260/2), NaaS (EP/K031724/2) and Contrive (EP/N028422/1).
EPSRC (via University of Warwick) (RESWM34910001 CONTRIVE)
EPSRC (via Queen Mary University of London (QMUL)) (ECSA1W3R)
External DOI: https://doi.org/10.1145/3210240.3223570
This record's URL: https://www.repository.cam.ac.uk/handle/1810/279876