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Journal ArticleDOI

A Big Data-as-a-Service Framework: State-of-the-Art and Perspectives

TLDR
A tensor-based multiple clustering on bicycle renting and returning data is illustrated, which can provide several suggestions for rebalancing of the bicycle-sharing system and some challenges about the proposed framework are discussed.
Abstract
Due to the rapid advances of information technologies, Big Data, recognized with 4Vs characteristics (volume, variety, veracity, and velocity), bring significant benefits as well as many challenges A major benefit of Big Data is to provide timely information and proactive services for humans The primary purpose of this paper is to review the current state-of-the-art of Big Data from the aspects of organization and representation, cleaning and reduction, integration and processing, security and privacy, analytics and applications, then present a novel framework to provide high-quality so called Big Data-as-a-Service The framework consists of three planes, namely sensing plane, cloud plane and application plane, to systemically address all challenges of the above aspects Also, to clearly demonstrate the working process of the proposed framework, a tensor-based multiple clustering on bicycle renting and returning data is illustrated, which can provide several suggestions for rebalancing of the bicycle-sharing system Finally, some challenges about the proposed framework are discussed

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Citations
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Journal ArticleDOI

A computation offloading method over big data for IoT-enabled cloud-edge computing

TL;DR: A system model and dynamic schedules of data/control-constrained computing tasks are investigated, including the execution time and energy consumption for mobile devices, and NSGA-III (non-dominated sorting genetic algorithm III) is employed to address the multi-objective optimization problem of task offloading in cloud-edge computing.
Journal ArticleDOI

An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles

TL;DR: A privacy preservation method, named ECO, with privacy preservation for IoV is proposed in this paper and NSGA-II (non-dominated sorting genetic algorithm II) is adopted to realize multi-objective optimization to reduce the execution time and energy consumption of ECDs and prevent privacy conflicts of the computing tasks.
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Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

TL;DR: The enabling technologies of big data analytics of manufacturing data are surveyed and discussed and the future directions in this promising area are outlined.
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Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things

TL;DR: This article focuses on the deep-learning-enhanced HAR in IoHT environments, and a semisupervised deep learning framework is designed and built for more accurate HAR, which efficiently uses and analyzes the weakly labeled sensor data to train the classifier learning model.
Journal ArticleDOI

Multi-scale Dense Gate Recurrent Unit Networks for bearing remaining useful life prediction

TL;DR: A novel deep learning network, namely Multi-scale Dense Gate Recurrent Unit Network (MDGRU) is proposed in this paper, which is composed of the feature layers initialized by pre-trained Restricted Boltzmann Machine (RBM) network, multi-scale layers, skip gate recurrent unit layers, dense layers.
References
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Proceedings Article

One-to-Many Voice Conversion Based on Tensor Representation of Speaker Space

TL;DR: A novel approach to flexible control of speaker characteristics using tensor representation of speaker space is described, which can solve an inherent problem of supervector representation, and it improves the performance of voice conversion.
Journal ArticleDOI

Big data visual analytics for exploratory earth system simulation analysis

TL;DR: This paper describes and demonstrates a visual analytics system, called the Exploratory Data analysis ENvironment (EDEN), with specific application to the analysis of complex earth system simulation data sets, and bridges the growing gap between viable visualization techniques and real-world climate analysis.
Journal ArticleDOI

Low rank Tucker-type tensor approximation to classical potentials

TL;DR: In this article, the authors investigated the convergence of the Tucker decomposition with respect to the relative Frobenius norm in higher-order tensors, including inner, outer and Hadamard products, and proposed a mixed CT model based on additive splitting of a tensor as a sum of canonical and Tucker-type representations.
Journal ArticleDOI

Computing non-negative tensor factorizations

TL;DR: An approach for computing the NTF of a dataset that relies only on iterative linear-algebra techniques and that is comparable in cost to the non-negative matrix factorization (NMF) is described.
Proceedings ArticleDOI

SAMOA: a platform for mining big data streams

TL;DR: This talk offers a sneak preview of SAMOA, an upcoming platform for mining dig data streams in a cluster/cloud environment that features a pluggable architecture that allows it to run on several distributed stream processing engines such as S4 and Storm.
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