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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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Journal ArticleDOI

Ontology Based Information Retrieval in Semantic Web: A Survey

TL;DR: Semantic Web (SW) is a well defined portal that helps in extracting relevant information using many Information Retrieval (IR) techniques, and use of Ontology also contributes in building new generation of web- Semantic Web.
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Near-Lossless Multichannel EEG Compression Based on Matrix and Tensor Decompositions

TL;DR: A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) based on matrix/tensor decomposition models that achieves attractive compression ratios compared to compressing individual channels separately.
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A tensor-based big data model for QoS improvement in software defined networks

TL;DR: A novel tensor-based model for efficient provisioning of QoS in software defined networks is developed and a transition tensor model for network traffic prediction and QoS provisioning is introduced.
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Semi-supervised kernel canonical correlation analysis with application to human fMRI

TL;DR: This study implemented various supervised and semi-supervised versions of KCCA on human fMRI data, with regression to single and multi-variate labels (corresponding to video content subjects viewed during the image acquisition), and found that in each variate condition, the semi- supervised variants of K CCA performed better than the supervised variants, including a supervised variant with Laplacian regularization.
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Representation of functions on big data: Graphs and trees

TL;DR: A new system of orthogonal functions on weighted trees is developed that is local, easily implementable, and allows for scalable approximations without saturation.
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