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 discussedread more
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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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Online feature extraction based on accelerated kernel principal component analysis for data stream
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Robust PCA in High-dimension: A Deterministic Approach
TL;DR: A deterministic high-dimensional robust PCA algorithm which inherits all theoretical properties of its randomized counterpart, i.e., it is tractable, robust to contaminated points, easily kernelizable, asymptotic consistent and achieves maximal robustness.
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A Paradigm for Learning Queries on Big Data
TL;DR: A novel paradigm for interactive learning of queries on big data, without assuming any knowledge of the database schema is proposed and two instantiations that validated the proposed paradigm for learning relational join queries and for learning path queries on graph databases are presented.