scispace - formally typeset
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

read more

Citations
More filters
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
More filters
Proceedings ArticleDOI

Mining the Big Data: The Critical Feature Dimension Problem.

TL;DR: It is demonstrated that, under generally reasonable assumptions pertaining to feature ranking algorithms, the critical feature dimension are successfully discovered by the empirical method for a number of datasets of various sizes.
Journal ArticleDOI

A utility maximization approach for information-communication tradeoff in Wireless Body Area Networks

TL;DR: The theoretical framework of an economic approach, i.e., network utility maximization, is developed for sensor scheduling under operations cost constraint and it is shown that the compact subset of sensors can be found to provide necessary information for timely and correct diagnoses.
Journal ArticleDOI

Launching an Efficient Participatory Sensing Campaign: A Smart Mobile Device-Based Approach

TL;DR: A novel five-tier framework of participatory sensing is proposed and a trajectory-based strategy for participant recruitment is proposed to enable campaign organizers to identify well-suited participants for data sensing based on a joint consideration of temporal availability, trust, and energy.
Proceedings ArticleDOI

Learning optimal classifier chains for real-time big data mining

TL;DR: This paper proposes online distributed algorithms which can learn how to construct the optimal classifier chain in order to maximize the stream mining performance (i.e. mining accuracy minus cost) based on the dynamically-changing data characteristics.
Proceedings ArticleDOI

Supervised Locally Linear Embedding based dimension reduction for hyperspectral image classification

TL;DR: Comparison of overall classification accuracy and accuracy of each class in different methods shows that the supervised nonlinear feature extraction method contributes more to classification accuracies methods.
Related Papers (5)