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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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Proceedings ArticleDOI

Research on Master-Slave Distributed Large-Scale Poultry Farming Measurement and Control System

TL;DR: A master-slave distributed large-scale poultry farming measurement and control system with a network model adopting a total star-shaped structure, a cloud service monitoring center manages multiple lower-machine monitoring sub-stations, which overcomes the problem of insufficient control force, eliminates the hidden danger in manual management, expands the control scope, and improves the automatic control ability of poultry house environment.
Journal ArticleDOI

Edge–Cloud-Aided Differentially Private Tucker Decomposition for Cyber–Physical–Social Systems

TL;DR: In this article , a novel edge-cloud-aided differentially private Tucker decomposition scheme is proposed to avert data owner's private data from being learned by other data owners, untrusted edge, and cloud during tucker decomposition for CPSS.
Journal ArticleDOI

NMF based image sequence analysis and its application in gait recognition

TL;DR: Experimental results show that this method can achieve high recognition accuracy, and has a strong adaptability in cross-view and multi-clothes conditions, which means it can better adapt to the real environment.
Journal ArticleDOI

Mutual Informative MapReduce and Minimum Quadrangle Classification for Brain Tumor Big Data

TL;DR: In this paper , a framework called mutual informative MapReduce and minimum quadrangle classification (MIMR-MQC) is introduced for brain tumor detection to handle challenges associated with big data classification.
Proceedings ArticleDOI

A Blockchain Based Fast Authentication Framework for IoT Networks with Trusted Hardware

TL;DR: In this paper, a blockchain based authentication framework for IoT applications is presented, which achieves sufficient security provision such as identity anonymity, perfect secrecy, traceability, and resistance to common attacks.
References
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Book

Matrix computations

Gene H. Golub
Journal ArticleDOI

MapReduce: simplified data processing on large clusters

TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Journal ArticleDOI

MapReduce: simplified data processing on large clusters

TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
Journal ArticleDOI

Nonlinear dimensionality reduction by locally linear embedding.

TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Journal ArticleDOI

Learning the parts of objects by non-negative matrix factorization

TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.
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