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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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Proceedings ArticleDOI

A General Framework for Adjustable Neighbor Discovery in Wireless Sensor Networks

TL;DR: This paper proposes a general framework to handle the problem, then presents two distributed algorithms that can ensure the discovery between the neighboring nodes, no matter when they start and what duty cycle they choose, which is referred to as adjustable neighbor discovery.
Journal ArticleDOI

A Structured Approach Towards Big Data Identification

TL;DR: In this article , a structured approach is presented for identification of big data, based on three equations that categorize the Volume, Velocity, and Variety characteristics by relating data, application, and platform properties.
Posted ContentDOI

BiLSTM_SAE:A Hybrid Deep Learning Framework for Efficient Predictive Big Data Analytics System

TL;DR: In this article , a hybrid technique of BiLSTM-SAE has been proposed for business big data analytics, which is an advanced version of the conventional LSTM approach.
Journal ArticleDOI

Differentially Private Tensor Deep Computation for Cyber–Physical–Social Systems

TL;DR: A deep private tensor autoencoder (dPTAE), where tensors are used for data representation, and differential privacy guarantees strong privacy is proposed, to enforce differential privacy through noise injection into the objective functions instead of the results they produce.
Proceedings ArticleDOI

A big data-as-a-service architecture for naturalistic driving studies

TL;DR: In this article, a hybrid architecture based on big data-as-a-service (BDaaS) for naturalistic driving studies (NDS) is proposed to handle all aspects of big data challenges in NDS and inherently eases the deployment and maintenance of such systems.
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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