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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 FrameBuffer Oriented Graphical Human-Machine Interaction Mechanism for Intelligent In-Vehicle Systems

TL;DR: The extensive measurement results show that the framebuffer-based HiveGUI system usually consumes very few memory resources and low CPU occupancy in the self-developed intelligent in-vehicle system, and thus can offer good experience for automobile users.
Proceedings ArticleDOI

Patch-Wise Normalization for Pose-Invariant Face Recognition from Single Sample

TL;DR: In this paper, a patch-wise normalization method for non-frontal face images is proposed which is practical, effective, and can work well in continuous poses.
Book ChapterDOI

RETRACTED CHAPTER: A Cooperative Placement Method for Machine Learning Workflows and Meteorological Big Data Security Protection in Cloud Computing

TL;DR: In this paper, a collaborative placement method and a two-factor-based protection mechanism for machine-learning workflows and big data security protection is proposed, where the non-dominated sorting differential evolution (NSDE) technique is employed to realize joint optimization of data access time, energy efficiency and load balance.
Proceedings ArticleDOI

Optimal Model Design for the Cyber-Insurance Contract with Asymmetric Information

TL;DR: This work divides network users into high risk type and low risk type according to the security levels and proposes a user risk probability model under the condition of interdependent security and correlated risks and proves the validity of the model by numerical examples.
Posted Content

Convolutional Neural Networks with Transformed Input based on Robust Tensor Network Decomposition.

TL;DR: It is shown that tensor networks can systematically partition structured data, e.g. color images, for distributed storage and communication in privacy-preserving manner and a theory for adversarial examples that mislead convolutional neural networks to misclassification is proposed based on singular value decomposition (SVD).
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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