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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 New Approach Based on Parallel Probabilistic to Factorize a Semiprime

Jianhui Li
TL;DR: A method for finding small divisors of semi-prime numbers by parallel computing, which combines deterministic search with probabilistic search, requires less memory and can be implemented on a common multi-core computer.
Proceedings ArticleDOI

RPCC: A Replica Placement Method to Alleviate the Replica Consistency under Dynamic Cloud

TL;DR: Wang et al. as mentioned in this paper designed and implemented a dynamic replica placement method based on the node's credit risk, named RPCC, aiming to use replica placement to balance the relationship between system performance and replica consistency.
Proceedings ArticleDOI

NMF-Based Privacy-Preserving Collaborative Filtering on Cloud Computing

TL;DR: A hybrid algorithm based on NMF and random perturbation technology is proposed, which implements the recommendation system and solves the protection problem of user privacy data in the recommendation process on cloud computing.
Journal Article

The recent survey on data mining verus bigdata applications on various field

TL;DR: This paper concentrates on different tools and applications in the field of big data and data mining, and focuses on various file system used in bigdata and datamining application areas.
Proceedings ArticleDOI

Data Trading for Blockchain-Based Data Market in Cyber-Physical-Social Smart Systems

TL;DR: Wang et al. as mentioned in this paper proposed a blockchain-based data market model to enable data trading between the CPS3 operator and CPS3 users, which is formulated as two kinds of non-cooperative games, namely, independent optimization problem and competition-enhanced optimization problem.
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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