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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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Journal ArticleDOI

A Study of College English Culture Intelligence-Aided Teaching System and Teaching Pattern

Cao Meng-yue, +2 more
- 24 Feb 2020 - 
TL;DR: In this article, an intelligence-aided system for college English cultural teaching is proposed, aiming to extend the depth and width of the application of modern information technology in college English culture teaching.
Journal ArticleDOI

Improved Multi-Order Distributed HOSVD with Its Incremental Computing for Smart City Services

TL;DR: Tree-based Ring algorithm and Tree-based Tree algorithm are proposed for the problems of increasing scale of processable data and computational efficiency, as an extension of multi-order distributed and incremental High Order Singular Value Decomposition (HOSVD) computing.
Journal ArticleDOI

Using Big Data Fuzzy K-Means Clustering and Information Fusion Algorithm in English Teaching Ability Evaluation

Chen Zhen
- 05 Feb 2021 - 
TL;DR: In this article, an English teaching ability evaluation algorithm based on big data fuzzy K-means clustering and information fusion is proposed to improve the accuracy of teacher evaluation and the efficiency of teaching resources allocation.
Journal ArticleDOI

A Distributed Hierarchical Deep Computation Model for Federated Learning in Edge Computing

TL;DR: A novel Distributed Hierarchical Tensor Deep Computation Model (DHT-DCM) is proposed by condensing the model parameters from a high-dimensional tensor space into a set of low-dimensional subspaces to reduce the bandwidth consumption and storage requirement for federated learning.
Journal ArticleDOI

Coding-Based Large-Scale Task Assignment for Industrial Edge Intelligence

TL;DR: A generative-coding group evolution algorithm, which involves a coding-based operator to approximate different sorts of evolutionary operators, is proposed in this paper and is able to provide near-optimal solutions for large-scale tasks within a very short time compared with some traditional approaches.
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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