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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 rolling bearing fault diagnosis method based on hybrid deep learning network model

TL;DR: In this article , a hybrid deep learning network based on multi-scale convolutional neural network-cost-sensitive learning support vector machines (CS-SVM) is developed model, which first converts the bearing vibration signal into a wavelet time-frequency image using continuous wavelet transform, then inputs the wavelet Time-Frequency map and the fast Fourier transformed signal into the multiscale CNN, further extracts the fault features accurately, fuses the data in the output layer, and inputs them into the CS- SVM classifier.
Proceedings ArticleDOI

Combined Navigation Method of RBF Neural Network Based on Quantum Genetic Algorithm in Edge Devices

TL;DR: The combined navigation method of RBF neural network based on the quantum genetic algorithm is put forward and test results show that the algorithm could realize a better precision and robustness than the standard Kalman filtering system.
Posted ContentDOI

BiLSTM_SAE:A Hybrid Deep Learning Framework for Predictive Data Analytics System in Traffic Modeling

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

A multi-sided market of personal data resource allocation: An empirical study of China’s car-hailing platform:

TL;DR: The current rapid development of online car-hailing services creates a serious challenge to the existing paradigm of market governance and antitrust policy as discussed by the authors, however, the debate on the market struct...
Journal Article

T4C: A Framework for Time-Series Clustering-as-a-Service

TL;DR: T4C as mentioned in this paper is an open-source Python-based framework for time-series clustering-as-a-service, which integrates some of the most used time series clustering models and techniques, and it is able to generate on-the-fly websites where users can explore the result of the clustering procedure on their previously uploaded time series.
References
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Matrix computations

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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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