Big Data

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Details

This book constitutes the proceedings of the 7th CCF Conference on Big Data, BigData 2019, held in Wuhan, China, in October 2019.

The 30 full papers presented in this volume were carefully reviewed and selected from 324 submissions. They were organized in topical sections as follows: big data modelling and methodology; big data support and architecture; big data processing; big data analysis; and big data application.


Inhalt
Big Data Modelling and Methodology.- A constrained self-adaptive sparse combination representation method for abnormal event detection.- A Distributed Scheduling Framework of Service based ETL Process.- A Probabilistic Soft Logic Reasoning Model with Automatic Rule Learning.- Inferring How Novice Students Learn to Code: Integrating Automated Program Repair with Cognitive Model.- Predicting Friendship Using a Unified Probability Model.- Product Feature Extraction via Topic Model and Synonym Recognition Approach.- Vietnamese Noun Phrase Chunking Based on BiLSTM-CRF Model and Constraint Rules.- Big Data Support and Architecture.- A Distributed Big Data Discretization Algorithm under Spark.- A Novel Distributed Duration-aware LSTM for Large Scale Sequential Data Analysis.- Considering User Distribution and Cost Awareness to Optimize Server Deployment.- Convolutional Neural Networks on EEG-based Emotion Recognition.- Distributed Subgraph Matching Privacy Preserving Method for Dynamic SocialNetwork.- Big Data Processing.- Clustering-Anonymization-Based Differential Location Privacy Preserving Protocol in WSN.- Distributed Graph Perturbation Algorithm on Social Networks with Reachability Preservation.- Minimum Spanning Tree Clustering Based on Density Filtering.- Research of CouchDB Storage Plugin for Big Data Query Engine Apache Drill.- Visual Saliency Based on Two-Dimensional Fractional Fourier Transform.- Big Data Analysis.- An Information Sensitivity Inference Method for Big Data Aggregation Based on Granular Analysis.- Attentional Transformer Networks for Target-Oriented Sentiment Classification.- Distributed Logistic Regression for Separated Massive Data.- Similarity Evaluation on Labeled Graphs via Hierarchical Core Decomposition.- Weighted Multi-label Learning with Rank Preservation.- Orthogonal Graph Regularized Nonnegative Matrix Factorization for Image Clustering.- Big Data Application.- Characteristics of Patient Arrivals and Service Utilization in OutpatientDepartments.- College Academic Achievement Early Warning Prediction based on Decision Tree Model.- Intelligent Detection of Large-scale KPI Streams Anomaly Based on Transfer learning.- Latent Feature Representation for Cohesive Community Detection based on Convolutional Auto-Encoder.- Research on Monitoring Method of Ethylene Oxide Process by Improving C4.5 Algorithm.- Web API Recommendation with Features Ensemble and Learning-to-Rank.- EagleQR: An Application in Accessing Printed Text for the Elderly and Low Vision People.

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09789811518980
    • Editor Hai Jin, Xuemin Lin, Yihua Huang, Xuanhua Shi, Nong Xiao, Xueqi Cheng
    • Sprache Englisch
    • Auflage 1st edition 2019
    • Größe H235mm x B155mm x T25mm
    • Jahr 2019
    • EAN 9789811518980
    • Format Kartonierter Einband
    • ISBN 981151898X
    • Veröffentlichung 28.11.2019
    • Titel Big Data
    • Untertitel 7th CCF Conference, BigData 2019, Wuhan, China, September 26-28, 2019, Proceedings
    • Gewicht 674g
    • Herausgeber Springer Nature Singapore
    • Anzahl Seiten 448
    • Lesemotiv Verstehen
    • Genre Informatik

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