Web Information Systems Engineering - WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I

Web Information Systems Engineering - WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I

Web Information Systems Engineering - WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I

Web Information Systems Engineering - WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I

eBook1st ed. 2016 (1st ed. 2016)

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Overview

This two volume set LNCS 10041 and LNCS 10042 constitutes the proceedings of the 17th International Conference on Web Information Systems Engineering, WISE 2016, held in Shanghai, China, in November 2016.

The 39 full papers and 31 short papers presented in these proceedings were carefully reviewed and selected from 233 submissions. The papers cover a wide range of topics such as Social Network Data Analysis; Recommender Systems; Topic Modeling; Data Diversity; Data Similarity; Context-Aware Recommendation; Prediction; Big Data Processing; Cloud Computing; Event Detection; Data Mining; Sentiment Analysis; Ranking in Social Networks; Microblog Data Analysis; Query Processing; Spatial and Temporal Data; Graph Theory; Non-Traditional Environments; and Special Session on Data Quality and Trust in Big Data.


Product Details

ISBN-13: 9783319487403
Publisher: Springer-Verlag New York, LLC
Publication date: 11/01/2016
Series: Lecture Notes in Computer Science , #10041
Sold by: Barnes & Noble
Format: eBook
File size: 10 MB

Table of Contents

Social Network Data Analysis.- Recommender Systems.- Topic Modeling.- Data Diversity.- Data Similarity.- Context-Aware Recommendation.- Prediction.- Big Data Processing.- Cloud Computing.- Event Detection.- Data Mining.- Sentiment Analysis.- Ranking in Social Networks.- Microblog Data Analysis.- Query Processing.- Spatial and Temporal Data.- Graph Theory.- Non-Traditional Environments.- and Special Session on Data Quality and Trust in Big Data.

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