Social Network Based Big Data Analysis and Applications (eBook)

Artikelnummer: 978-3-319-78196-9
Einband: PDF
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This book is a timely collection of chapters that present the state of the art within the analysis and application of big data. Working within the broader context of big data, this text focuses on the hot topics of social network modelling and analysis such as online dating recommendations, hiring practices, and subscription-type prediction in mobile phone services. 

Manuscripts are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'2016), which was held in August 2016. 

The papers were among the best featured at the meeting and were then improved and extended substantially. Social Network Based Big Data Analysis and Applications will appeal to students and researchers in the field.


This book is a timely collection of chapters that present the state of the art within the analysis and application of big data. Working within the broader context of big data, this text focuses on the hot topics of social network modelling and analysis such as online dating recommendations, hiring practices, and subscription-type prediction in mobile phone services. 

Manuscripts are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'2016), which was held in August 2016. 

The papers were among the best featured at the meeting and were then improved and extended substantially. Social Network Based Big Data Analysis and Applications will appeal to students and researchers in the field.


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VerlagSpringer International Publishing
EinbandPDF
Erscheinungsjahr2018
Seitenangabe249 S.
AusgabekennzeichenEnglisch
AbbildungenX, 249 p. 89 illus., 77 illus. in color.
Auflage1st ed. 2018
PlattformPDF
ReiheLecture Notes in Social Networks
AutorKaya, Mehmet (Hrsg.) / Kawash, Jalal (Hrsg.) / Khoury, Suheil (Hrsg.) / Day, Min-Yuh (Hrsg.)

Alle Bände der Reihe "Lecture Notes in Social Networks"

Über den Autor Mehmet (Hrsg.) Kaya

Mehmet Kaya received the Ph.D. degree from Firat University, in 2003. During 2002 and 2003, he was a Visiting Scholar with the ADSA Laboratory, Department of Computer Science, University of Calgary, Canada. He is currently a Professor in the Department of Computer Engineering, Firat University. He has published over 100 papers in refereed international journals and conferences. His research interests include data mining, machine learning, natural language processing and deep learning. Sleiman Alhajj is currently at the International School of Medicine at Istanbul Medipol University, Istanbul, Turkey. He is a member of the medical data management and analysis group. He served as program chair of HI-BI-Bi 2023. His research interests include cancer, infectious diseases, network analysis, and medical image analysis. Kashfia Sailunaz completed her MSc and PhD in Computer Science at the University of Calgary, Alberta, Canada. She is currently a research associate at the University of Calgary, Alberta, Canada. Her research interests include machine learning, social media analysis, and medical image analysis. Dr. Min-Yuh Day is a Professor in the Graduate Institute of Information Management at National Taipei University, Taiwan. Prior to joining the faculty at NTPU in 2020, he was an Associate Professor in the Department of Information Management at Tamkang University, Taiwan. He was a Postdoctoral Fellow in the Intelligent Agent Systems Lab, Institute of Information Science, Academia Sinica, Taiwan. He received the Ph.D. degree from the Department of Information Management at National Taiwan University, Taiwan. He received his MBA in Management Information System from Tamkang University, Taiwan. His current research interests include electronic commerce, financial technology, artificial intelligence, big data analytics, data mining and text mining, social media marketing, information systems evaluation, question answering systems, and biomedical informatics.

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