Big Data Analyses, Services, and Smart Data

Author:   Wookey Lee ,  Carson K. Leung ,  Aziz Nasridinov
Publisher:   Springer Verlag, Singapore
Edition:   2021 ed.
Volume:   899
ISBN:  

9789811587337


Pages:   117
Publication Date:   12 September 2021
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Big Data Analyses, Services, and Smart Data


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Author:   Wookey Lee ,  Carson K. Leung ,  Aziz Nasridinov
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Verlag, Singapore
Edition:   2021 ed.
Volume:   899
Weight:   0.209kg
ISBN:  

9789811587337


ISBN 10:   9811587337
Pages:   117
Publication Date:   12 September 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Prof. Wookey Lee.  He received his B.S., M.S., and Ph.D. degrees from Seoul National University, South Korea, and his M.S.E. degree from Carnegie Mellon University, USA. Currently, he is the director of VOICE AI institute and a professor at Inha University, South Korea, and has been a visiting professor in Dept. of Computer Science, University of British Columbia, Canada. He won the best paper awards in IEEE TCSC, KORMS, KOSIPER, and KIISE. He currently is the editor-in-chief of the Journal of Information Technology and Architecture, and Journal of Big Data Service, also an associate editor of World Wide Web Journal, Supercomputing, Cluster Computing, Sensors, etc. He has served as the president of Korea DataBase Society and an executive committee member of IEEE TCDE. He has participated as a steering committee of BigComp, and a PC member for APWeb, BigComp, CIKM, IEEE DEST, iiWAS, ITA, and WAIM, and an Organizing Committee member for DASFAA,EA, SRDS, and VLDB, etc. His research interests are Deep Learning, graph search, Voice-informatics, patent information, mobile and Data Warehousing, etc. Carson Leung is currently a Full Professor at the University of Manitoba, Canada. He obtained his B.Sc. (Hons.), M.Sc. and Ph.D. from the University of British Columbia, Canada. He has published more than 250 papers on the topics of databases, data mining, big data computing, social network analysis, as well as visual analytics—including papers in ACM Transactions on Database Systems (TODS), Social Network Analysis and Mining (SNAM), Future Generation Computer Systems (FGCS), Journal of Organizational Computing and Electronic Commerce (JOCEC), IEEE International Conference on Data Engineering (ICDE), IEEE International Conference on Data Mining (ICDM), and Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Over the past few years, he has served on (i) the Steering Committee of IEEE SmartData 2020, (ii) the Advisory Committee of IEEE CBDCom 2019, (iii) the Organizing Committee of ACM SIGMOD 2008, IEEE/ACM ASONAM 2014, ACM CIKM 2018, IEEE BigComp 2020, IEEE DSAA 2020, and IEEE ICDM 2020. He has also served as (i) a General Chair of IEEE CBDCom 2016 and IEEE SmartData 2018, (ii) a Program Chair of IEEE CBDCom 2020 and IDEAS 2020, as well as (iii) a PC member of numerous international conferences including ACM KDD and ECML/PKDD. He is a senior member of both ACM and IEEE. Prof. Aziz Nasridinov is currently an Associate Professor of computer science with Chungbuk National University. He received the B.Sc. degree from the Tashkent University of Information Technologies in 2006 and the M.Sc. and Ph.D. degrees from Dongguk University in 2009 and 2012, respectively. Prof. Aziz Nasridinov has published over 20 papers in various high-ranked international journals and conferences. He has also served as a program committee member and co-organizer for numerous top-tier conferences, including ACM SAC, IEEE Big Data, and IEEE Globecom, and also served in the editorial board of several international journals. His research interests include traditional databases, big data analytics with machine learning, and distributed processing system applications.

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