UID:
almahu_9948612949402882
Format:
IX, 117 p. 27 illus., 19 illus. in color.
,
online resource.
Edition:
1st ed. 2021.
ISBN:
9789811587313
Series Statement:
Advances in Intelligent Systems and Computing, 899
Content:
This book covers topics like big data analyses, services, and smart data. It contains (i) invited papers, (ii) selected papers from the Sixth International Conference on Big Data Applications and Services (BigDAS 2018), as well as (iii) extended papers from the Sixth IEEE International Conference on Big Data and Smart Computing (IEEE BigComp 2019). The aim of BigDAS is to present innovative results, encourage academic and industrial interaction, and promote collaborative research in the field of big data worldwide. BigDAS 2018 was held in Zhengzhou, China, on August 19-22, 2018, and organized by the Korea Big Data Service Society and TusStar. The goal of IEEE BigComp, initiated by Korean Institute of Information Scientists and Engineers (KIISE), is to provide an international forum for exchanging ideas and information on current studies, challenges, research results, system developments, and practical experiences in the emerging fields of big data and smart computing. IEEE BigComp 2019 was held in Kyoto, Japan, on February 27-March 02, 2019, and co-sponsored by IEEE and KIISE.
Note:
Chapter 1. Analysis of the Effects of Nature and Facility Environmental Attributes on the Cause of Death from Disease -- Chapter 2. Big Data Computing and Mining in a Smart World -- Chapter 3. Data Science for Big Data Applications and Services: Data Lake Management, Data Analytics and Visualization -- Chapter 4. Detection of Editing Bursts and Extraction of Significant Keyphrases from Wikipedia Edit History -- Chapter 5. Emotion Detection on Twitter Textual Data -- Chapter 6. Factors Affecting an Organization's Information Security Performance: The Characteristics of Information Security Officers -- Chapter 7. Living Labs as the Methodology: Using for Verifying Certification Criteria of New Convergence Products -- Chapter 8. Vertical Data Mining from Relational Data and Its Application to COVID-19.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9789811587306
Additional Edition:
Printed edition: ISBN 9789811587320
Additional Edition:
Printed edition: ISBN 9789811587337
Language:
English
DOI:
10.1007/978-981-15-8731-3
URL:
https://doi.org/10.1007/978-981-15-8731-3
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