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.
Author(s): Wookey Lee, Carson K. Leung, Aziz Nasridinov
Series: Advances in Intelligent Systems and Computing, 899
Publisher: Springer
Year: 2020
Language: English
Pages: 128
City: Cham
Preface
Contents
Analysis of the Effects of Nature and Facility Environmental Attributes on the Cause of Death from Disease
1 Introduction
2 Previous Research
2.1 Analysis of Causes of Illness and Death
2.2 MLRA (Multiple Linear Regression Analysis)
2.3 CART (Classification and Regression Tree)
3 Research Design and Results
3.1 Research Design
3.2 Research Results
4 Conclusion
References
Big Data Computing and Mining in a Smart World
1 Introduction and Related Works
2 Interconnection Between the Physical World and the Cyber World via Frequent Pattern Mining
2.1 Serial Algorithms
2.2 Parallel, Distributed, and High Performance Computing Algorithms
2.3 Fog and Edge Computing Algorithms
3 Interconnection Between the Physical-Cyber Worlds and the Thinking World via Cognitive Mining
3.1 Frequent Pattern Mining-as-a-Service
3.2 Constrained Frequent Pattern Mining via Crowdsourcing
4 Interconnection Between the Physical-Cyber-Thinking Worlds and the Social World via Social Network Analysis
5 Discussion: Mining COVID-19 Data in a Smart World Environment
6 Conclusions
References
Data Science for Big Data Applications and Services: Data Lake Management, Data Analytics and Visualization
1 Introduction and Related Works
2 Big Data Management: Information Fusion and Data Lake
3 Big Data Analytics and Mining
4 Big Data Visualization: Visual Analytics
4.1 Big Data Visualization via Polylines or Orthogonal Wires
4.2 Hierarchical Big Data Visualization
4.3 Orientation-Free Big Data Visualization
4.4 Summary of Comparisons Among Visualizers
5 Discussion: Data Science on COVID-19 Data
6 Conclusions
References
Detection of Editing Bursts and Extraction of Significant Keyphrases from Wikipedia Edit History
1 Introduction
2 Related Work
3 Proposed Method
3.1 Burst Period Detection
3.2 Data Preprocessing
3.3 Keyphrase Extraction Based on TextRanknfidf
4 Experiments and Evaluations
4.1 Datasets
4.2 Results on Burst Period Detection
4.3 Results of Keyphrase Extraction
5 Migration of Editing Activities Between Article Categories
6 Conclusion and Future Work
References
Emotion Detection on Twitter Textual Data
1 Introduction
2 Backgrounds and Related Works
2.1 Preprocessing
2.2 Classification
3 Our Approach Walk Through
4 Evaluation and Analysis
5 Conclusion
References
Factors Affecting an Organization’s Information Security Performance: The Characteristics of Information Security Officers
1 Introduction
2 Literature Review
3 Research Design
3.1 Research Model
3.2 Hypotheses
4 Research Methods and Results
4.1 Study Participants
4.2 Research Method
4.3 Results
5 Conclusion
References
An Empirical Investigation of Customer Loyalty in Chinese Smartphone Markets with Large-Scale Data: Apple, Samsung, and Xiaomi Cases
1 Introduction
2 Literature Review
2.1 Customer Satisfaction and Loyalty (CS&L)
3 Theoretical Development
3.1 The Effect of Customer Satisfaction on Customer Loyalty
3.2 The Effect of Perceived Relative Advantage on Customer Satisfaction
3.3 The Effect of Perceived Emotional Attachment on Customer Satisfaction
3.4 The Effect of Hardware Performance on Perceived Relative Advantage and Perceived Emotional Attachment
3.5 The Effect of Software Quality on Perceived Relative Advantage and Perceived Emotional Attachment
3.6 The Effect of Service Quality on Perceived Relative Advantage and Perceived Emotional Attachment
4 Data Analysis
4.1 Data Collection
4.2 Measurement
4.3 PLS for Data Analysis
5 Results
5.1 Descriptive Analysis
5.2 Test of Measurement Model
5.3 Test of Structural Model
5.4 Brand Comparison
5.5 Additional Analysis: Switching Brands
6 Contributions and Limitations
References
Vertical Data Mining from Relational Data and Its Application to COVID-19 Data
1 Introduction and Related Works
2 Vertical Frequent Pattern Mining from Precise Data
2.1 The Eclat Algorithm
2.2 The dEclat Algorithm
2.3 The VIPER Algorithm
2.4 A Hybrid Algorithm
3 Vertical Frequent Pattern Mining from Uncertain Data
3.1 The UV-Eclat Algorithm
3.2 The U-VIPER Algorithm
4 Improvements to Vertical Frequent Pattern Mining from Uncertain Data
4.1 An Improved UV-Eclat Algorithm
4.2 An Improved U-VIPER Algorithm
5 Case Studies: Vertical Mining from Relational Data
5.1 Mining Epidemiological Data on COVID-19 Cases
5.2 Mining Spatio-Economic Data
6 Conclusions
References
Author Index
