This book constitutes the refereed proceedings of the 17th International Conference on Knowledge Management in Organisations, KMO 2023, held in Bangkok, Thailand, during July 24–27, 2023. The 32 full papers included in this book were carefully reviewed and selected from 73 submissions. They were organized in topical sections as follows: Knowledge Transfer & Sharing, Knowledge in Business & Organisation, Digital Transformation and Innovation, Data Analysis and Science, KM and Education, Knowledge Management Process and Model, Information & Knowledge Systems, IT &New Trends in KM, Healthcare.

This book contains the refereed proceedings of the 14th International Conference on Knowledge Management in Organizations, KMO 2019, held in Zamora, Spain, in July 2019. The 46 papers accepted for KMO 2019 were selected from 109 submissions and are organized in topical sections on: knowledge management models and analysis; knowledge transfer and learning; knowledge and service innovation; knowledge creation; knowledge and organization; information systems and information science; data mining and intelligent science; social networks and social aspects of KM; big data and IoT; and new trends in IT.

This book contains the refereed proceedings of the 13th International Conference on Knowledge Management in Organizations, KMO 2018, held in Zilina, Slovakia, in August 2018. The theme of the conference was "Emerging Research for Knowledge Management in Organizations."The 59 papers accepted for KMO 2018 were selected from 141 submissions and are organized in topical sections on: Knowledge management models and analysis; knowledge sharing; knowledge transfer and learning; knowledge and service innovation; knowledge creation; knowledge and organization; information systems and information science; knowledge and technology management; data mining and intelligent science; business and customer relationship management; big data and IoT; and new trends in IT.

This book contains the refereed proceedings of the 12th International Conference on Knowledge Management in Organizations, KMO 2017, held in Beijing, China, in August 2017. The theme of the conference was "Emerging Technology and Knowledge Management in Organizations." The 45 contributions accepted for KMO 2017 were selected from 112 submissions and are organized in topical sections on: Knowledge Management Models and Behaviour Studies; Knowledge Sharing; Knowledge Transfer and Learning; Knowledge and Service Innovation; Knowledge and Organization; Information Systems Research; Value Chain and Supply Chain; Knowledge Re-presentation and Reasoning; Data Mining and Intelligent Science; Big Data Management; Internet of Things and Network.

The proceedings from the  eighth KMO conference represent the findings of this international meeting which brought together researchers and developers from industry and the academic world to report on the latest scientific and technical advances on knowledge management in organizations. This conference provided an international forum for authors to present and discuss research focused on the role of knowledge management for innovative services in industries, to shed light on recent advances in social and big data computing for KM as well as to identify future directions for researching the role of knowledge management in service innovation and how cloud computing can be used to address many of the issues currently facing KM in academia and industrial sectors.

Proceedings from the 2013 LTEC conference in Kaohsiung, Taiwan. The papers examine diverse aspects of Learning Technology for Education in Cloud environments, including social, technical and infrastructure implications. Also addressed is the question of how cloud computing can be used to design applications to support real time on demand learning using technologies. The workshop proceedings provide opportunities for delegates to discuss the latest research in TEL (Technology Enhanced Learning) and its impacts for learners and institutions, using cloud technologies.

These papers on Intelligent Data Analysis and Management (IDAM) examine issues related to the research and applications of Artificial Intelligence techniques in data analysis and management across a variety of disciplines. The papers derive from the 2013 IDAM conference in Kaohsiung ,Taiwan. It is an interdisciplinary research field involving academic researchers in information technologies, computer science, public policy, bioinformatics, medical informatics, and social and behavior studies, etc. The techniques studied include (but are not limited to): data visualization, data pre-processing, data engineering, database mining techniques, tools and applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing, etc.

This book contains the refereed proceedings of the 10th International Conference on Knowledge Management in Organizations, KMO 2015, held in Maribor, Slovenia, in August 2015. The theme of the conference was "Knowledge Management and Internet of Things." The KMO conference brings together researchers and developers from industry and academia to discuss how knowledge management using big data can improve innovation and competitiveness. The 59 contributions accepted for KMO 2015 were selected from 163 submissions and are organized in topical sections on: knowledge management processes, successful knowledge sharing and knowledge management practices, innovations for competitiveness, knowledge management platforms and tools, social networks and mining techniques, knowledge management and the Internet of Things, knowledge management in health care, and knowledge management in education and research.

This book contains the refereed proceedings of the 9th International Conference on Knowledge Management in Organizations (KMO) held in Santiago, Chile, during September 2014. The theme of the conference is "Knowledge Management to Improve Innovation and Competitiveness through Big Data." The KMO conference brings together researchers and developers from industry and academia to discuss and research how knowledge management using big data can improve innovation and competitiveness. The 39 contributions accepted for KMO 2014 were selected from 89 submissions and are organized in sections on: big data and knowledge management, knowledge management practice and case studies, information technology and knowledge management, knowledge management and social networks, knowledge management in organizations, and knowledge transfer, sharing and creation.

The proceedings from the eighth KMO conference represent the findings of this international meeting which brought together researchers and developers from industry and the academic world to report on the latest scientific and technical advances on knowledge management in organizations. This conference provided an international forum for authors to present and discuss research focused on the role of knowledge management for innovative services in industries, to shed light on recent advances in social and big data computing for KM as well as to identify future directions for researching the role of knowledge management in service innovation and how cloud computing can be used to address many of the issues currently facing KM in academia and industrial sectors.

Proceedings from the 2013 LTEC conference in Kaohsiung, Taiwan. The papers examine diverse aspects of Learning Technology for Education in Cloud environments, including social, technical and infrastructure implications. Also addressed is the question of how cloud computing can be used to design applications to support real time on demand learning using technologies. The workshop proceedings provide opportunities for delegates to discuss the latest research in TEL (Technology Enhanced Learning) and its impacts for learners and institutions, using cloud technologies.

These papers on Intelligent Data Analysis and Management (IDAM) examine issues related to the research and applications of Artificial Intelligence techniques in data analysis and management across a variety of disciplines. The papers derive from the 2013 IDAM conference in Kaohsiung ,Taiwan. It is an interdisciplinary research field involving academic researchers in information technologies, computer science, public policy, bioinformatics, medical informatics, and social and behavior studies, etc. The techniques studied include (but are not limited to): data visualization, data pre-processing, data engineering, database mining techniques, tools and applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing, etc.

Mining social networks has now becoming a very popular research area not only for data mining and web mining but also social network analysis. Data mining is a technique that has the ability to process and analyze large amount of data and by this to discover valuable information from the data. In recent year, due to the growth of social communications and social networking websites, data mining becomes a very important and powerful technique to process and analyze such large amount of data. Thus, this book will focus upon Mining and Analyzing social network. Some chapters in this book are extended from the papers that presented in MSNDS2009 (the First International Workshop on Mining Social Networks for Decision Support) and SNMABA2009 ((The International Workshop on Social Networks Mining and Analysis for Business Applications)). In addition, we also sent invitations to researchers that are famous in this research area to contribute for this book. The chapters of this book are introduced as follows: In chapter 1-Graph Model for Pattern Recognition in Text, Qin Wu et al. present a novel approach that uses a weighted directed multigraph for text pattern recognition. In the proposed methodology, a weighted directed multigraph model has been set up by using the distances between the keywords as the weights of arcs as well a keyword-frequency distance based algorithm has also been introduced. Case studies are also included in this chapter to show the performance is better than traditional means.

Web mining has become a popular area of research, integrating the different research areas of data mining and the World Wide Web. According to the taxonomy of Web mining, there are three sub-fields of Web-mining research: Web usage mining, Web content mining and Web structure mining. These three research fields cover most content and activities on the Web. With the rapid growth of the World Wide Web, Web mining has become a hot topic and is now part of the mainstream of Web - search, such as Web information systems and Web intelligence. Among all of the possible applications in Web research, e-commerce and e-services have been iden- fied as important domains for Web-mining techniques. Web-mining techniques also play an important role in e-commerce and e-services, proving to be useful tools for understanding how e-commerce and e-service Web sites and services are used, e- bling the provision of better services for customers and users. Thus, this book will focus upon Web-mining applications in e-commerce and e-services. Some chapters in this book are extended from the papers that presented in WMEE 2008 (the 2nd International Workshop for E-commerce and E-services). In addition, we also sent invitations to researchers that are famous in this research area to contr- ute for this book. The chapters of this book are introduced as follows: In chapter 1, Peter I.

Web mining has become a popular area of research, integrating the different research areas of data mining and the World Wide Web. According to the taxonomy of Web mining, there are three sub-fields of Web-mining research: Web usage mining, Web content mining and Web structure mining. These three research fields cover most content and activities on the Web. With the rapid growth of the World Wide Web, Web mining has become a hot topic and is now part of the mainstream of Web - search, such as Web information systems and Web intelligence. Among all of the possible applications in Web research, e-commerce and e-services have been iden- fied as important domains for Web-mining techniques. Web-mining techniques also play an important role in e-commerce and e-services, proving to be useful tools for understanding how e-commerce and e-service Web sites and services are used, e- bling the provision of better services for customers and users. Thus, this book will focus upon Web-mining applications in e-commerce and e-services. Some chapters in this book are extended from the papers that presented in WMEE 2008 (the 2nd International Workshop for E-commerce and E-services). In addition, we also sent invitations to researchers that are famous in this research area to contr- ute for this book. The chapters of this book are introduced as follows: In chapter 1, Peter I.

Mining social networks has now becoming a very popular research area not only for data mining and web mining but also social network analysis. Data mining is a technique that has the ability to process and analyze large amount of data and by this to discover valuable information from the data. In recent year, due to the growth of social communications and social networking websites, data mining becomes a very important and powerful technique to process and analyze such large amount of data. Thus, this book will focus upon Mining and Analyzing social network. Some chapters in this book are extended from the papers that presented in MSNDS2009 (the First International Workshop on Mining Social Networks for Decision Support) and SNMABA2009 ((The International Workshop on Social Networks Mining and Analysis for Business Applications)). In addition, we also sent invitations to researchers that are famous in this research area to contribute for this book. The chapters of this book are introduced as follows: In chapter 1-Graph Model for Pattern Recognition in Text, Qin Wu et al. present a novel approach that uses a weighted directed multigraph for text pattern recognition. In the proposed methodology, a weighted directed multigraph model has been set up by using the distances between the keywords as the weights of arcs as well a keyword-frequency distance based algorithm has also been introduced. Case studies are also included in this chapter to show the performance is better than traditional means.

This book contains the refereed proceedings of the 16th International Conference on Knowledge Management in Organizations, KMO 2022, held in Hagen, Germany, in July 2022. The 24 full papers and 5 short papers accepted for KMO 2022 were selected from 61 submissions and are organized in topical sections on: knowledge transfer and sharing; knowledge and organization; knowledge and service innovation; industry 4.0; information and knowledge systems; intelligent science; AI and new trends in KM.

This book contains the refereed proceedings of the 15th International Conference on Knowledge Management in Organizations, KMO 2021, held in Kaohsiung, Taiwan, in July 2021. The 28 full papers and 9 short papers accepted for KMO 2021 were selected from 86 submissions and are organized in topical sections on: knowledge management models and analysis; knowledge transfer and learning; knowledge and service innovation; knowledge and organization; information systems and information science; privacy and security; intelligent science and data mining; AI and new trends in IT.

This book constitutes the refereed proceedings of the 6th International Conference on Multidisciplinary Social Networks Research, MISNC 2019, held in Wenzhou, China, in August 2019. The 15 full papers presented were carefully reviewed and selected from 37 submissions. The papers deal with the following topics: social network, social network analysis, data engineering, data mining, user behavior.

This book constitutes the refereed proceedings of the Second International Multidisciplinary Social Networks Conference, MISNC 2015, held in Matsuyama, Japan, in September 2015. The 49 full papers presented were carefully reviewed and selected from 125 submissions. The papers deal with the following topics: multidisciplinary research on social networks; ethical issues related to SNS; information technology and social networks mining.

Social network analysis dates back to the early 20th century, with initial studies focusing on small group behavior from a sociological perspective. The emergence of the Internet and subsequent increase in the use of online social networking applications has caused a shift in the approach to this field. Faced with complex, large datasets, researchers need new methods and tools for collecting, processing, and mining social network data. Social Network Mining, Analysis and Research Trends: Techniques and Applications covers current research trends in the area of social networks analysis and mining. Containing research from experts in the social network analysis and mining communities, as well as practitioners from social science, business, and computer science, this book proposes new measures, methods, and techniques in social networks analysis and also presents applications and case studies in this changing field.