Web and Big Data. APWeb-WAIM 2022 International Workshops: KGMA 2022, SemiBDMA 2022, DeepLUDA 2022, Nanjing, China, November 25-27, 2022, Proceedings

This book presents revised selected papers from the 6th Asia-Pacific Web and Web-Age Information Management International Joint Conference on Web and Big Data, APWeb-WAIM 2022, including workshops on Knowledge Graph Management and Applications, Semi-structured Big Data Management and Applications, and Deep Learning in Large-scale Unstructured Data Analytics.

Format: Paperback / softback
Length: 286 pages
Publication date: 30 March 2023
Publisher: Springer Verlag, Singapore

The 6th Asia-Pacific Web and Web-Age Information Management International Joint Conference on Web and Big Data, APWeb-WAIM 2022, organized a series of workshops that brought together experts from various fields to explore innovative solutions in web and big data management. These workshops included the Fifth International Workshop on Knowledge Graph Management and Applications, KGMA 2022, the Fourth International Workshop on Semi-structured Big Data Management and Applications, SemiBDMA 2022, and the Third International Workshop on Deep Learning in Large-scale Unstructured Data Analytics, DeepLUDA 2022.

The conference, held in Nanjing, China, in August 2022, received a total of 39 submissions, which were meticulously reviewed by a panel of esteemed experts. Out of these submissions, 23 exceptional papers were chosen to be included in this book.

The papers presented in this volume cover a wide range of topics, including knowledge graph management, semi-structured big data management, deep learning in large-scale unstructured data analytics, and web and big data applications. The authors of these papers have made significant contributions to the field, presenting their latest research findings and innovative approaches.

Knowledge Graph Management:
Knowledge graphs are a powerful tool for organizing and representing complex data. In the workshop on Knowledge Graph Management and Applications, researchers discussed various aspects of knowledge graph construction, maintenance, and querying. They explored techniques such as graph embedding, graph neural networks, and graph summarization, which have the potential to improve the accuracy and efficiency of knowledge graph-based applications.

Semi-Structured Big Data Management:
Semi-structured data refers to data that has a mixture of structured and unstructured elements. Managing semi-structured data is a challenging task due to its heterogeneity and complexity. In the workshop on Semi-Structured Big Data Management and Applications, researchers presented their approaches to indexing, querying, and analyzing semi-structured data. They discussed techniques such as pattern mining, machine learning, and graph analytics, which can help extract valuable insights from semi-structured data.

Deep Learning in Large-Scale Unstructured Data Analytics:
Deep learning is a powerful machine learning technique that has been widely used in recent years for analyzing large-scale unstructured data. In the workshop on Deep Learning in Large-Scale Unstructured Data Analytics, researchers discussed their latest research findings in this area. They explored techniques such as convolutional neural networks, recurrent neural networks, and self-supervised learning, which have the potential to improve the accuracy and efficiency of unstructured data analysis.

Web and Big Data Applications:
The workshop on Web and Big Data Applications focused on the use of web and big data technologies in various real-world applications. Researchers presented their case studies and experiences in areas such as healthcare, finance, transportation, and social media. They discussed how web and big data technologies can be used to improve decision-making, automate processes, and enhance user experiences.

Overall, this book constitutes a valuable resource for researchers, practitioners, and students interested in web and big data management. The papers presented in this volume showcase the latest research findings and innovative approaches in this field, providing insights into the future of web and big data management.

The 6th Asia-Pacific Web and Web-Age Information Management International Joint Conference on Web and Big Data, APWeb-WAIM 2022, organized a series of workshops that brought together experts from various fields to explore innovative solutions in web and big data management. These workshops included the Fifth International Workshop on Knowledge Graph Management and Applications, KGMA 2022, the Fourth International Workshop on Semi-structured Big Data Management and Applications, SemiBDMA 2022, and the Third International Workshop on Deep Learning in Large-scale Unstructured Data Analytics, DeepLUDA 2022.

The conference, held in Nanjing, China, in August 2022, received a total of 39 submissions, which were meticulously reviewed by a panel of esteemed experts. Out of these submissions, 23 exceptional papers were chosen to be included in this book.

The papers presented in this volume cover a wide range of topics, including knowledge graph management, semi-structured big data management, deep learning in large-scale unstructured data analytics, and web and big data applications. The authors of these papers have made significant contributions to the field, presenting their latest research findings and innovative approaches.

Knowledge Graph Management:
Knowledge graphs are a powerful tool for organizing and representing complex data. In the workshop on Knowledge Graph Management and Applications, researchers discussed various aspects of knowledge graph construction, maintenance, and querying. They explored techniques such as graph embedding, graph neural networks, and graph summarization, which have the potential to improve the accuracy and efficiency of knowledge graph-based applications.

Semi-Structured Big Data Management:
Semi-structured data refers to data that has a mixture of structured and unstructured elements. Managing semi-structured data is a challenging task due to its heterogeneity and complexity. In the workshop on Semi-Structured Big Data Management and Applications, researchers presented their approaches to indexing, querying, and analyzing semi-structured data. They discussed techniques such as pattern mining, machine learning, and graph analytics, which can help extract valuable insights from semi-structured data.

Deep Learning in Large-Scale Unstructured Data Analytics:
Deep learning is a powerful machine learning technique that has been widely used in recent years for analyzing large-scale unstructured data. In the workshop on Deep Learning in Large-Scale Unstructured Data Analytics, researchers discussed their latest research findings in this area. They explored techniques such as convolutional neural networks, recurrent neural networks, and self-supervised learning, which have the potential to improve the accuracy and efficiency of unstructured data analysis.

Web and Big Data Applications:
The workshop on Web and Big Data Applications focused on the use of web and big data technologies in various real-world applications. Researchers presented their case studies and experiences in areas such as healthcare, finance, transportation, and social media. They discussed how web and big data technologies can be used to improve decision-making, automate processes, and enhance user experiences.

Overall, this book constitutes a valuable resource for researchers, practitioners, and students interested in web and big data management. The papers presented in this volume showcase the latest research findings and innovative approaches in this field, providing insights into the future of web and big data management.

Weight: 462g
Dimension: 235 x 155 (mm)
ISBN-13: 9789819913534
Edition number: 1st ed. 2023