Web and Big Data - Zhang Wenjie
- 8th International Joint Conference, APWeb-WAIM 2024, Jinhua, China, August 30 - September 1, 2024, Proceedings, Part III
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024.
The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions.
The papers are organized in the following topical sections:
Part I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System.
Part II: Recommender System, Knowledge Graph and Spatial and Temporal Data.
Part III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization.
Part IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security
Part V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
EAN: 9789819772377
The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30-September 1, 2024.
The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions.
The papers are organized in the following topical sections:
Part I: Natural language processing, Generative AI and LLM, Computer Vision and Recommender System.
Part II: Recommender System, Knowledge Graph and Spatial and Temporal Data.
Part III: Spatial and Temporal Data, Graph Neural Network, Graph Mining and Database System and Query Optimization.
Part IV: Database System and Query Optimization, Federated and Privacy-Preserving Learning, Network, Blockchain and Edge computing, Anomaly Detection and Security
Part V: Anomaly Detection and Security, Information Retrieval, Machine Learning, Demonstration Paper and Industry Paper.
EAN: 9789819772377