Web and Big Data

8th International Joint Conference, APWeb-WAIM 2024, Jinhua, China, August 30 ¿ September 1, 2024, Proceedings, Part III
Herausgegeben:Zhang, Wenjie; Tung, Anthony; Zheng, Zhonglong; Yang, Zhengyi; Wang, Xiaoyang; Guo, Hongjie

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Lecture Notes in Computer Science 14963
• Verlag: Springer / Springer Nature Singapore / Springer, Berlin
• Artikelnr. des Verlages: 978-981-97-7237-7
• 2024
• Seitenzahl: 536
• Erscheinungstermin: 28. August 2024
• Englisch
• Abmessung: 235mm x 155mm x 29mm
• Gewicht: 803g
• ISBN-13: 9789819772377
• ISBN-10: 9819772370
• Artikelnr.: 71261008

Inhaltsangabe

.- Spatial and Temporal Data.
.- Temporalformer: A Temporal Decomposition Causal Transformer Network For Wind Power Forecasting.
.- MSCFNet: A Multi-Scale Spatial and Channel Fusion Network for Geological Environment Remote Sensing Interpreting.
.- TS-HCL: Hierarchical Layer-wise Contrastive Learning for Unsupervised Domain Adaptation on Time-Series.
.- Dynamic-Static Fusion for Spatial-Temporal Anomaly Detection and Interpretation in Multivariate Time Series.
.- MFCD:A deep learning method with fuzzy clustering for time series anomaly detection.
.- Graph Neural Network.
.- SBGMN: A Multi-View Sign Prediction Network for Bipartite Graphs.
.- Product Anomaly Detection on Heterogeneous Graphs with Sparse Labels.
.- Generic and Scalable Detection of Risky Transactions Using Density Flows: Applications to Financial Networks.
.- Attributed Heterogeneous Graph Embedding with Meta-graph Attention.
.- Automated Multi-scale Contrastive Learning with Sample-awareness for Graph Classification.
.- CGAR: A Contrastive Graph Attention Residual Network for Enhanced Fake News Detection.
.- GCH: Graph contrastive Learning with Higher-order Networks.
.- LPRL-GCNN for Multi-Relation Link Prediction in Education.
.- Multi-view Graph Neural Network for Fair Representation Learning.
.- MERGE: Multi-View Relationship Graph Network for Event-Driven Stock Movement Prediction.
.- Relation-Aware Heterogeneous Graph Neural Network for Fraud Detection.
.- Graph Mining.
.- Robust Local Community Search over Large Heterogeneous Information Networks.
.- Community discovery in social network via dual-technique.
.- CSGTM: Capsule Semantic Graph-Guided Latent Community Topics Discovery.
.- Efficient ( , beta, Gamma)-Core Search in Bipartite Graphs Based on Bi-triangles.
.- Identifying Rank-happiness Maximizing Sets under Group Fairness Constraints.
.- Reachability-Aware Fair Influence Maximization.
.- Towards Efficient Heuristic Graph Edge Coloring.
.- Tree and Graph based Two-Stages Routing for Approximate Nearest Neighbor Search.
.- Unbiasedly Estimate Temporal Katz Centrality and Identify Top-K Vertices in Streaming Graph.
.- Database System and Query Optimization.
.- Gar++: Natural Language to SQL Translation with Efficient Generate-and-Rank.
.- A Composable Architecture for Cloud Transactional DBMS.
.- Computing Minimum Subset Repair On Incomplete Data.
.- Flutist: Parallelizing Transaction Processing for LSM-tree-based Relational Database.
.- Poplar: Partially-Ordered Parallel Logging for Lower Isolation Levels.
.- Table Embedding Models Based on Contrastive Learning for Improved Cardinality Estimation.