Web and big data Part 3
.- 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 Learnin...
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| Weitere Verfasser: | , , , , , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Singapore
Springer
2024
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| Schriftenreihe: | Lecture notes in computer science
14963 |
| Schlagworte: |
Algorithmen und Datenstrukturen
> Algorithms & data structures
> Angewandte Informatik
> Bildverarbeitung
> COMPUTERS / Computer Science
> COMPUTERS / Data Processing / Optical Data Processing
> COMPUTERS / Database Management / Data Mining
> COMPUTERS / Database Management / General
> COMPUTERS / Expert Systems
> COMPUTERS / Information Theory
> COMPUTERS / Programming / Algorithms
> Data Mining
> Data mining
> Databases
> Datenbanken
> Expert systems / knowledge-based systems
> Image processing
> Information technology: general issues
> Information theory
> Informationstheorie
> Konferenzschrift
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| Online Zugang: | Cover |
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| Zusammenfassung: | .- 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. 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 |
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| Beschreibung: | Literaturangaben |
| Beschreibung: | xvii, 515 Seiten Diagramme |
| ISBN: | 9789819772377 978-981-97-7237-7 |