Atjaunināt sīkdatņu piekrišanu

E-grāmata: Web and Big Data: 8th International Joint Conference, APWeb-WAIM 2024, Jinhua, China, August 30 - September 1, 2024, Proceedings, Part III

Edited by , Edited by , Edited by , Edited by , Edited by , Edited by
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 77,31 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Web and Big Data: 8th International Joint Conference, APWeb-WAIM 2024, Jinhua, China, August 30 - September 1, 2024, Proceedings, Part IIIPalielināt
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

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 30September 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.
.- 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 (, , )-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.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97898197723842e.html