Web and Big Data
Springer Verlag, Singapore
978-981-95-5718-9 (ISBN)
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The 136 full papers and 15 short papers presented in these proceedings were carefully reviewed and selected from 472 submissions. The papers are organized in the following topical sections:
Part I: Data Mining I; Machine Learning I; Information Retrieval and Knowledge Management I; Graph Data Management andAnalytics I; Complex Data Management.
Part II: Complex Data Management; Spatial and Temporal Data Management; Data Privacy and Trusted AI; Data Management on New Hardwares; Query Processing and Optimization; Data Mining II.
Part III: Data Mining II; Machine Learning II; Information Retrieval and Knowledge Management II; Graph Data Management andAnalytics II; Big Data Management.
Part IV: Big Data Management; LLM for Data Management; Information Retrieval; Demonstration Paper; Industry Paper.
.- Data Mining II.
.- TimeMultiformer: Attention-Based Collaborative Feature and Temporal Dependencies Learning for
Multivariate Time Series Imputation.
.- Heterogeneous Graph-Enhanced Temporal Prediction withAdaptive Monitoring for Industrial Chain
Data.
.- Finding Maximum Common Subgraphs Efficiently Through Dynamic Bidirectional Vertex Selection.
.- TD-LTNet: Temporal-Decay LSTM-Transformer Network for Mobile Video QoE Prediction.
.- FACKT:AFault-Aware Model for Code Knowledge Tracing.
.- UCPM-CPI:AUnited Co-location Pattern MiningAlgorithm Based on Correlated Participation Index.
.- Machine Learning II.
.- ACross-modal Fusion Method for Short Video Fake News Detection via MambaFormer.
.- ALightweight Pavement Defect Detection Method Based on Multi-BranchAttention with Contextual
Guidance.
.- A Hierarchical Reinforcement Learning Method based on Decision Frequency and Internal Reward
Mechanism.
.- ASoluble Solids Content Prediction Method for Blueberries Based on Differential Enhancement and
Multi-Scale Feature Fusion.
.- MedDPA: Multi-Scale Decomposition and Prototype-based ChannelAggregation for Medical Time
Series Classification.
.- Knowledge Graph and Hypergraph Enabled Semantic Modeling for Dual-Intent Recommendation.
.- GK-SMOTE:AHyperparameter-free Noise-Resilient Gaussian KDE-Based OversamplingApproach.
.- Robust Ensemble Learning via t-Tilted Loss: ANoise-Resistant Framework.
.- A Multi-Scale Dilated Convolution model with Edge Optimization for Crack Detection.
.- A DeepReinforcement Learning Framework for Denial Constraint Discovery.
.- Information Retrieval and Knowledge Management II.
.- Generating Difficulty Controllable Multiple-Choice Question By Iteratively Guiding The Large
Language Model.
.- Research on a multi-modal entity alignment method based on neighborhood matching.
.- Multi-Granularity Knowledge Graph EntityAlignment via Semantic Clustering and Dynamic
Collaborative Projection.
.- MNS-EA:AMixedNegative Sampling-based EntityAlignment Model.
.- Improving Continual Relation Extraction via Parameter Regularization and Dynamic Memory.
.- MultiRelE: Multi-relation Knowledge Graph Embedding Model.
.- LE2C: LLM-Enhanced Event Evolutionary Graph for Explainable Classification.
.- Discrete Channel Mapping in Knowledge Distillation.
.- Uncertainty-Aware Semantic Decoding for LLM-Based Sequential Recommendation.
.- Dual-StreamAdaptive Retrieval and HierarchicalAgent Collaboration for Document Visual QA.
.- Retrieval and Distill: ATemporal Data Shift-Free Paradigm for Online Recommendation System.
.- Retrieval-based Knowledge Consistency Validation for Fake News Detection.
.- Graph Data Management andAnalytics II.
.- DCS-GCN:ADual-Channel Social Graph Convolutional Network for Recommendation.
.- DED: Integrating Degree Entropy and Dynamic Delay Mechanisms for Influence Maximization in Multilayer Networks.
.- ComIVY: Community-Driven Budgeted Influence Maximization via IVY Optimization.
.- Enhancing Partial Evaluation Subgraph Matching Through Vertex Hotness Caching.
.- Fetan: Enhancing Few-Shot Classification on Text-Attributed Graphs with In-Context Learning of LLMs.
.- Graph Contrastive Anomaly Detection Based on Beta Wavelet Multi-GNNs.
.- Adaptive Graph Contrastive Learning for Blockchain Smart Contract Vulnerability Detection.
.- CPGCN:Evolutionary Relationship Mining Framework for Ethnic Costume Elements.
.- Shilling Attacks on GNN-based Recommender Systems with Graph Contrastive Learning.
.- Big Data Management.
.- DC-Storage: AFast Permissioned IoT Blockchain Storage with Decoupled Index.
.- zkHARPS:ADecentralized Method for Cross-Chain Identity Aggregation and Privacy Proof.
.- FLAR: Fibonacci-assisted Lightweight Anonymous RevocableAuthentication Scheme in Industrial
Internet of Things.
.- RL-ABO:Adaptive Blockchain Control Parameter Optimization Method Based on Reinforcement
Learning.
.- ByzTierFL:ATieredApproach to Byzantine Robust and Decentralized Federated Learning.
.- Root Cause Localization Through Holistic Fault Propagation Perspective for Cloud-native Systems.
| Erscheint lt. Verlag | 7.3.2026 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | Approx. 700 p. |
| Verlagsort | Singapore |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Advanced database and Web applications • Big data management • Block chain models and applications • Data engineering for big remote sensing data • Data management on new hardwares • Data Mining • Graph and social network analysis • Graph data, RDF, social networks • information extraction • Information Retrieval • knowledge graphs • machine learning • Natural Language Processing • Parallel and distributed data management • query processing and optimization • Recommender Systems • representation learning • Security, privacy, and trusted AI • spatial and temporal databases • Streams, complex event processing |
| ISBN-10 | 981-95-5718-6 / 9819557186 |
| ISBN-13 | 978-981-95-5718-9 / 9789819557189 |
| Zustand | Neuware |
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