the proceedings contain 73 papers. the topics discussed include: dependability, abstraction, and programming;the challenge of assuring data trustworthiness;probabilistic inverse ranking queries over uncertain data;ada...
ISBN:
(纸本)9783642008863
the proceedings contain 73 papers. the topics discussed include: dependability, abstraction, and programming;the challenge of assuring data trustworthiness;probabilistic inverse ranking queries over uncertain data;adaptive safe regions for continuous spatial queries over moving objects;optimization on data object compression and replication in wireless multimedia sensor networks;an effective and efficient method for handling transmission failures in sensor networks;GraphREL: a decomposition-based and selectivity-aware relational framework for processing sub-graph queries;a uniform framework for ad-hoc indexes to answer reachability queries on large graphs;Ricochet: a family of unconstrained algorithms for graph clustering;top-K correlation sub-graph search in graph databases;an EM-based algorithm for clustering data streams in sliding windows;and eager evaluation of partial tree-pattern queries on XML streams.
the proceedings contain 87 papers. the special focus in this conference is on databasesystems for advancedapplications. the topics include: Higher-Order Graph Contrastive Learning for Recommendation;FNDPro: Eva...
ISBN:
(纸本)9789819757787
the proceedings contain 87 papers. the special focus in this conference is on databasesystems for advancedapplications. the topics include: Higher-Order Graph Contrastive Learning for Recommendation;FNDPro: Evaluating the Importance of Propagations during Fake News Spread;leveraging Homophily-Augmented Energy Propagation for Bot Detection on Graphs;multi-level Contrastive Learning on Weak Social Networks for Information Diffusion Prediction;biasRec: A General Bias-Aware Social Recommendation Model;beyond the Known: Novel Class Discovery for Open-World Graph Learning;robust Graph Recommendation via Noise-Aware Adversarial Perturbation;learning Social Graph for Inactive User Recommendation;MANE: A Multi-cascade Adversarial Network Embedding Model for Anchor Link Prediction;uTransfer: Unified Transferability Metric Incorporating Heterogeneous User Data in Social Network;GPSR: Graph Prompt for Session-Based Recommendation;guiding Graph Learning with Denoised Modality for Multi-modal Recommendation;enhancing Multi-view Contrastive Learning for Graph Anomaly Detection;global Route Planning for Large-Scale Requests on Traffic-Aware Road Network;TransGAD: A Transformer-Based Autoencoder for Graph Anomaly Detection;unsupervised Node Clustering via Contrastive Hard Sampling;DySDGNN: Representation Learning in Dynamic Signed Directed Networks;multi-objective Graph Neural Network Explanatory Model with Local and Global Information Preservation;EGNN-AD: An Effective Graph Neural Network-Based Approach for Anomaly Detection on Edge-Attributed Graphs;diffusion Model-Enhanced Contrastive Learning for Graph Representation;H2GNN: Graph Neural Networks with Homophilic and Heterophilic Feature Aggregations;Super-Node Generation for GNN-Based Recommender systems: Enhancing Distant Node Integration via Graph Coarsening;history Driven Sampling for Scalable Graph Neural Networks;subgraph Patterns Enhanced Graph Neural Network for Fraud Detection;advancing Latent Representation Ranking fo
the proceedings contain 87 papers. the special focus in this conference is on databasesystems for advancedapplications. the topics include: Higher-Order Graph Contrastive Learning for Recommendation;FNDPro: Eva...
ISBN:
(纸本)9789819755547
the proceedings contain 87 papers. the special focus in this conference is on databasesystems for advancedapplications. the topics include: Higher-Order Graph Contrastive Learning for Recommendation;FNDPro: Evaluating the Importance of Propagations during Fake News Spread;leveraging Homophily-Augmented Energy Propagation for Bot Detection on Graphs;multi-level Contrastive Learning on Weak Social Networks for Information Diffusion Prediction;biasRec: A General Bias-Aware Social Recommendation Model;beyond the Known: Novel Class Discovery for Open-World Graph Learning;robust Graph Recommendation via Noise-Aware Adversarial Perturbation;learning Social Graph for Inactive User Recommendation;MANE: A Multi-cascade Adversarial Network Embedding Model for Anchor Link Prediction;uTransfer: Unified Transferability Metric Incorporating Heterogeneous User Data in Social Network;GPSR: Graph Prompt for Session-Based Recommendation;guiding Graph Learning with Denoised Modality for Multi-modal Recommendation;enhancing Multi-view Contrastive Learning for Graph Anomaly Detection;global Route Planning for Large-Scale Requests on Traffic-Aware Road Network;TransGAD: A Transformer-Based Autoencoder for Graph Anomaly Detection;unsupervised Node Clustering via Contrastive Hard Sampling;DySDGNN: Representation Learning in Dynamic Signed Directed Networks;multi-objective Graph Neural Network Explanatory Model with Local and Global Information Preservation;EGNN-AD: An Effective Graph Neural Network-Based Approach for Anomaly Detection on Edge-Attributed Graphs;diffusion Model-Enhanced Contrastive Learning for Graph Representation;H2GNN: Graph Neural Networks with Homophilic and Heterophilic Feature Aggregations;Super-Node Generation for GNN-Based Recommender systems: Enhancing Distant Node Integration via Graph Coarsening;history Driven Sampling for Scalable Graph Neural Networks;subgraph Patterns Enhanced Graph Neural Network for Fraud Detection;advancing Latent Representation Ranking fo
the proceedings contain 26 papers. the special focus in this conference is on Big Data Management and Service. the topics include: FAIth: A Fast, Accurate, and Lightweight database-Agnostic Learned Cost Model;fas...
ISBN:
(纸本)9789819609130
the proceedings contain 26 papers. the special focus in this conference is on Big Data Management and Service. the topics include: FAIth: A Fast, Accurate, and Lightweight database-Agnostic Learned Cost Model;fast Approximate Temporal Butterfly Counting on Bipartite Graphs via Edge Sampling;Financial-ICS: Identifying Peer Firms via LongBERT from 10K Reports;establishing a Decentralized Diamond Quality Management System: Advancing Towards Global Standardization;co-estimation of Data Types and their Positional Distribution;Enhancing Load Forecasting with VAE-GAN-Based Data Cleaning for Electric Vehicle Charging Loads;audio-Guided Visual Knowledge Representation;boundary Point Detection Combining Gravity and Outlier Detection Methods;A Meta-learning Approach for Category-Aware Sequential Recommendation on POIs;automatic Post-editing of Speech Recognition System Output Using Large Language Models;comparative Analysis with Multiple Large-Scale Language Models for Automatic Generation of Funny Dialogues;effectiveness of the Programmed Visual Contents Comparison Method for Two Phase Collaborative Learning in Computer Programming Education: A Case Study;generating Achievement Relationship Graph Between Actions for Alternative Solution Recommendation;generating News Headline Containing Specific Person Name;Investigating Evidence in Sentence Similarity Using MASK in BERT;acceleration of Synopsis Construction for Bounded Approximate Query Processing;Query Expansion in Food Review Search with Synonymous Phrase Generation by LLM;Question Answer Summary Generation from Unstructured Texts by Using LLMs;Real Estate Information Exploration in VR with LoD Control by Physical Distance;voices of Asynchronous Learning Students: Revealing Learning Characteristics through Vocabulary Analysis of Notes Tagged in Videos;review Search Interface Based on Search Result Summarization Using Large Language Model;yes-No Flowchart Generation for Interactive Exploration of Personalized Health Improve
Honeypots are tools to help identify attackers' intrusion techniques, patterns and behaviors into IT systems. Among such systems, database management systems (DBMSs) are traditionally targeted software, as they st...
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Withthe new advancements in cloud computing and internet of things (IoT), mobile applications are transforming the healthcare sector offering solutions for both users and researchers. the massive amounts of data gene...
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Nowadays, web applications are generally utilized as a busybody between PC clients. Also, web applications are utilized by online business organizations, government agencies, research organizations, etc. that need to ...
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Adaptive user interfaces can lead to better user experience and task accomplishment, when tailored to the users' needs and helping to understand the complex task structure and functionality of e-commerce applicati...
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In recent years, there has been a notable rise in the application of machine learning to cost estimation for query optimization. Central to an effective cost model are the abilities of accuracy, efficiency, lightness,...
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ISBN:
(纸本)9789819609130;9789819609147
In recent years, there has been a notable rise in the application of machine learning to cost estimation for query optimization. Central to an effective cost model are the abilities of accuracy, efficiency, lightness, and generalizability. However, traditional cost models are based on heuristics thus lack of accuracy. On the other hand, the learned cost models frequently struggle to strike a balance between accuracy and efficiency, with many lacking broad applicability. To combat these challenges, we introduce FAIth, a fast, accurate, and database-agnostic learned cost model. FAIth harnesses data from multiple sources to learn cross-database meta-knowledge. It is then effectively refined, leveraging the unique data information from the target database via an Adapter we developed. Proven through various benchmarks, FAIth consistently showcases its prowess in delivering accurate and robust cost estimations.
the maritime industry plays a crucial role in global trade and transport, necessitating effective collision avoidance, navigation security, and reduction in human errors and costs. this paper presents an automated dat...
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