Web and Big Data : 5th International Joint Conference, APWeb-WAIM 2021, Guangzhou, China, August 23–25, 2021, Proceedings, Part II

This two-volume set, LNCS 12858 and 12859, constitutes the thoroughly refereed proceedings of the 5th International Joint Conference, APWeb-WAIM 2021, held in Guangzhou, China, in August 2021. The 44 full papers presented together with 24 short papers, and 6 demonstration papers were carefully reviewed and selected from 184 submissions. The papers are organized around the following topics: Graph Mining; Data Mining; Data Management; Topic Model and Language Model Learning; Text Analysis; Text Classification; Machine Learning; Knowledge Graph; Emerging Data Processing Techniques; Information Extraction and Retrieval; Recommender System; Spatial and Spatio-Temporal Databases; and Demo. Preface Organization Contents – Part II Contents – Part I Machine Learning 2 Unsupervised Deep Hashing via Adaptive Clustering 1 Introduction 2 Related Work 2.1 Similarity-Preserving Hashing 3 Method 3.1 Problem Definition 3.2 Framework 3.3 Discriminative Loss 3.4 Classification Loss 3.5 Cluster Reassignments 4 Experiments 4.1 Datasets 4.2 Baseline Methods 4.3 Evaluation 4.4 Implementation Details 4.5 Result Analysis 4.6 Discussion 5 Conclusion References FedMDR: Federated Model Distillation with Robust Aggregation 1 Introduction 2 Robust Federated Model Distillation 2.1 Problem Statement 2.2 The FedMDR Framework 2.3 Robust Aggregation Mechanism 2.4 FedMDR with Differential Privacy 3 Performance Evaluation 3.1 Experimental Setup 3.2 Experimental Results 4 Discussions 5 Related Work 6 Conclusion References Data Augmentation for Graph Convolutional Network on Semi-supervised Classification 1 Introduction 2 Proposed Method 2.1 Data Augmentation Strategy 2.2 Feature Availability Investigation 2.3 Attentional Integration Model 2.4 Objective Function 3 Experiments 3.1 Experiment Setting 3.2 Semi-Supervised Classification 3.3 Attentional Integration Model Analysis 3.4 Parameter Sensitivity 4 Related Works 5 Conclusion References Generating Long and Coherent Text with Multi-Level Generative Adversarial Networks 1 Introduction 2 Preliminaries 3 Methodology 3.1 Semantic Sketch Generation 3.2 Sentence Realization 3.3 Discussion and Learning 4 Experiments 4.1 Experimental Setup 4.2 Results and Analysis 5 Related Work 6 Conclusion References A Reasonable Data Pricing Mechanism for Personal Data Transactions with Privacy Concern 1 Introduction 2 Related Work 3 Personal Data Pricing Mechanism 3.1 System Model 3.2 Personal Privacy Data Pricing Function 4 Experiments 4.1 Experimental Data and Setup 4.2 Experimental Results 5 Conclusion References Knowledge Graph A Probabilistic Inference Based Approach for Querying Associative Entities in Knowledge Graph 1 Introduction 2 Related Work 3 AEBN Construction 3.1 Generating Rules for AEBN Construction 3.2 Structure Construction 3.3 Parameter Learning 4 Ranking AEs by Approximate Inferences over AEBN 5 Experiments 5.1 Experiment Setup 5.2 Effectiveness Tests 5.3 Efficiency Tests 6 Conclusions and Future Work References BOUNCE: An Efficient Selective Enumeration Approach for Nested Named Entity Recognition 1 Introduction 2 Related Work 3 BOUNCE Approach to Nested NER 3.1 Token Representation 3.2 Unit Region Classification 3.3 Span Region Head Detection 3.4 Span Region Classification 3.5 Time Complexity Analysis 4 Experiments 4.1 Datasets and Experimental Settings 4.2 Comparison Results 4.3 Ablation Study 5 Conclusion References PAIRPQ: An Efficient Path Index for Regular Path Queries on Knowledge Graphs 1 Introduction 2 Related Work 2.1 Path Index 2.2 RDF Storage Engine 3 Preliminaries 4 Path Index for Regular Path Queries 4.1 Frequent Path Mining 4.2 Index Scheme 4.3 Query Processing 5 Experiments 5.1 Experimental Settings 5.2 Experimental Results 6 Conclusion References A Hybrid Semantic Matching Model for Neural Collective Entity Linking 1 Introduction 2 Related Work 3 The HSM Model 3.1 Local Mention-to-Entity Model 3.2 Global Model 4 Experiments and Results 4.1 Datasets 4.2 Settings 4.3 Results 5 Conclusion References Multi-space Knowledge Enhanced Question Answering over Knowledge Graph 1 Introduction 2 Our Approach 2.1 Answer Aspect Attention Network 2.2 Multi-space Attention Network 2.3 Model Training 3 Experiments 3.1 Performance Comparison 4 Conclusion References Emerging Data Processing Techniques A Distribution-Aware Training Scheme for Learned Indexes 1 Introduction 2 The Learned Index 3 The Problem 3.1 Query Distribution 3.2 Data Distribution 4 DATum 4.1 Data Stretching 4.2 Model Cache 5 Experiments 5.1 Data Stretching 5.2 Model Cache 6 Related Works 7 Conclusion References AIR Cache: A Variable-Size Block Cache Based on Fine-Grained Management Method 1 Introduction 2 Related Work 3 Motivation 3.1 Internal Fragmentation 3.2 False Positives in Identifying Hot Cache Blocks 4 AIR Cache Design 4.1 Fine-Grained Recorder 4.2 Multi-Granularity Writer 4.3 Multi-Granularity Eviction 5 Experimental Evaluation 5.1 Experimental Setup 5.2 Performance Results 6 Conclusion References Learning an Index Advisor with Deep Reinforcement Learning 1 Introduction 2 Problem Formalization 3 Learning Index Selection 3.1 ISP as a DRL Problem 3.2 Index Agent for Indexes 3.3 Reward Design 3.4 Reinforcement Learning Training 4 Experiments 4.1 Experimental Setup 4.2 Reinforcement Learning Training Details 4.3 Performance Comparative Evaluation 5 Conclusions References SardineDB: A Distributed Database on the Edge of the Network 1 Introduction 2 Architecture of SardineDB 2.1 Architecture of SardineDB 2.2 Architecture of SardineCore 3 Experiments 3.1 Experimental Setup 3.2 Performance of SardineCore 3.3 Performance of SardineDB 4 Conclusion References DLSM: Distance Label Based Subgraph Matching on GPU 1 Introduction 2 Distance Label Based Subgraph Matching (DLSM) 2.1 Problem Definition 2.2 Distance Label Based Filtering 3 DLSM Overview 4 Experimental Results 4.1 Experimental Setup 4.2 Results on Synthetic and Real World Datasets 5 Related Work 6 Conclusion References Information Extraction and Retrieval Distributed Top-k Pattern Mining 1 Introduction 2 Graphs, Patterns and Pattern Mining 2.1 Graph Pattern Matching 2.2 Frequent Pattern Mining 2.3 Problem Formalization 3 Distributed Top-k Pattern Mining 4 Experimental Study 5 Conclusion References SQKT: A Student Attention-Based and Question-Aware Model for Knowledge Tracing 1 Introduction 2 Related Work 3 Problem Formulation 4 The SQKT Method 4.1 Question Representation 4.2 Student Attention Mechanism 4.3 Modeling Process of SQKT 4.4 Optimization 5 Experiments 5.1 Datasets 5.2 Baselines 5.3 Metrics 5.4 Model Evaluation 5.5 Ablation Studies 6 Conclusion References Comparison Question Generation Based on Potential Compared Attributes Extraction 1 Introduction 2 Related Work 3 Framework 3.1 Attribute Extractor 3.2 Attribute-Attention Seq2seq Module 4 Experiment 4.1 Dataset 4.2 Experimental Details 4.3 Evaluation 4.4 Comparative Models for Generation Task 5 Results and Analysis 5.1 Additional Features 5.2 Case Study 6 Conclusions References Multimodal Encoders for Food-Oriented Cross-Modal Retrieval 1 Introduction 2 Related Work 3 Model Framework 3.1 Overview of the Overall Framework 3.2 Initial Embedding Generation 3.3 Multimodal Encoders 3.4 Modality Alignment 3.5 Cross-Modal Learning 3.6 Training and Inference 4 Experiments 4.1 Dataset and Evaluation Metrics 4.2 Baselines 4.3 Implementation 4.4 Main Results 4.5 Ablation Studies 5 Conclusion References Data Cleaning for Indoor Crowdsourced RSSI Sequences 1 Introduction 2 Preliminaries and Problem Statement 3 Cleaning the Received Signal Strength Values 3.1 Alignment and Matching of RSSIs in Different RSSI Sequences 3.2 Cleaning Missing Values in Continuous Multiple RSSIs 4 Cleaning the Location Labels 4.1 Logical Graph Gl: Topology and Constraints of Indoor Space 4.2 Repair of False and Missing Location Label for Single RSSI 4.3 Repair of False and Missing Location Labels for Continuous Multiple RSSIs 5 Experimentation and Evaluation 5.1 Accuracy of Location Labels Cleaning 5.2 Accuracy of Localization 5.3 Average Error Distance 6 Conclusion References Recommender System A Behavior-Aware Graph Convolution Network Model for Video Recommendation 1 Introduction 2 Related Work 3 Sagittarius Model 3.1 Problem Formulation 3.2 Model Architecture 3.3 Learning Objectives 3.4 Recommendation Acceleration 4 Evaluation 4.1 Experimental Setup 4.2 Competitors 4.3 Performance Comparison 4.4 Ablation Analyses 4.5 Impact of Hyper-parameters 4.6 Online A/B Test 5 Conclusion References GRHAM: Towards Group Recommendation Using Hierarchical Attention Mechanism 1 Introduction 2 Related Work 2.1 Group Recommendation 2.2 Attention Mechanism for Recommendation 3 GRHAM Model 3.1 Notations and Problem Formulation 3.2 Model Framework 3.3 Model Optimization 4 Experiments 4.1 Datasets 4.2 Evaluation 4.3 Baselines 4.4 Overall Performance Comparison(RQ1) 4.5 Model Performances for Different Hyper-Parameters (RQ2) 5 Conclusion and Future Work References Multi-interest Network Based on Double Attention for Click-Through Rate Prediction 1 Introduction 2 Related Work 3 Model of This Paper 3.1 Embedded Layer 3.2 Users' Dynamic Interest Features Extraction Layer 3.3 Multi-interest Extraction Layer 3.4 Loss Function 4 Experiments 4.1 Datasets and Experiment Setup 4.2 Competitors 4.3 Evaluation Metrics 4.4 Comparative Experimental Results 4.5 Ablation Experimental Results 5 Conclusion References Self-residual Embedding for Click-Through Rate Prediction 1 Introduction 2 Problem Definition 3 Methodology 3.1 Input and Embedding Layer 3.2 Self-residual Embedding Layer 3.3 Second-Order Feature Interaction 3.4 Hidden Layer 3.5 Output Layer 3.6 Model Training 4 Experiments 4.1 Experimental Settings 4.2 Experimental Results 4.3 Influence of the Network Structure 5 Conclusion References GCNNIRec: Graph Convolutional Networks with Neighbor Complex Interactions for Recommendation 1 Introduction 2 Related Works 3 Our Approach 3.1 Raw Input and Embedding Initialization 3.2 Linear-Aggregator Module 3.3 Interaction-Aggregator Module 3.4 Final Embedding 3.5 Rating Prediction 4 Experiments 4.1 Experimental Settings 4.2 Performance Comparison 4.3 Hyper-Parameter Analysis of GCNNIRec 5 Conclusion References Spatial and Spatio-Temporal Databases Velocity-Dependent Nearest Neighbor Query 1 Introduction 2 Velocity-Dependent Nearest Neighbors 2.1 Baseline Algorithm 3 R-Tree Based Algorithm 3.1 Useful MBRs 3.2 Search Algorithm 4 Tile-Based Algorithms 4.1 Queries on Adaptive Tiles 5 Experiment 5.1 Query Performances 5.2 Efficiencies of Tile-Based Structures 6 Conclusions References Finding Geo-Social Cohorts in Location-Based Social Networks 1 Introduction 2 Related Work 3 Problem Formulation 3.1 Preliminary Concepts 3.2 Objective 3.3 Maximizing Activity Density 4 COVER Algorithm 5 Experimental Study 5.1 Brute-Force Convoy Retrieval 5.2 Use Case: Convoy Prediction 5.3 Revalidating the Social Clique Constraint 5.4 Prediction Without Input Categories 5.5 Prediction with Input Categories 5.6 Effect of Surplus Parameter 5.7 Scalability and Prediction Quality 5.8 Analysis on Retrieved Groups 6 Conclusion References Modeling Dynamic Spatial Influence for Air Quality Prediction with Atmospheric Prior 1 Introduction 2 Related Work 3 Problem Formulation 4 Proposed Method 4.1 Feature Representation 4.2 Dynamic Spatial Graph Construction 4.3 Dynamic Spatial Graph Embedding 4.4 Encoder-Decoder Based Spatio-Temporal Fusion 4.5 Model Learning 5 Experiments 5.1 Datasets 5.2 Experimental Settings 5.3 Compared Methods 5.4 Experimental Results 6 Conclusion and Future Work References Learning Cooperative Max-Pressure Control by Leveraging Downstream Intersections Information for Traffic Signal Control 1 Introduction 2 Related Work 3 Preliminary 4 Our Approach 4.1 Agent Design 4.2 Learning Process 5 Stability Analysis 6 Experiments 6.1 Datasets and Baselines 6.2 Experimental Settings 6.3 Experimental Results 7 Conclusion and Future Work References Privacy-Preserving Healthcare Analytics of Trajectory Data 1 Introduction and Related Works 2 Our Differential-Privacy Framework for Analytics of Spatio-Temporal Trajectory 3 Evaluation 4 Conclusions References Demo PARROT: An Adaptive Online Shopping Guidance System 1 Introduction 2 System Implementation 3 Demo Scenarios 3.1 Scenario 1: Products' Descriptions with Basic Attributes 3.2 Scenario 2: Products' Descriptions with Functional Attributes 3.3 Scenario 3: Products' Descriptions with Experience Attributes 4 Conclusion References gStore-C: A Transactional RDF Store with Light-Weight Optimistic Lock 1 Introduction 2 System Overview 3 Lightweight Optimistic Lock 4 Demonstration References Deep-gAnswer: A Knowledge Based Question Answering System 1 Introduction 2 System Architecture 3 Techniques 4 Demonstration References ALMSS: Automatic Learned Index Model Selection System 1 Introduction 2 System Overview 3 Key Technologies 3.1 Automatic Model Selection Module 3.2 Learned Index with Automatic Model Selection Module 4 Demonstration Scenarios References GPKRS: A GPU-Enhanced Product Knowledge Retrieval System 1 Introduction 2 System Overview 3 Demonstration References Standard-Oriented Standard Knowledge Graph Construction and Applications System 1 Introduction 2 Architecture of Standard Knowledge Graph 3 Standard-Oriented Knowledge Graph Based Algorithms 3.1 Standard Template Recommendation 3.2 Standard Conflict Detection 4 Visualization and Case Study References Author Index