Database Systems for Advanced Applications: 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11–14, 2021, Proceedings, Part II (Lecture Notes in Computer Science, 12682)

The three-volume set LNCS 12681-12683 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021, held in Taipei, Taiwan, in April 2021. The total of 156 papers presented in this three-volume set was carefully reviewed and selected from 490 submissions. The topic areas for the selected papers include information retrieval, search and recommendation techniques; RDF, knowledge graphs, semantic web, and knowledge management; and spatial, temporal, sequence, and streaming data management, while the dominant keywords are network, recommendation, graph, learning, and model. These topic areas and keywords shed the light on the direction where the research in DASFAA is moving towards. Due to the Corona pandemic this event was held virtually. Preface Organization Contents – Part II Text and Unstructured Data Multi-label Classification of Long Text Based on Key-Sentences Extraction 1 Introduction 2 Related Work 2.1 Multi-Label Learning 2.2 Multi-Task Learning 3 Model 3.1 Task Definition 3.2 Sentence Encoder 3.3 Key-Sentences Extraction with Semi-supervised Learning 3.4 Multi-label Prediction Based Multi-label Attention 3.5 Optimization 4 Experiments 4.1 Data 4.2 Baseline Models and Evaluation Metrics 4.3 Experimental Settings 4.4 Results and Analysis 4.5 Ablation Test 4.6 Case Study 5 Conclusion References Automated Context-Aware Phrase Mining from Text Corpora 1 Introduction 2 Methodology 2.1 Problem Definition 2.2 Overview 2.3 Data Process 2.4 Topic-Aware Phrase Recognition Network (TPRNet) 2.5 Instance Selection Network (ISNet) 2.6 Training Details 3 Experiments 3.1 Experimental Setup 3.2 Experimental Results 3.3 Impact of Topic Information 3.4 Effectiveness of Selection Policy 4 Related Work 5 Conclusion References Keyword-Aware Encoder for Abstractive Text Summarization 1 Introduction 2 Related Work 3 Model Description 3.1 Keywords Extraction 3.2 Dependency-Based Keyword Sequence 3.3 Keyword-Aware Encoder 3.4 Summary Decoder 3.5 Objective Function 4 Experiments 4.1 Datasets 4.2 Baselines 4.3 Evaluation Metric 4.4 Implementation Details 4.5 Evaluation 5 Discussion 5.1 Visualization of Gates and Attention Weights 5.2 Influence of Keyword Extraction Ratio 5.3 Analysis of Content Selection Methods 5.4 Case Study 6 Conclusion References Neural Adversarial Review Summarization with Hierarchical Personalized Attention 1 Introduction 2 Related Work 3 Proposed Method 3.1 Problem Formulation 3.2 Review Encoder 3.3 Abstractive Summary Generation 4 Experimental Setup 4.1 Datasets 4.2 Baseline Methods 4.3 Experimental Settings 5 Result and Discussion 5.1 Performance Evaluation 5.2 Ablation Study 5.3 Case Study 5.4 Visualization of Attention 6 Conclusion References Generating Contextually Coherent Responses by Learning Structured Vectorized Semantics 1 Introduction 2 Related Work 3 Method 3.1 Model Overview 3.2 Hierarchical Centralized Encoder 3.3 Inference Network 3.4 Decoder with Calibration Mechanism 3.5 Loss Function 4 Experiments 4.1 Experimental Settings 4.2 Automatic Metric-Based Evaluation 4.3 Manual Evaluation 4.4 Further Analysis of Our Method 4.5 Case Study 5 Conclusion References Latent Graph Recurrent Network for Document Ranking 1 Introduction 2 Related Work 2.1 Interaction Based Neural Ranking Models 2.2 Pretrained Neural Language Models for IR 2.3 Graph Neural Network 3 Method 3.1 Formalization 3.2 Architecture 3.3 Loss Function 4 Experiments 4.1 Experimental Setting 4.2 Effectiveness Analysis 4.3 Ablation Study for Masking Strategy 4.4 Ablation Study for Distance Learning Task 4.5 Query Length Analysis 5 Conclusion References Discriminative Feature Adaptation via Conditional Mean Discrepancy for Cross-Domain Text Classification 1 Introduction 2 Preliminary 2.1 Kernels and Hilbert Space Embedding 2.2 Hilbert Space Embedding of Conditional Distributions 3 Proposed Model 3.1 Conditional Mean Discrepancy 3.2 Aligned Adaptation Networks with Adversarial Learning 4 Experiments 4.1 Setup 4.2 Results 4.3 Analysis 5 Related Work 6 Conclusion References Discovering Protagonist of Sentiment with Aspect Reconstructed Capsule Network 1 Introduction 2 Related Work 3 The CAPSAR Model 3.1 Model Overview 3.2 Sequence Encoder 3.3 Location Proximity with Given Aspect 3.4 Capsule Layers with Sharing-Weight Routing 3.5 Model Training with Aspect Reconstruction 3.6 Combining CAPSAR with BERT 4 Experiments 4.1 Datasets 4.2 Compared Methods 4.3 Experimental Settings 4.4 Results on Standard ATSA 4.5 Results on Aspect Term Detection 5 Conclusion References Discriminant Mutual Information for Text Feature Selection 1 Introduction 2 Related Work 2.1 Text Representation 2.2 Mutual Information 2.3 mRMR 3 Discriminant Mutual Information 3.1 Text Preprocessing 3.2 Discriminant Mutual Information 4 Experiments and Analysis 4.1 Datasets 4.2 Classifiers and Evaluation Measure 4.3 Experimental Results 5 Conclusion References CAT-BERT: A Context-Aware Transferable BERT Model for Multi-turn Machine Reading Comprehension 1 Introduction 2 Related Work 2.1 Machine Reading Comprehension 2.2 Transfer Learning 3 The CAT-BERT Model 3.1 Task Description and Overall Framework 3.2 Context-Aware BERT Encoding 3.3 Transfer Learning with Task-Specific Attention 3.4 Dynamic Training Policy 4 Experiments 4.1 Datasets 4.2 Experimental Setup 4.3 Overall Results 4.4 Comparison of Transfer Policies 4.5 The Benefit from the Attention Mechanism 4.6 Error Analysis 5 Conclusion and Future Work References Unpaired Multimodal Neural Machine Translation via Reinforcement Learning 1 Introduction 2 Background 3 Methodology 3.1 Problem Definition 3.2 Overview 3.3 Reward Computation 3.4 Objective Function 3.5 Training Details 4 Experiments 4.1 Datasets 4.2 Baseline Methods 4.3 Implementation Details 4.4 Main Results 4.5 Impact of Hyper-parameter 4.6 Case Study 5 Related Work 6 Conclusion and Future Work References Multimodal Named Entity Recognition with Image Attributes and Image Knowledge 1 Introduction 2 Related Work 2.1 Traditional NER with Text only 2.2 MNER with Image and Text 2.3 Other Multimodal Tasks 3 Our Proposed Model 3.1 Problem Formulation 3.2 Introducting Image Attributes and Knowledge 3.3 Feature Extraction 3.4 Modality Fusion 3.5 Conditional Random Fields 4 Experiments 4.1 Dataset 4.2 Implementation Details 4.3 Baselines 4.4 Results and Discussion 4.5 Bad Case Analysis 5 Conclusions References Multi-task Neural Shared Structure Search: A Study Based on Text Mining 1 Introduction 2 Our Approach 2.1 Multi-task Shared Structure Encoding (SSE) 2.2 Shared Structure and Auxiliary Task Search 2.3 Variant of Vanilla NAS Approach 2.4 m-Sparse Search Approach for Neural-Based Multi-task Model (m-S4MT) 2.5 Task-Wise Greedy Generation Search Approach for Neural-Based Multi-task Model (TGG-S3MT) 3 Experiments 3.1 Datasets 3.2 Experimental Settings 3.3 Q1: Are SSE and m-S4MT Effective? 3.4 Q2: Is TGG-S3MT Effective? 3.5 Q3: Which Search Approach Is More Efficient? 4 Related Work 4.1 Multi-task Methods in Text Mining 4.2 Network Architecture Search for Multi-task Models 4.3 Peer Review Prediction 5 Conclusion References A Semi-structured Data Classification Model with Integrating Tag Sequence and Ngram 1 Introduction 2 Related Works 3 TSGram Feature 3.1 Basic Definitions 3.2 Constructing a TSGram Feature Space 4 TSGram-Based Classifier 4.1 TSGrams Class Model 4.2 Classifying Documents Using the TSGrams Class Model 5 Experimental Study 5.1 Experimental Setting 5.2 Effects of the Length and Numbers of TSGrams 5.3 Effects of TSGram Feature Selection Parameter and Feature Combination 5.4 Classification Results 6 Conclusions References Inferring Deterministic Regular Expression with Unorder and Counting 1 Introduction 2 Preliminaries 2.1 Regular Expression with Unorder and Counting 2.2 SORE, SOREUC, SOA and Unorder Unit 3 Finite Automaton with Unorder and Counting (FAUC) 3.1 Unorder Markers, Counters and Update Instructions 3.2 Finite Automata with Unorder and Counting 4 Inference of SOREUCs 4.1 Computing Unorder Units 4.2 Constructing FAUC 4.3 Running FAUC 4.4 Generating SOREUC 5 Experiments 5.1 Expressiveness of SOREUCs 5.2 Conciseness, Generalization Ability and Time Performance 6 Conclusion References MACROBERT: Maximizing Certified Region of BERT to Adversarial Word Substitutions 1 Introduction 2 Methods 2.1 Certified Region 2.2 Perturbation Distribution Based on Multi-Hop Neighbors 2.3 Robust Training by Maximizing Certified Region 3 Experiment 3.1 Experimental Data and Baselines 3.2 Results and Analysis 4 Conclusion References A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification 1 Introduction 2 Model Description 2.1 Transformer-Based Encoder 2.2 Language Model Layer 2.3 Classification Layer 2.4 K- Augmentation 2.5 Regularization 2.6 Training Process 3 Experiments 3.1 Experimental Settings 3.2 Main Results 3.3 Ablation Study 3.4 Importance of Diversity and Constraints 4 Conclusion References Neural Demographic Prediction in Social Media with Deep Multi-view Multi-task Learning 1 Introduction 2 Related Work 3 Methodology 3.1 Context View 3.2 Sentiment View 3.3 Topic View 3.4 Training Procedure 4 Experiments 4.1 Experimental Setup 4.2 Experimental Results 5 Conclusion References An Interactive NL2SQL Approach with Reuse Strategy 1 Introduction 2 Related Work 3 Approach 3.1 Task Formulation 3.2 Tree-SQL 3.3 Basic Model 3.4 Optimization with Reuse Mechanism 4 Experiment 4.1 Experiment Setup 4.2 Overall Results 4.3 Effectiveness of Reuse Strategy 5 Conclusions References Data Mining Consistency- and Inconsistency-Aware Multi-view Subspace Clustering 1 Introduction 2 Methodology 2.1 Notations 2.2 Preliminary Knowledge 2.3 The Objective Function 3 Optimization 3.1 Optimization Algorithm 3.2 Model Complexity 4 Experiments 4.1 Data Sets and Evaluation Measures 4.2 Comparison Experiments 4.3 Parameter Analysis and Convergence Analysis 5 Conclusion References Discovering Collective Converging Groups of Large Scale Moving Objects in Road Networks 1 Introduction 2 Overview 2.1 Basic Conception 2.2 Problem Definition 2.3 Framework 3 Density-Based Algorithm in Road Networks 3.1 Core Points Identification 3.2 -Neighbourhood Computation Method 3.3 The VNIndex for -Neighbourhood Computation 4 Cluster Containment Join 4.1 Cluster Containment Join Algorithm 4.2 Signature Tree Based on Road Network Partition 5 Experiment 5.1 Effectiveness 5.2 Efficiency 5.3 Scalability 6 Related Work 7 Conclusion References Efficient Mining of Outlying Sequential Behavior Patterns 1 Introduction 2 Related Work 2.1 Outlying Aspect Mining 2.2 Distinguishing Sequential Pattern Mining 2.3 Education Data Mining 3 Problem Definition 4 Design of OBP-Miner 4.1 Framework 4.2 Candidate Behavior Pattern Generation 4.3 Candidate History Time Window Generation 5 Empirical Evaluation 5.1 Experimental Setting 5.2 Effectiveness 5.3 Efficiency 6 Conclusion References Clustering Mixed-Type Data with Correlation-Preserving Embedding 1 Introduction 2 Background 2.1 Problem Definition 2.2 Correlation Between Numerical and Categorical Data 3 Related Work 4 Correlation Preserving Embedding for Categorical Features - COPE 5 Experiment Results 5.1 Experimental Methodology 5.2 Datasets 5.3 COPE - Correlation Preservation and Convergence Test 5.4 Clustering Results 5.5 Data Representation Analysis 6 Conclusions References Beyond Matching: Modeling Two-Sided Multi-Behavioral Sequences for Dynamic Person-Job Fit 1 Introduction 2 Related Work 3 Problem Formulation 4 The Proposed Approach 4.1 Dynamic Multi-key Value Memory Network 4.2 Bilateral Cascade Multi-Task Learning 5 Experiment 5.1 Datasets 5.2 Experimental Settings 5.3 Comparative Methods 5.4 Implementation Details 5.5 Experimental Results 5.6 Visualization Analysis 6 Conclusion References A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks 1 Introduction 2 Related Work 3 Methodology 3.1 Overview 3.2 Building Neighborhood Similarity Graph 3.3 Node Context in Similarity Graphs 3.4 The Low-Dimensional Embedding Representation 4 Experiments 4.1 Experimental Setup 4.2 Computational Time 4.3 Data Classification 4.4 Data Clustering 4.5 Parameter Sensitivity 5 Conclusion References AE-UPCP: Seeking Potential Membership Users by Audience Expansion Combining User Preference with Consumption Pattern 1 Introduction 2 Related Work 3 Problem Statement 4 AE-UPCP Model 4.1 Feature Representation 4.2 Personalized Preference Extraction 4.3 Consumption Pattern Extraction 4.4 Fusion Learning 4.5 Similarity Calculation 5 Experiments 5.1 Experiment Setup 5.2 Results and Analysis 6 Conclusion References Self Separation and Misseparation Impact Minimization for Open-Set Domain Adaptation 1 Introduction 2 Related Work 3 Proposed Method 3.1 Self Separation 3.2 Distribution Matching 4 Experiments 4.1 Data Preparation 4.2 Setup 4.3 Results 4.4 Abation Analysis 5 Conclusion References Machine Learning Partial Modal Conditioned GANs for Multi-modal Multi-label Learning with Arbitrary Modal-Missing 1 Introduction 2 Related Work 3 Methodology 3.1 Problem Formulation 3.2 Modal Completion 3.3 Modal Information Extraction 3.4 Label Correlation Exploitation 4 Experiment 4.1 Experimental Setup 4.2 Experimental Results 4.3 Performance Analysis 5 Conclusion References Cross-Domain Error Minimization for Unsupervised Domain Adaptation 1 Introduction 2 Related Work 3 Motivation 3.1 Problem Definition 3.2 Main Idea 3.3 Classification Error 4 Method 4.1 Empirical Error Minimization 4.2 Distribution Alignment 4.3 Cross-domain Error Minimization 4.4 Discriminative Feature Learning 4.5 Optimization 4.6 Selective Target Samples 5 Experiment 5.1 Data Preparation 5.2 Baseline Methods 5.3 Experimental Setup 5.4 Results and Analysis 5.5 Effectiveness Analysis 6 Conclusion References Unsupervised Domain Adaptation with Unified Joint Distribution Alignment 1 Introduction 2 Related Work 3 Method 3.1 Problem Setting 3.2 Overall 3.3 Class Predictor Loss 3.4 Class-Level Alignment Loss 3.5 Domain-Level Alignment Loss 3.6 SSL Regularization Loss 3.7 Overall Objective 4 Experiments 4.1 Setup 4.2 Implementation Details 4.3 Results 4.4 Analysis 5 Conclusion References Relation-Aware Alignment Attention Network for Multi-view Multi-label Learning 1 Introduction 2 Related Work 3 Methodology 3.1 Deep Correlated Subspace Learning with View Interactions 3.2 Label Correlations Learning with Multi-head Attention 3.3 Label-View Dependence Learning with Alignment Attention 3.4 Label Prediction Layer 4 Experiments 4.1 Experimental Setting 4.2 Experimental Results 4.3 Experimental Analysis 5 Conclusion References BIRL: Bidirectional-Interaction Reinforcement Learning Framework for Joint Relation and Entity Extraction 1 Introduction 2 Methodology 2.1 Relation Extraction with RL 2.2 Entity Extraction (EE) with RL 2.3 Relation Calibration (RC) with Distant Supervision 2.4 RL Policy Learning 2.5 Data Augmentation for RL 3 Experiments 3.1 Datasets and Metrics 3.2 Baselines 3.3 Experimental Settings 3.4 Performance Comparison 3.5 Ablation Study 3.6 Case Study 4 Conclusion References DFILAN: Domain-Based Feature Interactions Learning via Attention Networks for CTR Prediction 1 Introduction 2 Related Work 2.1 FM-based CTR Models 2.2 Deep Learning-Based CTR Models 3 Our Proposed Model 3.1 Input Layer 3.2 Embedding Layer 3.3 Interaction Layer 3.4 Linear Unit and MLP Component 3.5 Prediction Layer 4 Experiments 4.1 Experimental Setup 4.2 Performance Comparison (RQ1) 4.3 Different Product Operator Comparision (RQ2) 4.4 Hyper-parameter Tuning (RQ3) 5 Conclusion References Double Ensemble Soft Transfer Network for Unsupervised Domain Adaptation 1 Introduction 2 Related Work 3 The Proposed Method 3.1 Label Propagation Ensemble 3.2 Classifiers Ensemble Framework 3.3 Class-Wise Adaptation 3.4 Overall Object Function 3.5 Theoretical Analysis 4 Experiments 4.1 Experimental Settings 4.2 Implementation Details 4.3 Results and Discussion 4.4 Empirical Analysis 5 Conclusion References Attention-Based Multimodal Entity Linking with High-Quality Images 1 Introduction 2 Related Work 2.1 Muiltimodal Representation Learning 2.2 Entity Linking on Social Data 3 Proposed Approach 3.1 Problem Definition 3.2 Mention Representation 3.3 MMKG and Candidate Entity Representation 3.4 A Two-Stage Image and Text Correlation Mechanism 3.5 Attention Mechanism Based on Finding Useful Multi-hop Entities 3.6 Candidate Entities Ranking 4 Experiments 4.1 Experimantal Settings 4.2 Dataset and Baselines 4.3 Results 4.4 Error Analysis 5 Conclusions and Future Work References Learning to Label with Active Learning and Reinforcement Learning 1 Introduction 2 Related Work 3 L2L Approach 3.1 Domain Adaptation with Multi-granularity Attention 3.2 Learning to Ranking 4 Experiments 4.1 Evaluation Plan 4.2 Results 5 Conclusion References Entity Resolution with Hybrid Attention-Based Networks 1 Introduction 2 Related Work 3 Hybrid Attention-Based Networks 4 Experimental Evaluation 5 Conclusion References Information Retrieval and Search MLSH: Mixed Hash Function Family for Approximate Nearest Neighbor Search in Multiple Fractional Metrics 1 Introduction 2 Related Work 3 Working Mechanism of SLSH 3.1 Preparations 3.2 The Algorithm 3.3 More Analysis 4 Working Mechanism of MLSH 4.1 The Mixed Hash Function Family 5 Parameter Setting and Complexity Analysis 6 Experiments 6.1 Experimental Setup 6.2 Experimental Results on MLSH 6.3 The Comparison Study 6.4 Results on Deep1B 7 Conclusion References Quantum-Inspired Keyword Search on Multi-model Databases 1 Introduction 2 Preliminaries 3 The Framework of the Quantum-Inspired Keyword Search 3.1 Transformation 3.2 Online Keyword Search Query 4 Experiment 4.1 Data Sets 4.2 Queries and Answers 4.3 Results Analysis 4.4 Time and Scalability Analysis 4.5 The Dimension of Density Vectors Analysis 5 Related Works 6 Conclusion References ZH-NER: Chinese Named Entity Recognition with Adversarial Multi-task Learning and Self-Attentions 1 Introduction 2 Related Work 3 ZH-NER Model 3.1 Character and Label Encoding 3.2 Adversarial Multi-task Learning 3.3 Different Layers of the ZH-NER Model 3.4 Model Training 4 Experimental Evaluations 4.1 Evaluation Datasets and Experimental Settings 4.2 Baseline Models and Experimental Results 5 Conclusion References Drug-Drug Interaction Extraction via Attentive Capsule Network with an Improved Sliding-Margin Loss 1 Introduction 2 Background 3 Proposed Method 3.1 Extract Textual Features 3.2 Obtain Dependency Representations 3.3 Attentive Capsule Network 3.4 Weighted Exponential Sliding-Margin Loss 4 Experiments 4.1 Dataset and Experimental Settings 4.2 Overall Performance 4.3 Effect of Various Modules 5 Conclusion References Span-Based Nested Named Entity Recognition with Pretrained Language Model 1 Introduction 2 Releated Work 3 Proposed Method 3.1 Encoder Layer 3.2 Span Representation Layer 3.3 Multi-task Layer 3.4 Prediction 4 Experiment 4.1 Experimental Setup 4.2 Overall Evaluation and Ablation Study 4.3 Running Time 5 Conclusion References Poetic Expression Through Scenery: Sentimental Chinese Classical Poetry Generation from Images 1 Introduction 2 Methodology 2.1 Problem Formulation and Overview 2.2 Information Extraction 2.3 Keyword Extension 2.4 Poetry Generation 3 Experiment 3.1 Dataset 3.2 Evaluation Metrics 3.3 Model Variants and Baselines 3.4 Human and Automatic Evaluation 3.5 Case Study 4 Conclusion and Future Work References Social Network SCHC: Incorporating Social Contagion and Hashtag Consistency for Topic-Oriented Social Summarization 1 Introduction 2 Related Work 2.1 Social Summarization 2.2 Social Network Influence 2.3 Hashtags in Social Media 3 Task Description and Observations 3.1 Task and Dataset 3.2 Verification of Sociological Theories 4 Our Method 4.1 Coverage and Sparsity 4.2 Social Contagion 4.3 Hashtag Consistency 4.4 Sparse Optimization for Social Summarization 5 Experiments 5.1 Research Questions 5.2 Evaluation Metrics 5.3 Compared Methods 6 Experimental Results 6.1 The Overall Performance 6.2 Ablation Study 6.3 Parameters Settings and Analysis 7 Conclusion References Image-Enhanced Multi-Modal Representation for Local Topic Detection from Social Media 1 Introduction 2 Related Work 3 Preliminaries 3.1 Problem Description 3.2 The Framework of IEMM-LTD 4 Image-Enhanced Multi-modal Embedding 4.1 Textual and Visual Encoding 4.2 Multi-modal Embedding 5 Topic Generation 5.1 Embedding-Based Topic Model 5.2 Parameter Estimation 6 Experiments 6.1 Experimental Setups 6.2 Experimental Results 7 Conclusion References A Semi-supervised Framework with Efficient Feature Extraction and Network Alignment for User Identity Linkage 1 Introduction 2 Related Work 3 The Proposed Framework 3.1 Feature Extraction Model 3.2 Network Alignment Model 4 Experimental Results 4.1 Dataset and Experimental Settings 4.2 Baselines 4.3 Comparisons with Baselines 4.4 Effect of Feature Extraction 4.5 Effect of Network Alignment 5 Conclusion References Personality Traits Prediction Based on Sparse Digital Footprints via Discriminative Matrix Factorization 1 Introduction 2 Related Work 3 Problem Definition 4 Personality Traits Prediction with Discriminative Matrix Factorization 4.1 Objective Formulation 4.2 Solving the Optimization Problem 4.3 Inferring Personality Traits for Unlabeled Users 5 Experiments 5.1 Dataset and Experimental Setting 5.2 Experimental Results 5.3 Parameter Sensitivity 6 Conclusion References A Reinforcement Learning Model for Influence Maximization in Social Networks 1 Introduction 2 Problem Formulation 2.1 Influence Maximization in Social Networks 2.2 Graph Embedding for Representing Users 3 Reinforcement Learning Model for Selecting Seed Nodes 3.1 Reinforcement Learning Model Formulation 3.2 Parameters Learning 4 Experiments 4.1 Datasets 4.2 Social Spread Prediction 4.3 Seed Set Selection 5 Conclusion References A Multilevel Inference Mechanism for User Attributes over Social Networks 1 Introduction 2 Problem Definition 2.1 Semantic Tree 2.2 Labeled Graph 3 Attribute Inference Model 3.1 Information Propagation Model 3.2 Attribute Correction Model 4 Attribute Inference Algorithm 4.1 Algorithm Description 4.2 Time Complexity 5 Experiment 5.1 Experimental Settings 5.2 Results and Analysis 6 Related Work 7 Conclusion References Query Processing Accurate Cardinality Estimation of Co-occurring Words Using Suffix Trees 1 Introduction 2 Related Work 3 The Thin Suffix Tree 3.1 Our Vertical Pruning Approach 3.2 Character-Removing Map Functions 3.3 More Complex Map Functions 3.4 A General Character-Removing Map Function 3.5 Cases of Approximation Errors 4 Our Approach for Error Correction 4.1 Counting the Branch Conflations 4.2 Counting Fewer Input Strings 5 Experimental Evaluation 5.1 Objectives 5.2 Setup 5.3 Experiments 6 Conclusions References Shadow: Answering Why-Not Questions on Top-K Spatial Keyword Queries over Moving Objects 1 Introduction 2 Related Work 2.1 Spatial Keyword Query 2.2 Moving Objects Query 2.3 Why-Not Question 3 Preliminaries and Problem Formulation 3.1 Top-k Spatial Keyword Query over Moving Objects 3.2 Why-Not Questions on Top-k Spatial Keyword Query over Moving Objects 4 Moving Objects and Probability Distribution Function 4.1 Moving Ability 4.2 Probability Density and Probability 4.3 Probability Distribution Function and Normal Distribution 5 Shadow-Based Method on Answering Top-k WSKM Queries 5.1 The Index Structure of Shadow 5.2 Pruning Techniques 5.3 Answering Top-k WSKM Queries 6 Experiments 6.1 Experimental Setup 6.2 Experimental Result 7 Conclusion and Future Work References DBL: Efficient Reachability Queries on Dynamic Graphs 1 Introduction 2 Preliminaries 3 Related Work 4 DBL Framework 4.1 Definitions and Construction 4.2 Query Processing 5 DL and BL Update for Edge Insertions 6 Experimental Evaluation 6.1 Experimental Setup 6.2 Effectiveness of DL+BL 6.3 General Graph Updates 6.4 Synthetic Graph Updates 6.5 Parallel Performance 7 Conclusion References Towards Expectation-Maximization by SQL in RDBMS 1 Introduction 2 Preliminaries 3 Our Solution 4 The EM Training 5 Model Maintenance 6 Experimental Studies 7 Conclusion References Correction to: Database Systems for Advanced Applications Correction to: C. S. Jensen et al. (Eds.): Database Systems for Advanced Applications, LNCS 12682, https://doi.org/10.1007/978-3-030-73197-7 Author Index