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[null,null,"Titolo",null,"Database systems for advanced applications : DASFAA 2021 international workshops : BDQM, GDMA, MLDLDSA, MobiSocial, and MUST, Taipei, Taiwan, April 11-14, 2021 : proceedings / / Christian S. Jensen [and seven others], editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-73216-9"]
[null,null,"Descrizione fisica",null,"1 online resource (xiii, 446 pages)"]
[null,null,"Collana",null,"Lecture notes in computer science ; ; 12680"]
[null,null,"Disciplina",null,"005.74"]
[null,null,"Soggetti",null,"Database management"]
[{"$nulls":4},"Databases"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- The 6th International Workshop on Big Data Quality Management -- ASQT: An Efficient Index for Queries on Compressed Trajectories -- 1 Introduction -- 2 Related Work -- 2.1 Trajectory Compression Algorithm -- 2.2 Existing Similarity Measures -- 3 The ASQT Index -- 3.1 Index Construction -- 3.2 The Properties of ASQT -- 4 Queries on ASQT -- 4.1 Trajectory Range Query Processing on ASQT -- 4.2 Trajectory Similarity Query on ASQT -- 5 Experiments -- 5.1 Experiment Settings -- 5.2 Experiment Results -- 6 Conclusion -- References -- ROPW: An Online Trajectory Compression Algorithm -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Basic Notations -- 3.2 Line Simplification Algorithms -- 4 Algorithm ROPW -- 4.1 Motivation -- 4.2 Error-Based Metrics -- 4.3 ROPW -- 5 Experiments -- 5.1 Experimental Data Set -- 5.2 Experiment Settings -- 5.3 Experiment Results -- 6 Conclusion -- References -- HTF: An Effective Algorithm for Time Series to Recover Missing Blocks -- 1 Introduction -- 2 Related Work -- 3 Backgroud -- 3.1 Definition -- 3.2 Patterns of Missing Blocks -- 4 Hankelized Tensor Factorization -- 4.1 Methodology -- 4.2 Relation to Singular Spectrum Analysis -- 5 Experiments -- 5.1 Experiment Setting -- 5.2 Recovery Effect -- 5.3 Result Conclusion -- 6 Conclusion -- References -- LAA: Inductive Community Detection Algorithm Based on Label Aggregation -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 LAA "]
[{"$nulls":4},"Framework -- 4.1 AGM -- 4.2 Label Aggregation -- 4.3 Parameter Training -- 5 Experiments -- 5.1 Datasets -- 5.2 Evaluation -- 5.3 Baseline Methods -- 5.4 Setup -- 5.5 Experiment Results -- 6 Conclusions -- References -- Modeling and Querying Similar Trajectory in Inconsistent Spatial Data -- 1 Introduction -- 1.1 Modeling Inconsistent Spatial Data."]
[{"$nulls":4},"1.2 Nearest Entities in Inconsistent Spatial Data -- 1.3 Contributions -- 2 Problem Definition -- 3 Frequent Probabilistic Nearest Neighbor Query Processing -- 3.1 The Computation of the Nearest Neighbor Probability -- 3.2 Frequent Probability Nearest Neighbor Lookup Basic Query Algorithm -- 3.3 Probability Nearest Neighbor Advanced Query Algorithm -- 3.4 The Calculation of the Probability Upper Bounds -- 4 Experiments -- 4.1 Experimental Configuration -- 4.2 Analysis of Experimental Results -- 5 Conclusion -- References -- The 5th International Workshop on Graph Data Management and Analysis -- ESTI: Efficient k-Hop Reachability Querying over Large General Directed Graphs -- 1 Introduction -- 2 Problem Definition and Overview -- 2.1 Problem Definition -- 2.2 Overview -- 3 Offline Indexing -- 3.1 FELINE+ Index -- 3.2 Extended Spanning Tree Index for General Directed Graph -- 3.3 Summary of Offline Indexing -- 4 Online Querying -- 4.1 Basic Query Process -- 4.2 Pruning Strategies -- 4.3 Summary of Online Querying -- 5 Experiments -- 5.1 Datasets -- 5.2 Performance of FELINE+ -- 5.3 Queries with Different k -- 5.4 Comparison with the State-of-art -- 6 Related Works -- 6.1 Reachabilty Query -- 6.2 k-hop Reachabilty Query -- 7 Conclusion -- References -- NREngine: A Graph-Based Query Engine for Network Reachability -- 1 Introduction -- 1.1 Key Contributions -- 2 Related Work -- 3 Overview -- 3.1 Problem Definitions -- 3.2 System Architecture -- 4 The Offline Part of NREngine -- 4.1 Network Reachability Model Based on Network Security Policies -- 4.2 Knowledge Graph of Network Reachability -- 5 The Online Part of NREngine -- 5.1 Structured Query Language for Network Rechability -- 5.2 Execution of NRQL Statements -- 5.3 Analysis -- 6 Experiments -- 6.1 Setting -- 6.2 Experiments Results of Offline Part -- 6.3 Experiments Results of Online Part."]
[{"$nulls":4},"7 Conclusion -- References -- An Embedding-Based Approach to Repairing Question Semantics -- 1 Introduction -- 2 Overview of Our Approach -- 3 Our Approach -- 3.1 Question Formalization -- 3.2 Natural Question Representation -- 3.3 Question Completion -- 3.4 Natural Question Generation -- 4 Experiments and Evaluation -- 4.1 Experiment Setup -- 4.2 Effectiveness of Question Completion -- 4.3 Comparison to Question Generation (QG) -- 4.4 Evaluating Time-Aware Recommendation -- 4.5 Applications of Question Completion -- 4.6 Error Analysis -- 5 Related Works -- 6 Conclusions -- References -- Hop-Constrained Subgraph Query and Summarization on Large Graphs -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Statement -- 3 Existing Work -- 3.1 Reachability/k-hop Reachability -- 3.2 Shortest Path/k-shortest Paths -- 3.3 Path Enumeration/Top-k Path Enumeration -- 4 KHSQ: The K-hop Subgraph Query Algorithm -- 4.1 Rationale -- 4.2 Algorithm -- 4.3 Analysis -- 5 KHGS: The k-hop s-t Graph Summarization Algorithm -- 5.1 Problem Overview -- 5.2 Finding Skeleton Nodes -- 5.3 Summarized Graph Construction -- 6 Experiments -- 6.1 Experimental Settings -- 6.2 Efficiency -- 7 Conclusion -- References -- Ad Click-Through Rate Prediction: A Survey -- 1 Introduction -- 2 Overview of Machine Learning CTR Prediction Models -- 2.1 Factorization Machine (FM) -- 2.2 Field-Aware Factorization Machine (FFM) -- 3 Overview of Deep Learning Prediction CTR Models -- 3.1 Deep Neural Network Model (DeepCTR) -- 3.2 "]
[{"$nulls":4},"Factorisation-Machine Supported Neural Network (FNN) -- 3.3 Product-Based Neural Network (PNN) -- 3.4 Wide &amp -- Deep Learning Model -- 3.5 DeepFM -- 3.6 Deep Knowledge-Aware Network (DKN) -- 3.7 InteractionNN -- 3.8 Deep Interest Network (DIN) -- 3.9 RippleNet -- 4 Challenges of CTR Prediction -- 5 Conclusions -- References."]
[{"$nulls":4},"An Attention-Based Approach to Rule Learning in Large Knowledge Graphs -- 1 Introduction -- 2 Preliminaries -- 2.1 Knowledge Graphs and Rules -- 2.2 Representation Learning -- 3 Attention-Based Rule Learning (ARL) -- 3.1 Hop-Based Sampling -- 3.2 Attention-Based Predicate Selection -- 4 Result and Discussion -- 4.1 Experimentation -- 4.2 Rule Learning -- 4.3 Link Prediction -- 5 Conclusion -- References -- The 1st International Workshop on Machine Learning and Deep Learning for Data Security Applications -- Multi-scale Gated Inpainting Network with Patch-Wise Spacial Attention -- 1 Introduction -- 2 Related Work -- 2.1 Image Inpainting -- 2.2 Feature Matching and Feature Selection -- 3 Approach -- 3.1 An Overview of Our Model (GS-Net) -- 3.2 Multi-scale Gated Inpainting Module (MG) -- 3.3 Patch-Wise Spacial Attention Module (PSA) -- 4 Loss Function -- 5 Experiments -- 5.1 Datasets and Experimental Details -- 5.2 Qualitative Comparisons -- 5.3 Quantitative Comparisons -- 5.4 Ablation Study and Discussion -- 6 Conclusion -- References -- An Improved CNNLSTM Algorithm for Automatic Detection of Arrhythmia Based on Electrocardiogram Signal -- 1 Introduction -- 2 Materials and Methods -- 2.1 Description of Dataset -- 2.2 Networks -- 2.3 Experimental Detail and Results -- 3 Conclusion -- References -- Cross-Domain Text Classification Based on BERT Model -- 1 Introduction -- 2 Related Work -- 2.1 BERT Model -- 2.2 Matrix Preprocessing -- 2.3 K-means Clustering -- 2.4 Mean-Shift Clustering -- 3 Model Specification -- 4 Experimental Analysis -- 4.1 Experimental Design -- 4.2 Evaluation Indices -- 4.3 Analysis of Experimental Results -- 5 Conclusions -- References -- Surface Defect Detection Method of Hot Rolling Strip Based on Improved SSD Model -- 1 Introduction -- 2 SSD Model and its Improved Method -- 2.1 SSD Model -- 2.2 Knowledge Distillation Algorithm."]
[{"$nulls":4},"2.3 Residual Network -- 2.4 Feature Fusion -- 2.5 Attention Mechanism -- 3 RAF-SSD Model -- 3.1 RAF-SSD Model Structure -- 3.2 Priority Box Setting and Matching -- 3.3 Loss Function -- 4 Experiment -- 4.1 Experimental Environment -- 4.2 Experimental Data -- 4.3 Experimental Results -- 5 Conclusion -- References -- Continuous Keystroke Dynamics-Based User Authentication Using Modified Hausdorff Distance -- 1 Introduction -- 2 Related Work -- 2.1 Feature Extraction and Preprocessing -- 2.2 User Model Construction -- 3 Proposed Approach -- 3.1 Feature Extraction -- 3.2 Quantile Discretization -- 3.3 Hausdorff Distance -- 3.4 Sum of Maximum Coordinate Deviations -- 3.5 K Nearest Neighbors -- 4 Results -- 5 Conclusion -- References -- Deep Learning-Based Dynamic Community Discovery -- 1 Introduction -- 2 Spatio-Temporal Feature Extraction Method -- 2.1 Principle Explanation -- 2.2 General Description -- 3 Dynamic Community Discovery Method -- 3.1 Algorithm -- 3.2 Time Complexity Analyses -- 4 Experimental Results and Discussions -- 4.1 Experimental Environments -- 4.2 Experimental Results and Analyses -- 5 Conclusions and Future Work -- References -- 6th International Workshop on Mobile Data Management, Mining, and Computing on Social Network -- Deep Attributed Network Embedding Based on the PPMI -- 1 Introduction -- 2 Related Work -- 2.1 Network Embedding -- 2.2 Attributed Network Embedding -- 3 The Proposed Model -- 3.1 Problem Definition -- 3.2 The Architecture of Proposed Model -- 4 Experiments and Results -- 4.1 Datasets -- "]
[{"$nulls":4},"4.2 Baselines -- 4.3 Parameter Settings -- 4.4 Node Classification -- 4.5 Node Clustering -- 4.6 Network Visualization -- 4.7 Sensitivity Analysis of Parameters -- 5 Conclusion -- References -- Discovering Spatial Co-location Patterns with Dominant Influencing Features in Anomalous Regions -- 1 Introduction -- 2 Basic Concepts."]
[{"$nulls":4},"3 Mining Algorithms."]