
Database and Expert Systems Applications
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The 37 full papers presented together with 31 short papers in these volumes were carefully reviewed and selected from a total of 149 submissions. The papers are organized around the following topics: big data; data analysis and data modeling; data mining; databases and data management; information retrieval; prediction and decision support.
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Content
- Intro
- Preface
- Organization
- Abstracts of Keynote Talks
- Privacy in the Era of Big Data, Machine Learning, IoT, and 5G
- Don't Handicap AI without Explicit Knowledge
- Extreme-Scale Model-Based Time Series Management with ModelarDB
- Big Minds Sharing their Vision on the Future of AI (Panel)
- Contents - Part I
- Contents - Part II
- Big Data
- Reference Architecture for Running Large Scale Data Integration Experiments
- 1 Introduction
- 2 Reference Architecture: Logical Level
- 3 Reference Architecture: Implementation Level
- 4 Summary
- References
- Subgroup Discovery with Consecutive Erosion on Discontinuous Intervals
- 1 Introduction
- 2 Related Works
- 3 SD-CEDI Algorithm
- 3.1 Preliminaries
- 3.2 SD-CEDI
- 4 Experimental Results
- 4.1 Best Quality on Raw Dataset
- 4.2 Best Quality on Resized Datasets
- 4.3 Time Comparison
- 5 Conclusion
- References
- Fast SQL/Row Pattern Recognition Query Processing Using Parallel Primitives on GPUs
- 1 Introduction
- 2 Related Work
- 2.1 Implementation of SQL/RPR Based on Spark SQL
- 2.2 SQL Query Processing on GPUs
- 3 SQL/Row Pattern Recognition
- 4 Implementation of SQL/RPR on GPUs
- 4.1 Partition
- 4.2 Sorting
- 4.3 Pattern Definition Using Regular Expression
- 4.4 Pattern Matching
- 5 Evaluation
- 5.1 Method Used for Comparison
- 5.2 Experiment Setup
- 5.3 Experimental Results
- 6 Conclusion
- References
- Scalable Tabular Metadata Location and Classification in Large-Scale Structured Datasets
- 1 Introduction
- 2 Definitions
- 2.1 Non-relational Table Representation
- 3 Methodology
- 3.1 Ensemble Architecture
- 3.2 Large-Scale Evaluation Architecture
- 4 Experimental Study
- 4.1 Training the Models
- 4.2 Training Data
- 4.3 Test Data
- 5 Evaluation
- 6 Related Work
- 7 Conclusion
- References
- Unified and View-Specific Multiple Kernel K-Means Clustering
- 1 Introduction
- 2 Background
- 2.1 Kernel k-means (KKM)
- 2.2 Multiple Kernel k-means (MKKM)
- 3 The Proposed Method
- 3.1 A Variant of Kernel k-means
- 3.2 Unified and View-Specific Multiple Kernel Clustering
- 3.3 Optimization
- 4 Experiments and Analysis
- 4.1 DataSets
- 4.2 Compared Algorithms
- 4.3 Experimental Setup
- 4.4 Clustering Performance
- 4.5 Kernel Structure and Parameter Sensitivity Study
- 5 Conclusion
- References
- Data Analysis and Data Modeling
- Augmented Lineage: Traceability of Data Analysis Including Complex UDFs
- 1 Introduction
- 2 Related Work
- 3 Data Model
- 4 Augmented Lineage
- 5 Augmented Lineage Derivation
- 5.1 Segment
- 5.2 Tracing Query
- 5.3 Augmented Lineage Derivation Procedure
- 6 Implementation of Augmented Lineage Derivation
- 7 Experiment
- 8 Conclusions and Future Work
- References
- Neural Ordinary Differential Equations for the Regression of Macroeconomics Data Under the Green Solow Model
- 1 Introduction
- 2 The Green Solow Model
- 3 Related Work
- 4 Methodology
- 4.1 Baseline Method
- 4.2 Proposed Models
- 5 Performance Evaluation
- 5.1 Experimental Setup
- 5.2 Results and Discussion
- 6 Conclusion
- References
- A Quantum-Inspired Neural Network Model for Predictive BPaaS Management
- 1 Introduction
- 2 Related Work
- 3 Resource Usage Prediction Model
- 3.1 Multi-layer Perceptron for Resource Usage Prediction
- 3.2 Quantum Genetic Algorithm for the Neural Network's Training
- 4 Experiments
- 4.1 Evaluation of the Prediction Model
- 4.2 Evaluation of the Placement Quality
- 5 Conclusion and Future Work
- References
- Predicting Psychiatric Diseases Using AutoAI: A Performance Analysis Based on Health Insurance Billing Data
- 1 Introduction
- 2 Method
- 2.1 Data
- 2.2 AutoAI Frameworks, Development Environment, and Configuration
- 3 Results
- 3.1 Watson AutoAI
- 3.2 Auto-Sklearn
- 4 Discussion
- 5 Conclusion
- References
- Improving Billboard Advertising Revenue Using Transactional Modeling and Pattern Mining
- 1 Introduction
- 2 Proposed Framework of the Problem
- 3 Proposed Billboard Allocation Framework
- 4 Performance Evaluation
- 5 Conclusion
- References
- Sarcasm Detection for Japanese Text Using BERT and Emoji
- 1 Introduction
- 2 Related Works
- 3 Proposed Method
- 3.1 Definition of Sarcasm
- 3.2 Overview of Proposed Method
- 3.3 BERT: Bidirectional Encoder Representations from Transformers
- 3.4 Word2Vec
- 4 Experiment
- 4.1 Dataset and Preprocessing
- 4.2 Experimental Setup
- 4.3 Experimental Results
- 5 Conclusion
- References
- Sigmalaw PBSA - A Deep Learning Model for Aspect-Based Sentiment Analysis for the Legal Domain
- 1 Introduction
- 2 Related Work
- 2.1 Legal Information Extraction
- 2.2 Existing Aspect-Based Sentiment Analysis Models
- 3 Methodology
- 3.1 Word Embedding Layer
- 3.2 RNN Layer
- 3.3 Position Aware Attention Mechanism
- 3.4 Graph Convolution Network
- 3.5 Sentiment Classification
- 3.6 Model Training
- 4 Experiments
- 4.1 Dataset
- 4.2 Parameter Setting
- 4.3 Word Embedding Models Comparison
- 4.4 RNN Models Comparison
- 4.5 Overall Performance
- 4.6 Ablation Study
- 5 Conclusion
- References
- BERT-Based Sentiment Analysis: A Software Engineering Perspective
- 1 Introduction
- 1.1 BERT
- 2 Related Work
- 3 Proposed Work
- 3.1 Data Augmentation
- 3.2 Fine-Tuning BERT Model
- 3.3 Ensemble BERT Models
- 3.4 Compressed Model
- 4 Experiments
- 4.1 Training and Testing
- 5 Results and Discussion
- 6 Conclusion
- References
- A Stochastic Block Model Based Approach to Detect Outliers in Networks
- 1 Introduction
- 2 Model Definition
- 3 Experiments
- References
- Medical-Based Text Classification Using FastText Features and CNN-LSTM Model
- 1 Introduction
- 2 Related Works
- 3 Materials and Methods
- 3.1 Dataset
- 3.2 Feature Extraction
- 3.3 Machine Learning Algorithms
- 3.4 CNN-LSTM
- 4 Experimentations and Results
- 4.1 Experimental Flow
- 4.2 Analysis and Discussion of the Results
- 5 Conclusion
- References
- Data Mining
- Diversified Pattern Mining on Large Graphs
- 1 Introduction
- 2 Related Work
- 3 Graphs, Patterns and Pattern Mining
- 3.1 Graph Pattern Matching
- 3.2 Diversified Top-k Pattern Mining
- 4 Diversified Top-k Pattern Mining
- 5 Experimental Study
- 6 Conclusion
- References
- EHUCM: An Efficient Algorithm for Mining High Utility Co-location Patterns from Spatial Datasets with Feature-specific Utilities
- 1 Introduction
- 2 Problem Definition
- 3 The EHUCM Algorithm
- 3.1 The Search Space
- 3.2 Feature-Object Neighbor Tree (FONT)
- 3.3 Two Pruning Strategies
- 3.4 The EHUCM Algorithm
- 4 Experimental Evaluation
- 5 Conclusions
- References
- BERT-Based Multi-Task Learning for Aspect-Based Opinion Mining
- 1 Introduction
- 1.1 Problem Definition
- 1.2 Contributions
- 2 Literature Review
- 3 Proposed Approach
- 3.1 Multi-Task Learning Model
- 3.2 Aspect Term and Category Related Opinion Polarity
- 4 Steps of BERT-MTL
- 4.1 Steps of BERT-MTL for ATE and ACD Algorithm
- 4.2 Steps of BERT-MTL for Fine and Coarse-Grained Algorithm
- 5 Experimental Evaluation
- 5.1 Dataset
- 5.2 Hyper-Parameters
- 5.3 Result Analysis
- 6 Conclusions and Future Work
- References
- GPU-Accelerated Vertex Orbit Counting for 5-Vertex Subgraphs
- 1 Introduction
- 2 Preliminaries
- 2.1 Orbit Counting
- 2.2 EVOKE
- 3 Proposed Method
- 3.1 {3, 4}-Vertex Orbit Counting
- 3.2 5-Vertex Orbit Counting
- 4 Experiments
- 4.1 Experimental Setup
- 4.2 Execution Time Comparison
- 5 Related Work
- 6 Conclusion
- References
- Databases and Data Management
- Efficient Discovery of Partial Periodic-Frequent Patterns in Temporal Databases
- 1 Introduction
- 1.1 Background and Related Work
- 1.2 Motivation
- 1.3 The Contributions of This Paper
- 2 Proposed Algorithm
- 3 Experimental Results
- 3.1 Experimental Setup
- 4 Conclusions and Future Work
- References
- Database Framework for Supporting Retention Policies
- 1 Introduction
- 2 Retention Definitions and Requirements
- 3 Related Work
- 4 Policy Setting
- 5 Policy Execution
- 6 Experiments
- 7 Conclusion and Future Work
- References
- Internal Data Imputation in Data Warehouse Dimensions
- 1 Introduction
- 2 Preliminaries
- 3 Internal Data Imputation for Dimensions
- 4 Experimental Assessments
- 4.1 Datasets and Experimental Method
- 4.2 Intra-dimensional Imputation Experiments
- 4.3 Inter-dimensional Imputation Experiments
- 5 Conclusion and Future Work
- References
- Purging Data from Backups by Encryption
- 1 Introduction
- 1.1 Motivation
- 2 Background
- 2.1 Compliance Terminology
- 2.2 Database Backups and Types
- 2.3 Related Work
- 3 Our Process
- 3.1 Defining Policies
- 3.2 Encryption Process
- 3.3 Encryption on Update
- 3.4 Purging Process
- 3.5 Restore Process
- 4 Experiments
- 5 Discussion
- 5.1 Implementation
- 5.2 ACID Guarantees
- 5.3 Future Work
- 6 Conclusion
- References
- Information Retrieval
- Improving Quality of Ensemble Technique for Categorical Data Clustering Using Granule Computing
- 1 Introduction
- 2 Background and Notations
- 2.1 Information Granulation
- 2.2 Hybridization of Fuzzy Sets and Rough Sets
- 3 The Proposed Techniques
- 3.1 Information Granulation Selection Criteria
- 3.2 Fuzzy Rough Refinement Matrix
- 4 Experiments and Results
- 4.1 Evaluation of the Proposed Selection Criteria IGQS
- 4.2 Evaluation of the Proposed Refinement Techniques
- 4.3 Statistical Significance Tests
- 5 Conclusions and Future Work
- References
- Online Optimized Product Quantization for Dynamic Database Using SVD-Updating
- 1 Introduction
- 2 Preliminaries
- 2.1 Approximate k-Nearest Neighbor Search
- 2.2 Vector Quantization, Product Quantization, and Its Variants
- 2.3 Online Product Quantization for Dynamic Data
- 2.4 Other Methods Based on PQ
- 3 Proposed Method
- 3.1 Updating Codebook
- 3.2 Update Rotation Matrix Using SVD-Updating
- 3.3 Orthogonality
- 3.4 Computational Complexity
- 4 Experiments
- 4.1 Experimental Setting
- 4.2 Effects of Low-Rrank Approximation
- 4.3 Comparison with Non-online Methods
- 4.4 Comparison with Online Methods
- 5 Conclusion
- References
- Querying Collections of Tree-Structured Records in the Presence of Within-Record Referential Constraints
- 1 Introduction
- 2 Defining the Data Model
- 3 Within-Record Constraints
- 4 Querying Tree-Structured Data
- 4.1 Flattening Nested Data
- 4.2 Navigating Through References
- References
- Dealing with Plethoric Answers of SPARQL Queries
- 1 Introduction
- 2 Motivating Example
- 3 Related Work
- 4 Preliminaries and Problem Statement
- 4.1 Basic Notions
- 4.2 Notions of MFIS and XSS
- 5 Computing MFIS and XSS
- 5.1 Baseline
- 5.2 General Properties
- 5.3 Cardinality-Based Property
- 6 Experimental Evaluation
- 6.1 Results
- 7 Conclusion
- References
- Prediction and Decision Support
- Feature Selection and Software Defect Prediction by Different Ensemble Classifiers
- 1 Introduction
- 2 Related Works
- 3 Methods and Results
- 3.1 The Dataset
- 3.2 Research Methods: Feature Selection
- 3.3 Research Methods: Classification
- 3.4 Research Methods: The Ensemble of Classifiers
- 4 Conclusions and Future Work
- References
- Traffic Flow Prediction Through the Fusion of Spatial-Temporal Data and Points of Interest
- 1 Introduction
- 2 Related Work
- 2.1 Convolution Neural Network-Based Traffic Prediction Method
- 2.2 Graph Neural Network-Based Traffic Prediction Method
- 3 Preliminaries
- 4 Method
- 4.1 Gated Fusion Network
- 4.2 Hierarchical Adaptive Graph Convolution
- 4.3 Gated Convolution Module
- 4.4 Multi-task Learning Loss
- 5 Experiments
- 5.1 Experimental Settings
- 5.2 Performance Results (RQ1)
- 5.3 Ablation Experiments (RQ2)
- 5.4 Evaluation on Framework Settings (RQ3)
- 5.5 Evaluation on Spatial-Temporal Fusion Methods (RQ4)
- 6 Conclusion and Future Work
- References
- Predicting Student Performance in Experiential Education
- 1 Introduction
- 2 Preliminaries, Key Concepts and Theories
- 3 Model and Methods
- 4 Experiments, Results and Discussion
- References
- Log-Based Anomaly Detection with Multi-Head Scaled Dot-Product Attention Mechanism
- 1 Introduction
- 2 Related Works
- 2.1 Log Parsing
- 2.2 Log-Based Anomaly Detection
- 3 LogAttention Design
- 3.1 Overview
- 3.2 Log Parsing
- 3.3 Log Pattern Vectorization
- 3.4 Log Anomaly Detection
- 4 Evaluation
- 4.1 Datasets and Criteria
- 4.2 Overall Results and Analysis
- 4.3 Experiments on Log Sequences with Different Window Lengths
- 4.4 Experiments on Log Pattern Vectorization Method
- 5 Conclusion
- References
- Enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and Analysis in Emerging Environments
- 1 Introduction
- 2 Background Concepts
- 3 Genetic Pre-alignment Phase
- 4 A Novel Family of Enhanced Hybrid Scan Matching Algorithms
- 5 Experimental Study
- 5.1 Genetic Pre-alignment: Performance Analysis
- 5.2 Proposed Hybrid Algorithms: Performance Analysis
- 6 Conclusions and Future Work
- References
- A Stacking Approach for Cross-Domain Argument Identification
- 1 Introduction
- 2 Related Work
- 2.1 Argument Mining
- 2.2 Ensemble Learning
- 3 Contribution
- 3.1 Problem Statement
- 3.2 Corpora Description
- 3.3 Classical Machine Learning Model - SVM
- 3.4 Transfer Learning Model (DistilBERT-Based)
- 3.5 Overall Model (SVM + DistilBERT)
- 4 Evaluation
- 5 Conclusion
- References
- Property Analysis of Stay Points for POI Recommendation
- 1 Introduction
- 2 Related Work
- 3 Method
- 3.1 Preliminaries
- 3.2 Weighted HITS-Based Algorithm
- 4 Experiments and Results
- 5 Conclusion
- References
- Beacon Technology for Retailers - Tracking Consumer Behavior Inside Brick-and-Mortar-Stores
- 1 Introduction
- 2 Theoretical Background
- 2.1 Challenges of Brick-and-Mortar Stores
- 2.2 Beacon Technology
- 2.3 Applications of Beacon Technology in Brick-and-Mortar Stores
- 2.4 Challenges of Beacon Technology in Brick-and-Mortar Stores
- 3 Recommendations for Implementation
- 3.1 Prerequisites for Adapting Beacon Technology
- 3.2 Recommendations for Establishing Beacon Technology
- 4 Conclusion
- References
- Author Index
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