
Advances in Databases and Information Systems
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This book constitutes the proceedings of the 23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019, held in Bled, Slovenia, in September 2019.
The 27 full papers presented were carefully reviewed and selected from 103 submissions. The papers cover a wide range of topics from different areas of research in database and information systems technologies and their advanced applications from theoretical foundations to optimizing index structures. They focus on data mining and machine learning, data warehouses and big data technologies, semantic data processing, and data modeling. They are organized in the following topical sections: data mining; machine learning; document and text databases; big data; novel applications; ontologies and knowledge management; process mining and stream processing; data quality; optimization; theoretical foundation and new requirements; and data warehouses.
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Content
- Intro
- Preface
- Organization
- Abstracts of Invited Talks
- Location-in-Time Data: Compression vs. Augmentation
- Evolution of Data Management Systems: State of the Art and Open Issues
- Semantic Relational Learning
- Contents
- Data Mining
- Unsupervised Artificial Neural Networks for Outlier Detection in High-Dimensional Data
- 1 Introduction
- 2 Related Work
- 2.1 High-Dimensional Outlier Detection
- 2.2 ANNs for Outlier Detection
- 3 ANN-Based Approaches
- 3.1 Requirements and General Idea
- 3.2 Autoencoder
- 3.3 Self-Organising Maps
- 3.4 Restricted Boltzmann Machine
- 4 Evaluation
- 4.1 Parameter Selection
- 4.2 Outlier Detection Quality Evaluation
- 4.3 Runtime Evaluation
- 5 Conclusions
- References
- Improving Data Reduction by Merging Prototypes
- 1 Introduction
- 2 Background Knowledge
- 3 The dRHC and dRHC2 Algorithms
- 4 The Proposed Algorithms
- 5 Performance Evaluation
- 5.1 Experimental Setup
- 5.2 Results and Discussion
- 6 Conclusions and Future Work
- References
- Keys in Relational Databases with Nulls and Bounded Domains
- 1 Introduction
- 2 Related Work
- 3 Preliminaries
- 4 Strongly Possible Keys
- 4.1 Implication Problem
- 4.2 Armstrong Tables
- 4.3 Weak Similarity Graph
- 5 Matroids, Matchings and Strongly Possible Keys
- 5.1 Necessary Conditions
- 6 Strongly Possible Key Discovery
- 7 Conclusion and Future Directions
- A Appendix
- References
- Machine Learning
- ILIME: Local and Global Interpretable Model-Agnostic Explainer of Black-Box Decision
- 1 Introduction
- 2 Background
- 2.1 Influence Functions
- 2.2 LIME
- 3 Local Explainer ILIME
- 4 Global Attribution Using ILIME
- 5 Experimental Results
- 5.1 How Faithful ILIME to the Model Being Explained?
- 5.2 Can We Trust the Explanations of ILIME?
- 5.3 Can We Trust the Whole Model?
- 6 Conclusion and Future Work
- References
- Heterogeneous Committee-Based Active Learning for Entity Resolution (HeALER)
- 1 Introduction
- 2 Heterogeneous Committee-Based Active Learning for Entity Resolution
- 2.1 The Global Workflow
- 2.2 Initial Training Dataset Generation
- 2.3 Heterogeneous Committee
- 2.4 Training Data Candidate Selection
- 3 Evaluation
- 3.1 Experimental Setting
- 3.2 Initial Training Dataset Evaluation
- 3.3 Heterogeneous-Committee Evaluation
- 3.4 Overall Evaluation and Comparison
- 4 Related Work
- 5 Conclusions and Future Work
- References
- Document and Text Databases
- Using Process Mining in Real-Time to Reduce the Number of Faulty Products
- Abstract
- 1 Introduction
- 1.1 Real-Time Data Processing
- 1.2 Process Mining
- 1.3 Real-Time Process Mining
- 1.4 The Content of This Study
- 2 Background
- 2.1 The Manufacturing Process
- 2.2 The Log Files
- 2.3 The Creation Process of the Main Log File
- 2.4 Monitoring Software
- 3 Design and Development of the Test Environment
- 3.1 Simulator Software
- 3.2 Analyzer Software
- 4 The New Method, the RTSDA
- 4.1 Criteria for Applying the Method
- 4.2 Data Visualization Considerations
- 4.3 Data Storing
- 4.4 Data Processing
- 5 Application of RTSDA to Real Data
- 5.1 Evaluation of the Method
- 6 Conclusion and Future Work
- Acknowledgment
- References
- Pseudo-Relevance Feedback Based on Locally-Built Co-occurrence Graphs
- 1 Introduction
- 2 Related Work
- 3 Proposed Method
- 3.1 Term Co-occurrence Graphs
- 3.2 Local Co-occurrence Graphs-Based PRF
- 4 Experiments
- 4.1 Experimental Setup
- 4.2 Results and Discussion
- 5 Conclusion and Future Work
- References
- Big Data
- Workload-Awareness in a NoSQL-Based Triplestore
- 1 Introduction
- 2 Background and Related Work
- 3 WA-RDF
- 3.1 Data Fragmentation
- 3.2 Data Querying
- 4 Experimental Evaluation
- 5 Conclusion
- References
- nativeNDP: Processing Big Data Analytics on Native Storage Nodes
- 1 Introduction
- 2 Related Work
- 3 nativeNDP Framework
- 3.1 System Stack
- 3.2 Interfaces and Abstractions
- 4 Experimental Evaluation
- 4.1 Datasets and Operations
- 4.2 Experimental Setup
- 4.3 Experiment 1 - Baseline
- 4.4 Experiment 2 - Pushdown Cluster
- 4.5 Experiment 3 - Pushdown NDP Device
- 5 Conclusion
- References
- Calculating Fourier Transforms in SQL
- 1 Introduction
- 2 State of the Art
- 3 Fourier Transform in SQL
- 3.1 Theory of Fourier Transforms
- 3.2 Translation into SQL
- 4 Experimental Evaluation
- 4.1 Calculating Fourier Transforms
- 4.2 Short-Time Fourier Transform
- 5 Conclusion
- References
- Novel Applications
- Finding Synonymous Attributes in Evolving Wikipedia Infoboxes
- 1 Introduction
- 2 Problem Statement
- 3 The Approach
- 3.1 Positive and Negative Evidence for Synonymy
- 3.2 Holistic Approach for Synonym Discovery
- 4 Experimental Evaluation
- 4.1 Dataset Description
- 4.2 Qualitative Evaluation of the Effectiveness
- 4.3 Quantitative Evaluation of the Effectiveness
- 4.4 Case Study
- 5 Related Work
- 6 Conclusion
- References
- Web-Navigation Skill Assessment Through Eye-Tracking Data
- 1 Introduction
- 2 Related Work
- 2.1 Web Navigation Definition
- 2.2 Web Navigation as an Ability
- 2.3 Web Navigation as a Layout
- 2.4 Web Navigation as a Process
- 2.5 Web Literacy Estimating
- 3 Experiment: Influence of Web-Navigation Skill on User Behavior
- 3.1 Experiment Goals
- 3.2 Questionnaire
- 3.3 Apparatus
- 3.4 Session Description
- 3.5 Participants
- 3.6 Data
- 3.7 Normalization
- 3.8 Results
- 4 Conclusions
- References
- Ontologies and Knowledge Management
- Updating Ontology Alignment on the Concept Level Based on Ontology Evolution
- 1 Introduction
- 2 Related Works
- 3 Basic Notions
- 4 Updating Ontology Alignment on a Concept Level
- 5 Experimental Verification
- 6 Future Works and Summary
- References
- On the Application of Ontological Patterns for Conceptual Modeling in Multidimensional Models
- 1 Introduction
- 2 Multidimensional Modeling and Ontology Patterns
- 2.1 Multidimensional Modeling
- 2.2 Ontological Patterns as Tools for Conceptual Modeling
- 3 Piecing It All Together
- 3.1 Applying to Dimensions
- 3.2 Applying to Facts
- 4 Case Illustration on Education: Student Attendance
- 5 Conclusions
- References
- Process Mining and Stream Processing
- Accurate and Transparent Path Prediction Using Process Mining
- 1 Introduction
- 2 Preliminaries
- 3 Related Work
- 4 LaFM: Loop-Aware Footprint Matrix
- 4.1 LaFM Data Structure
- 4.2 Training Phase: Building LaFM
- 4.3 Prediction Phase: Using LaFM
- 5 Evaluation Procedure
- 6 LaFM: Evaluation
- 7 c-LaFM: Clustered Loop-Aware Footprint Matrix
- 8 c-LaFM: Evaluation
- 9 Conclusion
- References
- Contextual and Behavioral Customer Journey Discovery Using a Genetic Approach
- 1 Introduction
- 2 Customer Journey Discovery
- 3 Related Work
- 4 A Genetic Algorithm for Customer Journey Discovery
- 4.1 Preprocessing
- 4.2 Initial Population
- 4.3 Assignment of Actual Journeys
- 4.4 CJM Evaluation Criteria
- 4.5 Stopping Criterion
- 4.6 Genetic Operations
- 5 Evaluation Using Synthetic Datasets
- 5.1 Datasets
- 5.2 Metrics
- 5.3 Settings
- 5.4 Results
- 6 Experiments Using Real Datasets
- 7 Conclusion
- References
- Adaptive Partitioning and Order-Preserved Merging of Data Streams
- 1 Introduction
- 2 Related Work
- 2.1 Determining the Number of Partitions
- 2.2 State Handling
- 3 Data Stream Processing
- 3.1 Linear Road Streaming Benchmark
- 3.2 Adaptive Partitioning
- 3.3 Order-Preserving Merge
- 4 Experimental Analysis
- 4.1 Micro-benchmarks
- 4.2 Linear Road
- 5 Conclusion
- References
- Data Quality
- CrowdED and CREX: Towards Easy Crowdsourcing Quality Control Evaluation
- 1 Introduction
- 2 Crowdsourcing Quality Control
- 3 Specifications
- 4 State-of-the-Art of Crowdsourcing Evaluation Datasets
- 5 CrowdED: Crowdsourcing Evaluation Dataset
- 5.1 Raw Data Preparation
- 5.2 Data Collection
- 5.3 Data Structure and Statistics
- 6 CREX: CReate, Enrich, eXtend
- 6.1 Data Preparation Component (CREX-D)
- 6.2 Campaign Management Component (CREX-C)
- 7 CrowdED and CREX Re-usability
- 7.1 Usability in Quality Control Evaluation
- 7.2 Compliance with the FAIR Principles
- 8 Conclusion
- References
- Query-Oriented Answer Imputation for Aggregate Queries
- 1 Introduction
- 2 Imputation Model
- 3 Related Work
- 4 Query Imputation Process
- 5 Experiments
- 6 Conclusion
- References
- Optimization
- You Have the Choice: The Borda Voting Rule for Clustering Recommendations*-14pt
- 1 Introduction
- 2 Related Work
- 3 Background
- 3.1 The k-means and k-means++ Clustering Algorithms
- 3.2 Similarity Measures
- 4 Borda Social Choice Clustering
- 4.1 The Borda Social Choice Voting Rule
- 4.2 The Borda Clustering Algorithm
- 4.3 Convergence
- 4.4 Complexity
- 5 Synthetic Experiments
- 5.1 Benchmark Settings
- 5.2 Evaluation
- 6 Quality Experiments
- 6.1 Settings
- 6.2 Lessons Learned
- 7 Conclusion and Outlook
- References
- BM-index: Balanced Metric Space Index Based on Weighted Voronoi Partitioning*-10pt
- 1 Introduction
- 1.1 Problem Definition and Contributions
- 2 Related Work
- 2.1 Backgrounds and Indexing of Pivot Permutations
- 2.2 Searching of Pivot Permutations
- 3 Balanced Indexing with Weighted Voronoi Partitioning
- 3.1 Weighted Voronoi Partitioning in Metric Space
- 3.2 Setting Weights
- 3.3 Balancing Cells
- 3.4 Consistency of One Step in Weight Modification
- 3.5 Convergence of the Balancing Algorithm
- 3.6 Indexing with Recursive Weighted Voronoi Partitioning
- 4 Efficiency Evaluation
- 4.1 Setup
- 4.2 Querying Performance
- 4.3 Construction Costs
- 4.4 Overall Efficiency of the Proposed Algorithm
- 5 Conclusions and Future Work
- References
- Theoretical Foundation and New Requirements
- ProSA-Using the CHASE for Provenance Management
- 1 Introduction
- 2 Basic Notions and State of the Art
- 2.1 The CHASE Algorithm
- 2.2 Data Provenance
- 3 Invertible Query Evaluation
- 3.1 Research Data Management
- 3.2 CHASE&BACKCHASE
- 3.3 Provenance Using CHASE
- 4 Many Application Areas - One Tool: ChaTEAU
- 4.1 Theoretical Foundation of ChaTEAU
- 4.2 ChaTEAU
- 5 ProSA Using ChaTEAU
- 6 Other Applications of the CHASE
- 7 Conclusion and Future Work
- References
- ECHOES: A Fail-Safe, Conflict Handling, and Scalable Data Management Mechanism for the Internet of Things
- 1 Introduction
- 2 Application Case
- 3 Requirement Specification
- 4 Related Work
- 5 The ECHOES Protocol
- 6 Implementation of ECHOES
- 7 Evaluation
- 8 Conclusion
- References
- Transaction Isolation in Mixed-Level and Mixed-Scope Settings
- 1 Introduction
- 2 Transactions, Schedules, and Serialization
- 3 Concurrency-Based Isolation Levels
- 4 Multiscope Serializable Isolation
- 5 Conclusions and Further Directions
- References
- Data Warehouses
- Data Reduction in Multifunction OLAP
- 1 Introduction
- 2 Related Work
- 2.1 Visualization Methods
- 2.2 Data Reduction Methods
- 3 Preliminaries
- 3.1 Multifunction Multidimensional Conceptual Data Model
- 3.2 Case Study
- 4 Data Reduction Method
- 4.1 Study the Current OLAP Query
- 4.2 Find Possible Rollup Operations
- 4.3 Calculate the Data Size for All Possible Rollups
- 4.4 Choose a Rollup Operation Based on a Selection Strategy
- 4.5 Realize the Chosen Rollup
- 5 Implementation
- 6 Conclusion
- References
- A Framework for Learning Cell Interestingness from Cube Explorations
- 1 Introduction
- 2 Related Work
- 3 Interestingness Aspects for Cube Exploration
- 3.1 Interestingness Aspects
- 3.2 Definition of Interestingness
- 4 Detecting Interesting Cells in an Exploration
- 4.1 Relevance
- 4.2 Novelty
- 4.3 Surprise
- 4.4 Peculiarity
- 5 Experiments
- 5.1 Experimental Setup
- 5.2 Lessons Learned
- 6 Conclusions
- References
- Towards a Cost Model to Optimize User-Defined Functions in an ETL Workflow Based on User-Defined Performance Metrics
- 1 Introduction
- 2 Running Example
- 2.1 Overview of the Use Case
- 2.2 Use Case for Running Example
- 3 Motivation
- 4 Proposed Cost Model
- 4.1 Stage 1 - Feasibility
- 4.2 Stage 2 - Degree of Parallelism
- 4.3 Stage 3 - Optimal Code Generation
- 4.4 Preliminary Results
- 5 Related Work
- 6 Conclusion
- References
- Author Index
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