
Database and Expert Systems Applications
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
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.
More details
Other editions
Additional editions

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 II
- Contents - Part I
- Authenticity, Privacy, Security and Trust
- Less is More: Feature Choosing under Privacy-Preservation for Efficient Web Spam Detection
- 1 Introduction
- 2 The PPGAFS Approach
- 2.1 Preselecting Privacy-Preserving Features
- 2.2 Generating Minimum Feature Subset Based on the Improved GA
- 3 Spam Detection and Verification Experiment Analysis
- 3.1 Web Spam Detection Procedure
- 3.2 Dataset and Evaluation Measures
- 3.3 Experiment Design and Result Analysis
- 4 Conclusion
- References
- Construction of Differentially Private Summaries Over Fully Homomorphic Encryption
- 1 Introduction
- 2 Preliminaries
- 2.1 Homomorphic Encryption
- 2.2 Differential Privacy
- 3 Related Work
- 3.1 Combination of Homomorphic Encryption and Differential Privacy
- 3.2 Range Queries Under Differential Privacy
- 4 Proposed Method
- 4.1 Overview
- 4.2 Adoption of Differential Privacy over Fully Homomorphic Encryption
- 4.3 Security Analysis
- 5 Experimental Evaluation
- 5.1 Experimental Setup
- 5.2 DP-Summary Construction Time
- 5.3 Accuracy of DP-Summary
- 6 Conclusion
- References
- SafecareOnto: A Cyber-Physical Security Ontology for Healthcare Systems
- 1 Introduction
- 2 Safecare Ontology
- 3 Knowledge Acquisition
- 4 Formalization and Implementation
- 4.1 Concepts Identification
- 4.2 Relationships Identification
- 4.3 Axioms Definition
- 4.4 Implementation
- 5 Safecare Use Cases
- 6 Related Work
- 7 Conclusion
- References
- Repurpose Image Identification for Fake News Detection
- 1 Introduction
- 2 Related Work
- 3 Proposed Framework
- 3.1 Event Type Classifier
- 3.2 Image Repurpose Detector
- 4 Experimental Evaluation
- 4.1 Experimental Datasets
- 4.2 Experiments on Event Type Classification
- 4.3 Comparative Study
- 4.4 Variants of RECAST
- 4.5 Case Study
- 5 Conclusion
- References
- Data and Information Processing
- An Urgency-Aware and Revenue-Based Itemset Placement Framework for Retail Stores
- 1 Introduction
- 2 Proposed Framework of the Problem
- 3 URIP: Urgency-Aware Itemset Placement Scheme
- 4 Performance Evaluation
- 5 Conclusion
- References
- NV-QALSH: An NVM-Optimized Implementation of Query-Aware Locality-Sensitive Hashing
- 1 Introduction
- 2 Preliminaries
- 2.1 The c-ANN Search Problem
- 2.2 The QALSH Method
- 2.3 Non-Volatile Memory
- 2.4 LB-Tree and LB-QALSH
- 3 Optimization Designs
- 3.1 Three-Level Storage Architecture
- 3.2 Leaf Node Optimization
- 3.3 Collision Counting Granularity Optimization
- 4 Experiments
- 4.1 Experiment Setup
- 4.2 Datasets and Queries
- 4.3 Evaluation Metrics
- 4.4 Benchmark Methods
- 4.5 Results and Analysis
- 5 Conclusion
- References
- NCRedis: An NVM-Optimized Redis with Memory Caching
- 1 Introduction
- 2 Implementation of NCRedis
- 2.1 Architecture of NCRedis
- 2.2 Log-Free Designs of LFSlab
- 2.3 Handling Persistent Memory Leak by LFSlab
- 2.4 Log-Free Designs of NCRedis
- 3 Evaluation
- 3.1 Experimental Setup
- 3.2 Memtier Benchmark Test
- 4 Conclusions
- References
- A Highly Modular Architecture for Canned Pattern Selection Problem
- 1 Introduction
- 2 System Architecture
- 2.1 Graph Similarity Module
- 2.2 Graph Clustering Module
- 2.3 Graph Connection Module
- 2.4 Pattern Mining Module
- 3 Conclusions
- References
- AutoEncoder for Neuroimage
- 1 Introduction
- 2 The Proposed Approach
- 2.1 Variational AutoEncoder Based Regression
- 2.2 Supervised Linear Autoencoder
- 2.3 Implementation Details
- 3 Experiments
- 4 Conclusion
- References
- Knowledge Discovery
- Towards New Model for Handling Inconsistency Issues in DL-Lite Knowledge Bases
- 1 Introduction
- 2 Related Works
- 3 DL-Lite Ontology and Management of Inconsistencies: An Overview
- 4 Most-Possible Repair Proposed Approach
- 4.1 Most-Possible Repair Algorithm
- 4.2 Experimental Study and Results Analysis
- 5 Conclusion and Prospects
- References
- ContextWalk: Embedding Networks with Context Information Extracted from News Articles
- 1 Introduction
- 2 Related Work
- 3 Dataset
- 3.1 Challenges
- 4 Algorithm
- 4.1 Context Embedding
- 4.2 ContextWalk
- 4.3 Complexity
- 5 Experiments
- 5.1 Compare Clusterings
- 5.2 Network and Embedding Distances
- 6 Discussion
- References
- FIP-SHA - Finding Individual Profiles Through SHared Accounts
- 1 Introduction
- 2 Background
- 3 Related Work
- 4 FIP-SHA
- 4.1 Session Representation
- 5 Experimental Evaluation Setup and Metrics
- 6 Results
- 6.1 Cut Off Sessions
- 6.2 Clustering
- 6.3 Analysis of (Weighted) User Separation
- 6.4 Discussion
- 7 Final Considerations
- References
- A Tag-Based Transformer Community Question Answering Learning-to-Rank Model in the Home Improvement Domain
- 1 Introduction
- 2 Related Work
- 3 Task Definition
- 4 Our Approach
- 4.1 Transformer Models
- 4.2 Input and Tag Representation
- 4.3 CQA Pair Matching Model
- 4.4 Model Optimisation
- 4.5 Candidate Answers Ranking
- 5 Dataset Building and Validation
- 5.1 Subjective CQA
- 5.2 Gold Standard Definition
- 6 Evaluation
- 6.1 Experiment Setup
- 6.2 Rank-Aware Evaluation Metrics
- 6.3 Results
- 7 Conclusion
- References
- An Autonomous Crowdsourcing System
- 1 Introduction
- 2 Related Work
- 3 Crowdsourcing Task
- 3.1 Workflow
- 4 Experimental Evaluation
- 4.1 Experimental Setup
- 4.2 Results
- 5 Conclusion
- References
- Machine Learning
- The Effect of IoT Data Completeness and Correctness on Explainable Machine Learning Models
- 1 Introduction
- 2 Related Work
- 3 Method
- 4 Observation, Analysis and Validation
- 5 Conclusion
- References
- Analysis of Behavioral Facilitation Tweets for Large-Scale Natural Disasters Dataset Using Machine Learning
- 1 Introduction
- 2 Related Work
- 3 Extraction of Behavioral Facilitation Tweets
- 3.1 A Classifier Based on LSTM
- 3.2 A Classifier Based on BiLSTM
- 3.3 A Classifier Based on BERT
- 4 Experiment 1: Comparison of Models for Classification Accuracy
- 4.1 Data
- 4.2 Method
- 4.3 Result
- 5 Experiment 2: Analysis Characteristics of BF-Tweets in a Large-Scale Disaster Situation
- 5.1 Experimental Conditions
- 5.2 Results
- 5.3 Discussion
- 6 Conclusion
- References
- Using Cross Lingual Learning for Detecting Hate Speech in Portuguese
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 4 Evaluation and Results
- 5 Final Remarks
- References
- MMEnsemble: Imbalanced Classification Framework Using Metric Learning and Multi-sampling Ratio Ensemble
- 1 Introduction
- 2 Related Work: Resampling Approaches
- 2.1 Oversampling
- 2.2 Undersampling
- 3 MMEnsemble
- 3.1 Base Ensemble Classifier - MLEnsemble
- 3.2 Ensemble Using Asset-Based Weighting
- 4 Experimental Evaluation
- 4.1 Settings
- 4.2 Results
- 4.3 Lessons Learned
- 5 Conclusion
- References
- Evaluate the Contribution of Multiple Participants in Federated Learning
- 1 Introduction
- 2 Method
- 2.1 Shapley Value for Models
- 2.2 Invalid Shapley Value
- 2.3 Method
- 2.4 Properties
- 3 Experiment
- 3.1 Utility Function
- 3.2 Noisy Labels
- 4 Conclusion
- References
- DFL-Net: Effective Object Detection via Distinguishable Feature Learning
- 1 Introduction
- 2 Related Work
- 3 Design of DFL-Net
- 3.1 High-Level Idea of DFL-Net
- 3.2 Full-Scale Fusion
- 3.3 Attention Guided Feature Refinement
- 4 Performance Evaluation
- 4.1 Settings
- 4.2 Results
- 4.3 Ablation Study
- 5 Conclusion and Future Work
- References
- Transfer Learning for Larger, Broader, and Deeper Neural-Network Quantum States
- 1 Introduction
- 2 Related Work
- 3 Background
- 3.1 Quantum Many-Body Systems
- 3.2 Deep Neural-Network Quantum States
- 4 Methodology
- 5 Performance Evaluation
- 5.1 Broader Networks
- 5.2 Deeper Networks
- 6 Conclusion
- References
- LGTM: A Fast and Accurate kNN Search Algorithm in High-Dimensional Spaces
- 1 Introduction
- 2 Theoretical Motivation
- 2.1 Preliminary
- 2.2 Theoretical Foundation
- 3 LGTM: From Theory to Practice
- 3.1 Pre-processing
- 3.2 Online (Query) Processing
- 4 Experiment
- 4.1 Comparison with AKNNG
- 4.2 Comparison with State-of-the-art Algorithms
- 5 Conclusion
- References
- TSX-Means: An Optimal K Search Approach for Time Series Clustering
- 1 Introduction
- 2 Notations and Definitions
- 3 TSX-Means: A New Method for Time Series Clustering
- 3.1 Principle of the Method
- 3.2 TSX-Means Algorithm
- 4 Experimental Results
- 5 Conclusion and Perspectives
- References
- A Globally Optimal Label Selection Method via Genetic Algorithm for Multi-label Classification
- 1 Introduction
- 2 Preliminaries
- 3 The Proposed Method
- 3.1 Uninformative Label Reduction via EBMD
- 3.2 Most Informative Label Selection via GA
- 3.3 Label Selection Algorithm Combining EBMD and GA
- 4 Experiments
- 4.1 Basic Experimental Settings
- 4.2 Experimental Results and Analysis
- 5 Conclusions
- References
- Semantic Web and Ontologies
- Discovering HOI Semantics from Massive Image Data
- 1 Introduction
- 2 Relate Work
- 2.1 Two-Stage Methods for HOI Detection
- 2.2 One-Stage Methods for HOI Detection
- 3 Framework for HOI Detection
- 3.1 Architecture
- 3.2 Object Detection Branch
- 3.3 Interaction Detection Branch
- 3.4 Loss and Inference
- 4 Performance Evaluation
- 4.1 Experimental Setting
- 4.2 Implementation Details
- 4.3 Results and Comparison
- 4.4 Ablation Study
- 5 Conclusion
- References
- Fuzzy Ontology-Based Possibilistic Approach for Document Indexing Using Semantic Concept Relations
- 1 Introduction
- 2 Related Work
- 3 Fuzzy Ontology-Based Possibilistic Proposed Approach
- 4 Analysis of Experimental Results and Discussion
- 5 Conclusion and Prospects
- References
- Multi-Objective Recommendations and Promotions at TOTAL
- 1 Introduction
- 2 Data Model and Preliminaries
- 3 Multi-Objective Recommendations and Promotions
- 3.1 Budget Recommendations
- 3.2 Business Recommendations
- 3.3 Promotional Offers
- 4 Experiments
- 4.1 Experimental Protocol
- 4.2 Evaluation Measures
- 4.3 Recommendation Experiments
- 4.4 Promotion Experiments
- 5 Related Work
- 6 Conclusion
- References
- An Effective Algorithm for Classification of Text with Weak Sequential Relationships
- 1 Introduction
- 2 Related Work
- 3 Our Model
- 3.1 B-BRNN Improved by Feature Fusion
- 3.2 Dynamic Decision Hybrid Model
- 4 Experimental Evaluation
- 4.1 Experiment Design
- 4.2 Experimental Results
- 5 Conclusions and Future Work
- References
- PatRIS: Patent Ranking Inventive Solutions
- 1 Introduction
- 2 Related Work
- 3 Typical Patent Inventiveness Indicators
- 4 Patent Ranking Inventive Solutions: PatRIS -1em
- 5 Dataset and Experimental Settings
- 6 Case Study
- 7 Conclusion and Future Work
- References
- Temporal, Spatial, and High Dimensional Databases
- Shared-Memory Parallel Hash-Based Stream Join in Continuous Data Streams
- 1 Introduction
- 2 Proposed Parallel Hash-Based Stream Joins
- 2.1 Parallel Symmetric Hash Join Algorithm
- 2.2 Chunk-Based Pairing Hash Join Algorithm
- 3 Empirical Evaluation
- 3.1 Scalability and Performance Evaluation
- 3.2 Latency Evaluation
- 4 Related Work and Conclusion
- References
- Event Related Data Collection from Microblog Streams
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 4 Baseline
- 5 Experiments
- 5.1 Dataset
- 5.2 System Configuration
- 5.3 Short Term Events
- 5.4 Medium Term Events
- 5.5 Long Term Events
- 5.6 System Performance
- 5.7 Human Judgements
- 6 Conclusion
- References
- GACE: Graph-Attention-Network-Based Cardinality Estimator
- 1 Introduction
- 2 Related Work
- 2.1 Traditional Cardinality Estimation Methods
- 2.2 Learning-Based Cardinality Estimation
- 3 Overview
- 4 Encoding and Model
- 4.1 Encoding
- 4.2 Model
- 5 Evaluation
- 5.1 Experimental Setup
- 5.2 Dynamic Features for Query Structure
- 5.3 Estimation Accuracy
- 6 Conclusion
- References
- A Two-Phase Approach for Enumeration of Maximal (, )-Cliques of a Temporal Network
- 1 Introduction
- 2 Problem Definition
- 3 Proposed Methodology
- 3.1 Phase 1 (Initialization)
- 3.2 Phase 2 (Enumeration)
- 4 Experimental Results
- 5 Conclusion
- References
- Author Index
System requirements
File format: PDF
Copy protection: Watermark-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Use the free software Adobe Reader, Adobe Digital Editions, or any other PDF viewer of your choice (see eBook Help).
- Tablet/Smartphone (Android; iOS): Install the free app Adobe Digital Editions or another reading app for eBooks, e.g., PocketBook (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Watermark-DRM, a „soft” copy protection. This means that there are no technical restrictions to prevent illegal distribution. However, there is a personalised watermark embedded in the eBook that can be used to identify the purchaser of the eBook in the event of misuse and to provide evidence for legal purposes.
For more information, see our eBook Help page.