Database and expert systems applications : 32nd international conference, DEXA 2021, virtual event, September 27-30, 2021 : proceedings. Part I

Database and expert systems applications : Part I / 32nd international conference, DEXA 2021, virtual event, September 27-30, 2021 : proceedings. Christine Strauss, Gabriele Kotsis, A Min Tjoa, Ismail Khalil (eds.).

This two-volume set, LNCS 12923 and 12924, constitutes the thoroughly refereed proceedings of the 5th International Conference on Database and Expert Systems Applications, DEXA 2021. Due to COVID-19 pandemic, the conference was held virtually. The 37 full papers presented together with 31 short pape...

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Bibliographic Details
Corporate Author: Database and Expert Systems Applications Conference Online
Other Authors: Strauss, Christine (Editor), Kotsis, Gabriele, 1967- (Editor), Tjoa, A. Min (Editor), Khalīl, Ismāʻīl (Editor)
Format: eBook
Language:English
Published: Cham : Springer, [2021]
Series:Lecture notes in computer science ; 12923.
LNCS sublibrary. Information systems and applications, incl. Internet/Web, and HCI.
Subjects:
Database management > Congresses.
Expert systems (Computer science) > Congresses.
Database management
Expert systems (Computer science)
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • 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.

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