Big data analytics and knowledge discovery : 25th International Conference, DaWaK 2023, Penang, Malaysia, August 28-30, 2023, Proceedings

Big data analytics and knowledge discovery : 25th International Conference, DaWaK 2023, Penang, Malaysia, August 28-30, 2023, Proceedings / Robert Wrembel, Johann Gamper, Gabriele Kotsis, A Min Tjoa, Ismail Khalil, editors.

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Korporativní autor: DaWaK (Conference) Penang, Malaysia)
Další autoři: Wrembel, Robert (Editor), Gamper, Johann (Editor), Kotsis, Gabriele, 1967- (Editor), Tjoa, A Min (Editor), Khalil, Ismail, 1960- (Editor)
Médium: E-kniha
Jazyk:English
Vydáno: Cham, Switzerland : Springer, 2023.
Edice:Lecture notes in computer science ; 14148.
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Big data > Congresses.
Data mining > Congresses.
Big data.
Data mining.
proceedings (reports)
Conference papers and proceedings.
Actes de congrès.
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Obsah:
  • Intro
  • Preface
  • Organization
  • From an Interpretable Predictive Model to a Model Agnostic Explanation (Abstract of Keynote Talk)
  • Contents
  • Data Quality
  • Using Ontologies as Context for Data Warehouse Quality Assessment
  • 1 Introduction
  • 2 Related Work
  • 3 Preliminaries
  • 3.1 Running Example
  • 3.2 Data Warehouse Formal Specification
  • 3.3 Context Formal Specification
  • 4 Data Warehouse to Ontology Mapping
  • 5 Context-Based Data Quality Rules
  • 6 Experimentation
  • 6.1 Implementation
  • 6.2 Validation
  • 7 Conclusions and Future Work
  • References
  • Preventing Technical Errors in Data Lake Analyses with Type Theory
  • 1 Introduction
  • 2 Related Works
  • 3 Type-Theoretical Framework
  • 4 Conclusion
  • References
  • EXOS: Explaining Outliers in Data Streams
  • 1 Introduction
  • 2 Related Work
  • 3 Preliminaries
  • 4 The Proposed Algorithm: EXOS
  • 4.1 Estimator
  • 4.2 Temporal Neighbor Clustering
  • 4.3 Outlying Attribute Generators
  • 5 Evaluation
  • 5.1 Experimental Setup
  • 5.2 Results and Analysis
  • 6 Conclusions
  • References
  • Motif Alignment for Time Series Data Augmentation
  • 1 Introduction
  • 2 Preliminaries
  • 2.1 Matrix Profile
  • 2.2 Pan-Matrix Profile
  • 2.3 DTW Alignment for Time Series Data Augmentation
  • 3 Proposed Method
  • 3.1 Motif Mapping
  • 3.2 Time Series Augmentation
  • 4 Experimental Evaluation
  • 4.1 Setup
  • 4.2 Aligning Time Series Using MotifDTW
  • 4.3 Performance Gain
  • 5 Conclusion
  • References
  • State-Transition-Aware Anomaly Detection Under Concept Drifts
  • 1 Introduction
  • 2 Related Works
  • 3 Problem Definition
  • 3.1 Terminology
  • 3.2 Problem Statement
  • 4 State-Transition-Aware Anomaly Detection
  • 4.1 Reconstruction and Latent Representation Learning
  • 4.2 Drift Detection in the Latent Space
  • 4.3 State Transition Model
  • 5 Experiment
  • 5.1 Experiment Setup
  • 5.2 Performance
  • 6 Conclusion
  • References
  • Anomaly Detection in Financial Transactions Via Graph-Based Feature Aggregations
  • 1 Introduction
  • 2 Related Work
  • 2.1 Graph Embedding
  • 2.2 Anomaly Detection
  • 3 Problem Formalization
  • 4 Proposed Method
  • 4.1 PFA: Proximal Feature Aggregation
  • 4.2 AFA: Anomaly Feature Aggregation
  • 5 Experiment
  • 5.1 Experimental Setup
  • 5.2 Effectiveness Evaluation
  • 5.3 Scalability Evaluation
  • 6 Conclusion
  • References
  • The Synergies of Context and Data Aging in Recommendations
  • 1 Introduction
  • 2 ALBA: Adding Aging to LookBack Apriori
  • 3 Context Modeling
  • 4 Evaluation
  • 4.1 Contexts
  • 4.2 Methodology
  • 4.3 Fitbit Validation
  • 4.4 Auditel Validation
  • 5 Conclusions and Future Work
  • References
  • Advanced Analytics and Pattern Discovery
  • Hypergraph Embedding Based on Random Walk with Adjusted Transition Probabilities
  • 1 Introduction
  • 2 Related Work
  • 3 Preliminaries
  • 3.1 Notation
  • 3.2 Hypergraph Projection
  • 3.3 Random Walk and Stationary Distribution
  • 3.4 Skip-Gram

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