Big Data Analytics and Knowledge Discovery [[electronic resource] ] : 25th International Conference, DaWaK 2023, Penang, Malaysia, August 28–30, 2023, Proceedings / / edited by Robert Wrembel, Johann Gamper, Gabriele Kotsis, A Min Tjoa, Ismail Khalil

• Autore: Wrembel Robert
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
• Edizione: 1st ed. 2023.
• Descrizione fisica: 1 online resource (407 pages)
• Disciplina: 001.422; 005.7
• Soggetto topico: Quantitative research; Data mining; Application software; Artificial intelligence; Data Analysis and Big Data; Data Mining and Knowledge Discovery; Computer and Information Systems Applications; Artificial Intelligence
• Altri autori: GamperJohann KotsisGabriele TjoaA. Min KhalilIsmail
• Nota di contenuto: 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 -- 4 Proposed Method -- 4.1 Random Walk -- 5 Experiment -- 5.1 Transition Probabilities in Steady State -- 5.2 Node Label Estimation -- 5.3 Parameter Dependence of F1 Score -- 6 Conclusion -- References -- Contextual Shift Method (CSM) -- 1 Introduction -- 2 Contextual Shifts -- 3 Contextual Shift Method -- 4 Experiments -- 5 Conclusion -- References -- Utility-Oriented Gradual Itemsets Mining Using High Utility Itemsets Mining -- 1 Introduction -- 2 Preliminary Definitions -- 3 High Utility Gradual Itemsets Mining -- 3.1 Database Encoding -- 3.2 High Utility Gradual Itemsets Extraction -- 4 Experimental Study -- 5 Conclusion -- References -- Discovery of Contrast Itemset with Statistical Background Between Two Continuous Variables -- 1 Introduction -- 2 Contrast ItemSB -- 3 Experimental Results -- 4 Conclusions -- References. ; DBGAN: A Data Balancing Generative Adversarial Network for Mobility Pattern Recognition -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Reproducing Kernel Hilbert Space Embeddings -- 3.2 Attention Mechanism -- 3.3 Generative Adversarial Network -- 4 DBGAN Mobility Pattern Classification Model -- 4.1 Attributes of Travel Trajectories Utilized for Classification -- 4.2 Sequences to Images with Kernel Embedding -- 4.3 Classification Using Self Attention-Based Generative Adversarial Network -- 5 Evaluation -- 6 Conclusion -- References -- Bitwise Vertical Mining of Minimal Rare Patterns -- 1 Introduction -- 2 Background and Related Works -- 3 Our RP-VIPER Algorithm -- 4 Evaluation -- 5 Conclusions -- References -- Inter-item Time Intervals in Sequential Patterns -- 1 Introduction -- 2 Related Work -- 3 Representing Time in Sequences -- 3.1 Preliminaries -- 3.2 Integrating Intervals in Sequences -- 4 Experiments -- 4.1 Datasets and Models -- 4.2 Results -- 5 Conclusion -- References -- Fair-DSP: Fair Dynamic Survival Prediction on Longitudinal Electronic Health Record -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Fair Dynamic Survival Model -- 3.2 Individual Fairness -- 3.3 Group Fairness -- 4 Experiments -- 4.1 Quantitative Analysis -- 4.2 Sensitivity Study -- 5 Conclusions -- References -- Machine Learning -- DAT@Z21: A Comprehensive Multimodal Dataset for Rumor Classification in Microblogs -- 1 Introduction -- 2 Related Works -- 2.1 Fake Health News Datasets -- 2.2 Fake News Datasets -- 3 Data Collection -- 3.1 News Articles and Ground Truth Collection -- 3.2 Preparing the Tweets Collection -- 3.3 Tweets Collection -- 4 Rumor Classification Using DAT@Z21 -- 4.1 Baselines -- 4.2 Experiment Settings -- 4.3 Experimental Results -- 5 Conclusion and Perspectives -- References. ; Dealing with Data Bias in Classification: Can Generated Data Ensure Representation and Fairness? -- 1 Introduction -- 2 Related Work -- 3 Measuring Discrimination -- 4 Problem Formulation -- 5 Methodology -- 6 Evaluation -- 6.1 Comparing Pre-processors -- 6.2 Investigating the Fairness-Agnostic Property -- 7 Conclusion -- 8 Discussion and Future Work -- A Proof of Time Complexity -- References -- Random Hypergraph Model Preserving Two-Mode Clustering Coefficient -- 1 Introduction -- 2 Preliminaries -- 3 Extending the Hyper dK-Series to the Case of dv = 2.5+ -- 4 Experiments -- 5 Conclusion -- References -- A Non-overlapping Community Detection Approach Based on -Structural Similarity -- 1 Introduction -- 2 Preliminaries -- 3 A Hierarchical Clustering Approach Based on -Structural Similarity -- 4 Experiments -- 5 Conclusion and Future Work -- A Appendix a -- B Appendix B -- References -- Improving Stochastic Gradient Descent Initializing with Data Summarization -- 1 Introduction -- 2 Definitions -- 2.1 Input Data Set -- 2.2 LR Model -- 3 System and Algorithms -- 3.1 Gamma Summarization () -- 3.2 Mini-batch SGD -- 3.3 Mini-batch SGD Initialization Using Gamma -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Related Work -- 6 Conclusions -- References -- Feature Analysis of Regional Behavioral Facilitation Information Based on Source Location and Target People in Disaster -- 1 Introduction -- 2 Related Work -- 3 Basic Concept of RBF Tweet Classification -- 3.1 Extraction of BF Tweets -- 3.2 RBF Tweet Extraction and Classification -- 4 Analysis of RBF Tweets -- 4.1 Training and Test Data -- 4.2 Research Question -- 4.3 Results and Discussion of Research Questions -- 5 Conclusion -- References -- Exploring Dialog Act Recognition in Open Domain Conversational Agents -- 1 Introduction -- 2 Related Works. ; 3 Proposed Dialog Act Taxonomy -- 3.1 Data Sources -- 4 Proposed Dialog Act Classifier -- 4.1 Experimental Setup -- 4.2 Performance Evaluation -- 4.3 Generalizability of Model -- 5 Conclusion -- References -- UniCausal: Unified Benchmark and Repository for Causal Text Mining -- 1 Introduction -- 2 Related Work -- 2.1 Tasks -- 2.2 Datasets -- 2.3 Other Large Causal Resources -- 3 Methodology -- 3.1 Creation of UniCausal -- 3.2 Baseline Model -- 4 Experiments -- 4.1 Baseline Performance -- 4.2 Impact of Datasets -- 4.3 Adding CauseNet to Investigate the Importance of Linguistic Variation in Examples -- 5 Conclusion -- References -- Deep Learning -- Accounting for Imputation Uncertainty During Neural Network Training -- 1 Introduction -- 2 Related Works -- 3 Contributions -- 3.1 Single-Hotpatching -- 3.2 Multiple-Hotpatching -- 4 Experiments -- 4.1 Experimental Protocol -- 4.2 Results -- 5 Discussion and Conclusion -- References -- Supervised Hybrid Model for Rumor Classification: A Comparative Study of Machine and Deep Learning Approaches -- 1 Introduction -- 2 Related Work -- 3 Datasets and Preprocessing -- 4 Implementation -- 4.1 Traditional ML Approaches -- 4.2 DL Approaches -- 4.3 The Ensemble Stack ML Model -- 4.4 The Hybrid ML-DL Model -- 5 Results and Analysis -- 6 Conclusion and Future Work -- References -- Attention-Based Counterfactual Explanation for Multivariate Time Series -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Notation -- 3.2 Proposed Method -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Methods -- 4.3 Experimental Result -- 5 Conclusion -- References -- DRUM: A Real Time Detector for Regime Shifts in Data Streams via an Unsupervised, Multivariate Framework -- 1 Introduction -- 2 Related Work -- 3 DRUM -- 4 Evaluation -- 5 Conclusion -- References. ; Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching.
• Sommario/riassunto: This book constitutes the proceedings of the 25th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2023, which took place in Penang, Malaysia, during August 29-30, 2023. The 18 full papers presented together with 19 short papers were carefully reviewed and selected from a total of 83 submissions. They were organized in topical sections as follows: Data quality; advanced analytics and pattern discovery; machine learning; deep learning; and data management.
• Titolo autorizzato: Big Data Analytics and Knowledge Discovery
• ISBN: 3-031-39831-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910741143403321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 14148