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[null,null,"Titolo",null,"Database and expert systems applications . Part II : 33rd international conference, DEXA 2022, Vienna, Austria, August 22-24, 2022, proceedings / / Christine Strauss [and four others], editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer International Publishing, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-12426-X"]
[null,null,"Descrizione fisica",null,"1 online resource (333 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.13427"]
[null,null,"Disciplina",null,"005.7565"]
[null,null,"Soggetti",null,"Database management"]
[{"$nulls":4},"Expert systems (Computer science)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Abstracts of Keynote Talks -- Responsible AI -- Following the Rules: From Policies to Norms -- Contents - Part II -- Contents - Part I -- Time Series, Streams and Event Data -- Clustering-Based Cross-Sectional Regime Identification for Financial Market Forecasting -- 1 Introduction -- 2 Related Work -- 3 The Proposed Model -- 3.1 Overview of the Proposed Model -- 3.2 Cluster-Based Regime Identification -- 3.3 Regime Modeling and Transition Probability Estimation -- 3.4 Financial Market Forecasting -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Performance Metrics -- 4.3 Experimental Results and Discussion -- 5 Conclusion -- References -- Alps: An Adaptive Load Partitioning Scaling Solution for Stream Processing System on Skewed Stream*-6pt -- 1 Introduction -- 2 Related Work -- 3 Model and Algorithm Design -- 3.1 Algorithm Foundation -- 3.2 Operator Performance Model -- 3.3 Adaptive Partitioning Scaling -- 4 Evaluation -- 4.1 Setup -- 4.2 Adaptive Partitioning Scaling -- 4.3 Alps on Realistic Datasets -- 5 Conclusion -- References -- Latent Relational Point Process: Network Reconstruction from Discrete Event Data -- 1 Introduction -- 2 Related Work -- 3 Framework -- 3.1 General Framework -- 3.2 Evolutionary Framework -- 3.3 Estimation via Expectation Maximisation -- 3.4 Goodness-of-Fit Test -- 4 Empirical Evaluation -- 4.1 Simple Pair Model -- 4.2 Synthetic Data -- 4.3 Revisiting MIT Reality Mining -- 5 Conclusion -- A  Appendix -- A.1  The Lower Bound of the Posterior -- "]
[{"$nulls":4},"A.2  The Surrogate is the Posterior Distribution -- A.3  Simple-Pair Model: Gilbert Graph -- References -- InTrans: Fast Incremental Transformer for Time Series Data Prediction -- 1 Introduction -- 2 Preliminaries -- 2.1 Time-Series Data -- 2.2 Informer -- 3 Incremental Transformer: InTrans -- 3.1 Motivation -- 3.2 Overview."]
[{"$nulls":4},"3.3 Incremental Positional Embedding -- 3.4 Incremental Temporal Embedding -- 3.5 Incremental Value Embedding -- 3.6 Incremental Self-Attention -- 3.7 Prediction Accuracy -- 3.8 Complexity Analysis -- 4 Experimental Evaluation -- 4.1 Dataset and Settings -- 4.2 Training and Testing Time Without GPU -- 4.3 Training and Testing Time Using a GPU -- 5 Related Works -- 6 Conclusion -- A  Proof of Theorem 1 -- B  Proof of Theorem 2 -- C  Proof of Theorem 3 -- D  Proof of Theorem 4 -- E  Proof of Theorem 5 -- F  Proof of Theorem 6 -- G  Proof of Theorem 7 -- References -- A Knowledge-Driven Business Process Analysis Methodology -- 1 Introduction -- 2 Business Process Analysis Canvas -- 3 Applying the BPA Canvas: A Running Example -- 4 Building Class Diagrams and the BPA Ontology -- 5 Related Work and Conclusions -- References -- Sequences and Graphs -- Extending Authorization Capabilities of Object Relational/Graph Mappers by Request Manipulation -- 1 Introduction -- 2 Outline of the Problem -- 2.1 Stakeholder Goals -- 2.2 Problem Context -- 2.3 Artifact -- 3 Our Solution -- 3.1 Interception -- 3.2 Responsibility of the Wrapper -- 3.3 Encoding the Authorization Properties -- 3.4 Summary -- 4 Assessing the Prototype -- 4.1 Applicability of the Solution Concept -- 4.2 Performance -- 5 Related Work -- 5.1 Query Rewriting and Interception -- 5.2 Authorization in Databases -- 5.3 Common Access Control Approaches -- 6 Conclusion and Future Work -- References -- Sequence Recommendation Model with Double-Layer Attention Net -- 1 Introduction -- 2 Related Work -- 2.1 Traditional Sequential Recommendation Models -- 2.2 Deep Neural Network Models -- 2.3 Attention-Based Models -- 3 The Proposed Model: DAttRec -- 3.1 Symbolic Description -- 3.2 STAMP Model -- 3.3 DAttRec Model -- 4 Experiments -- 4.1 Datasets and Data Preparation -- 4.2 Evaluation Metrics."]
[{"$nulls":4},"4.3 Baselines -- 4.4 Parameters -- 4.5 Performance Comparison -- 4.6 Model Analysis and Discussion -- 5 Conclusion -- References -- Fault Detection in Seismic Data Using Graph Attention Network -- 1 Introduction -- 2 Description of Data -- 3 Extraction of Patches -- 4 Representation of Patches in the Graph Domain -- 5 Application of GAT -- 6 Experimental Results -- 7 Application on Field Seismic Data -- 8 Conclusion -- References -- Skeleton-Based Mutual Action Recognition Using Interactive Skeleton Graph and Joint Attention -- 1 Introduction -- 2 Our Model -- 2.1 GCN with Distance Grouping Strategy -- 2.2 Interactive Skeleton Graph -- 2.3 Joint Attention Module -- 3 Experimental Evaluation -- 3.1 Datasets -- 3.2 Results -- 4 Conclusion -- References -- Comparison of Sequence Variants and the Application in Electronic Medical Records -- 1 Introduction -- 2 Related Work -- 2.1 SPM -- 2.2 Analysis of Medical Data by SPM -- 2.3 Analysis of SVs -- 3 Proposed Methods -- 3.1 Sequence -- 3.2 SV -- 3.3 LCS -- 3.4 LCSV -- 3.5 MSV -- 4 Experiments -- 4.1 Experimental Method and Environment -- 4.2 Data Sets -- 4.3 Experimental Results -- 5 Conclusion and Future Work -- References -- Neural Networks -- Reconciliation of Mental Concepts with Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Asymmetric Bidirectional Residual GNN -- 4 Dataset -- 5 Experiments -- 5.1 Methodology -- 5.2 Predictive Performance -- 5.3 General Link Prediction on Citeseer -- 5.4 Ablation Studies -- 6 Conclusion -- References -- I-PNN: An Improved Probabilistic Neural Network for Binary Classification "]
[{"$nulls":4},"of Imbalanced Medical Data -- 1 Introduction -- 2 State-of-the-Arts -- 3 I-PNN: Improved PNN for Imbalanced Classification Tasks in Medicine -- 4 Modeling and Results -- 5 Comparison and Discusion -- 6 Conclusions and Future Work -- References."]
[{"$nulls":4},"PBRE: A Rule Extraction Method from Trained Neural Networks Designed for Smart Home Services -- 1 Introduction -- 2 Context and Related Work -- 3 The Proposed PBRE Method -- 3.1 Generate Instance Rules -- 3.2 Generalize Instance Rules -- 3.3 Combine Rules -- 3.4 Refine Rules -- 4 Evaluation and Comparison with Existing Work -- 4.1 Comparative Experiment -- 5 NRL and Rule Extraction Methods in the Smart Home -- 5.1 Smart Home System in Practice -- 5.2 Smart Home System in Simulation -- 6 Experiment in the Smart Home Context -- 6.1 Simulated Environment -- 6.2 Experiment Results -- 7 Conclusion -- Appendix 1  Datasets Descriptions -- Appendix 2  Metric Acquiring Procedure -- Appendix 3  Extracted Rules for Light Services -- References -- Effective and Robust Boundary-Based Outlier Detection Using Generative Adversarial Networks -- 1 Introduction -- 2 Related Work -- 3 Our Method -- 3.1 Initial Reference Boundary -- 3.2 Boundary-Based GAN -- 4 Experiments -- 4.1 Experiment Design -- 4.2 Experiment Results -- 5 Conclusion -- References -- Efficient Data Processing Techniques -- Accelerated Parallel Hybrid GPU/CPU Hash Table Queries with String Keys -- 1 Introduction -- 2 Related Work -- 3 Basics -- 3.1 Hash Tables -- 3.2 Hardware Acceleration -- 4 Parallel Hybrid GPU/CPU Hash Table for String Keys -- 4.1 Idea -- 4.2 Data Structure and Search -- 5 Evaluation -- 5.1 Benchmark Framework -- 5.2 Benchmark Environment -- 5.3 Benchmark Results -- 6 Summary and Conclusions -- References -- Towards Efficient Discovery of Periodic-Frequent Patterns in Dense Temporal Databases Using Complements -- 1 Introduction -- 2 Related Work -- 3 Periodic-Frequent Pattern Model -- 4 Proposed Algorithm -- 4.1 Basic Idea: Calculating the Periodicity of a Pattern Using Complements -- 4.2 PFPM-C -- 5 Experimental Results -- 5.1 Experimental Setup."]
[{"$nulls":4},"5.2 Generation of Periodic-Frequent Patterns -- 5.3 Runtime Evaluation of the Algorithms -- 5.4 Memory Evaluation of the Algorithms -- 6 Conclusions and Future Work -- References -- An Error-Bounded Space-Efficient Hybrid Learned Index with High Lookup Performance -- 1 Introduction -- 2 Related Work -- 3 Hybrid Learned Index -- 3.1 Overview -- 3.2 Leaf Nodes with Error Bound -- 3.3 Inner Nodes Without Error Bound -- 4 Performance Evaluation -- 4.1 Experimental Settings -- 4.2 Query Performance -- 4.3 Space Cost -- 4.4 Space-Time Trade-Off -- 5 Conclusions and Future Work -- References -- .26em plus .1em minus .1emContinuous Similarity Search for Text Sets -- 1 Introduction -- 2 Problem Statement -- 3 Our Algorithm for CTS -- 4 Usage of Inverted Indices -- 5 Experiments -- References -- Exploiting Embedded Synopsis for Exact and Approximate Query Processing -- 1 Introduction -- 2 Synopsis Embedment and Synopsis-Aware Search -- 3 Experiment -- 4 Related Work and Conclusion -- References -- Advanced Analytics Methodologies and Methods -- Diversity-Oriented Route Planning for Tourists -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Preliminary -- 3.2 Model -- 3.3 Real-Time Statistics of the External Environment -- 4 Experiment and Result -- 4.1 Experiment Setting -- 4.2 Evaluation Metrics -- 4.3 Experimental Result -- 5 Conclusion -- References -- Optimizing the Post-disaster Resource Allocation with Q-Learning: Demonstration of 2021 China Flood -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Model Architecture -- 3.2 Optimization Function -- 3.3 Reward Update (R) and State Update (S) -- 4 Experiment -- 4.1 Data Preparation -- 4.2 Numerical Experiment -- 4.3 Analysis of "]
[{"$nulls":4},"Results -- 5 Conclusion -- References -- ARDBS: Efficient Processing of Provenance Queries Over Annotated Relations -- 1 Introduction."]
[{"$nulls":4},"2 Semantic-Aware Query Processing."]