Database Systems for Advanced Applications: 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22-25, 2019, Proceedings, Part II - Info and Reading Options

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Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Machine Learning -- An Approach Based on Bayesian Networks for Query Selectivity Estimation -- 1 Introduction -- 2 Related Work -- 2.1 Distribution Estimation -- 2.2 Sampling -- 2.3 Learning -- 2.4 Discussion -- 3 Methodology -- 3.1 Finding a Good Network -- 3.2 Estimating the Conditional Probabilities -- 3.3 Producing Selectivity Estimates -- 4 Experimental Study -- 4.1 Setup -- 4.2 Construction Time -- 4.3 Cardinality Estimates -- 4.4 Inference Time -- 4.5 Disk Usage -- 5 Conclusion -- References -- An Exploration of Cross-Modal Retrieval for Unseen Concepts -- 1 Introduction -- 2 Related Work -- 2.1 Cross-Modal Hashing -- 2.2 Zero-Shot Learning -- 3 Approach -- 3.1 Problem Definition -- 3.2 Cross-Modal Attribute Hashing Formulation -- 3.3 Optimization -- 4 Experiments -- 4.1 Datasets -- 4.2 Settings -- 4.3 Baselines -- 4.4 Experimental Results -- 4.5 Convergence Analysis -- 5 Conclusion -- References -- Continuous Patient-Centric Sequence Generation via Sequentially Coupled Adversarial Learning -- 1 Introduction -- 2 Related Work -- 2.1 Sequentially Generative Adversarial Networks -- 2.2 Medical Data Generation -- 3 Preliminaries -- 3.1 Data Description and Notations -- 3.2 Problem Definition -- 3.3 Basics of GANs -- 4 Methodology -- 4.1 Overview of SC-GAN -- 4.2 Coupled Generators -- 4.3 Discriminator -- 5 Experiments -- 5.1 Dataset Description and Preprocessing -- 5.2 Models for Comparison -- 5.3 Quantitative Evaluation for Synthetic Data -- 5.4 Qualitative Evaluation for Synthetic Data -- 6 Conclusion -- References -- DMMAM: Deep Multi-source Multi-task Attention Model for Intensive Care Unit Diagnosis -- 1 Introduction -- 2 Related Work -- 3 Proposed Framework -- 3.1 Problem Statement -- 3.2 Multi-modal Multi-task Temporal Learning Framework for Temporal Data

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