Database Systems for Advanced Applications - Info and Reading Options

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This two-volume set LNCS 11446 and LNCS 11447 constitutes the refereed proceedings of the 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, held in Chiang Mai, Thailand, in April 2019. The 92 full papers and 64 short papers were carefully selected from a total of 501 submissions. In addition, 13 demo papers and 6 tutorial papers are included. The full papers are organized in the following topics: big data; clustering and classification; crowdsourcing; data integration; embedding; graphs; knowledge graph; machine learning; privacy and graph; recommendation; social network; spatial; and spatio-temporal. The short papers, demo papers, and tutorial papers can be found in the volume LNCS 11448, which also includes the workshops of DASFAA 2019.

Open Data:

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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