Heterogeneous Data Management, Polystores, and Analytics for Healthcare

This book constitutes revised selected papers from two VLDB workshops: The International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2021, and the 7 th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2021, which were held virtually on August 2021. For Poly 2021, 7 full and 2 short papers were accepted from 10 submissions; and for DMAH 2021, 4 full papers together with 2 invited papers were accepted from a total of 7 submissions. The papers were organized in topical sections as follows: distributed information systems in enterprises, enterprise access to data constructed from a variety of programming models, data management, data integration, data curation, privacy, and security innovative data management and analytics technologies highlighting end-to-end applications, systems, and methods to address problems in healthcare.

Inhalt
Privacy, Security and/or Policy Issues for Heterogenous Data.- Data Virtual Machines: Enabling Data Virtualization.- A Formal Category Theoretical Framework for Multi-Model Data Transformations.- Towards Generic Fine-Grained Transaction Isolation in Polystores.- Data Governance in a Database Operating System (DBOS).- ACID-V: Towards a new class of DBMSs for Data Sharing.- Polystore Systems and DBMSs: Love Marriage or Marriage of Convenience?.- Pods: Privacy Compliant Scalable Decentralized Data Services.- Friends Don't Let Friends Deploy Black-Box Models: The Importance of Intelligibility and Explanation for Machine Learning in Healthcare.- Privacy-preserving Distributed Support Vector Machines.- Benchmarking Multi-Instance Learning for Multivariate Time Series Analysis.- A Cloud-Native NGS Data Processing and Annotation Platform.- Administrative Health Data Representation for Mortality and High Utilization Prediction.- Generating Longitudinal Synthetic EHR Data with Recurrent Autoencoders and Generative Adversarial Networks.