Heterogeneous Data Management, Polystores, and Analytics for Healthcare (eBook, PDF)

VLDB Workshops, Poly 2021 and DMAH 2021, Virtual Event, August 20, 2021, Revised Selected Papers
Redaktion: Rezig, El Kindi; Dubovitskaya, Alevtina; Kong, Jun; Luo, Gang; Wang, Fusheng; Kraska, Tim; Stonebraker, Michael; Mattson, Timothy; Gadepally, Vijay

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 183
• Erscheinungstermin: 1. Januar 2022
• Englisch
• ISBN-13: 9783030936631
• Artikelnr.: 63227795

Inhaltsangabe

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.