Heterogeneous Data Management, Polystores, and Analytics for Healthcare : VLDB Workshops, Poly 2022 and DMAH 2022, Virtual Event, September 9, 2022, Revised Selected Papers / edited by El Kindi Rezig, Vijay Gadepally, Timothy Mattson, Michael Stonebraker, Tim Kraska, Jun Kong, Gang Luo, Dejun Teng, Fusheng Wang.

Format:

Book

Contributor:

Rezig, El Kindi, editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 13814

Language:

English

Subjects (All):

Application software.

Computer and Information Systems Applications.

Local Subjects:

Computer and Information Systems Applications.

Physical Description:

1 online resource (103 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2022.

Summary:

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 2022, and the 8th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2022, which were held virtually on September 9, 2022. The proceedings include 3 full papers each from Poly 2022 and from DMAH 2022. DMAH deals with innovative data management and analytics technologies highlighting end-to-end applications, systems, and methods to address problems in healthcare, public health, and everyday wellness, with clinical, physiological, imaging, behavioral, environmental, and omic - data, and data from social media and the Web. Poly is focusing on the broader real-world polystore problem, which includes data management, data integration, data curation, privacy, and security.

Contents:

POLY 2022

Privacy, Security and/or Policy Issues for Heterogeneous Data

Ad-hoc Searches on Image Databases

A Survey of Data Challenges across a Modernizing Bureaucracy: A New Perspective on Examining Old Government Problems

Purpose Scan: A purpose-aware access method

DMAH 2022

Enabling Real-world Medicine with Data Lake Federation: a research perspective

Towards Assessing Data Bias in Clinical Trials

Clinical synthetic data generation to predict and identify risk factors for cardiovascular diseases. .

Notes:

Includes index.

Other Format:

Print version: Rezig, El Kindi Heterogeneous Data Management, Polystores, and Analytics for Healthcare

ISBN:

3-031-23905-9