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Author:   Vijay Gadepally ,  Timothy Mattson ,  Michael Stonebraker ,  Tim Kraska
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2021
Volume:   12633
Weight:   0.454kg
ISBN:  

9783030710545

ISBN 10:   3030710548
Pages:   233
Publication Date:   04 March 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

Poly 2020: Privacy, Security and/or Policy Issues for Heterogenous Data.- A Polystore Based Database Operating System (DBOS).- Polypheny-DB: Towards Bridging the Gap Between Polystores and HTAP Systems.- Persona Model Transfer for User Activity Prediction across Heterogeneous Domains.- PolyMigrate: Dynamic Schema Evolution and Data Migration in a Distributed Polystore.- An Architecture for the Development of Distributed Analytics based on Polystore Events.- Towards Data Discovery by Example.- The Transformers for Polystores - the next frontier for Polystore research.- DMAH 2020: COVID-19 Data Analytics and Visualization.- Open-world COVID-19 Data Visualization.- DMAH 2020: Deep Learning based Biomedical Data Analytics.- Privacy-Preserving Knowledge Transfer with Bootstrap Aggregation of Teacher Ensembles.- An Intelligent and Efficient Rehabilitation Status Evaluation Method: A Case Study on Stroke Patients.- Multiple Interpretations Improve Deep Learning Transparency for Prostate Lesion Detection.- DMAH 2020: NLP based Learning from Unstructured Data.- Tracing State-Level Obesity Prevalence from Sentence Embeddings of Tweets: A Feasibility Study.- Enhancing Medical Word Sense Inventories Using Word Sense Induction: A Preliminary Study.- DMAH 2020: Biomedical Data Modelling and Prediction.- Teaching analytics medical-data common sense.- CDRGen: A Clinical Data Registry Generator.- Prediction of lncRNA-disease associations from tripartite graphs.- DMAH 2020: Invited Paper.- Parameter Sensitivity Analysis for the Progressive Sampling-Based Bayesian Optimization Method for Automated Machine Learning Model Selection.  

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