Heterogeneous Data Management, Polystores, and Analytics for Healthcare VLDB Workshops, Poly 2020 and DMAH 2020, Virtual Event, August 31 and September 4, 2020, Revised Selected Papers /
Heterogeneous Data Management, Polystores, and Analytics for Healthcare VLDB Workshops, Poly 2020 and DMAH 2020, Virtual Event, August 31 and September 4, 2020, Revised Selected Papers / [electronic resource] :
edited by Vijay Gadepally, Timothy Mattson, Michael Stonebraker, Tim Kraska, Fusheng Wang, Gang Luo, Jun Kong, Alevtina Dubovitskaya.
- 1st ed. 2021.
- XIII, 233 p. 84 illus., 71 illus. in color. online resource.
- Security and Cryptology, 12633 2946-1863 ; .
- Security and Cryptology, 12633 .
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. .
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 2020, and the 6th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2020, which were held virtually on August 31 and September 4, 2020. For Poly 2020, 4 full and 3 short papers were accepted from 10 submissions; and for DMAH 2020, 7 full and 2 short papers were accepted from a total of 15 submissions. The papers were organized in topical sections as follows: Privacy, Security and/or Policy Issues for Heterogenous Data; COVID-19 Data Analytics and Visualization; Deep Learning based Biomedical Data Analytics; NLP based Learning from Unstructured Data; Biomedical Data Modelling and Prediction.
9783030710552
10.1007/978-3-030-71055-2 doi
Application software.
Database management.
Machine learning.
Information technology--Management.
Social sciences--Data processing.
Computer and Information Systems Applications.
Database Management.
Machine Learning.
Computer Application in Administrative Data Processing.
Computer Application in Social and Behavioral Sciences.
QA76.76.A65
005.3
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. .
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 2020, and the 6th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2020, which were held virtually on August 31 and September 4, 2020. For Poly 2020, 4 full and 3 short papers were accepted from 10 submissions; and for DMAH 2020, 7 full and 2 short papers were accepted from a total of 15 submissions. The papers were organized in topical sections as follows: Privacy, Security and/or Policy Issues for Heterogenous Data; COVID-19 Data Analytics and Visualization; Deep Learning based Biomedical Data Analytics; NLP based Learning from Unstructured Data; Biomedical Data Modelling and Prediction.
9783030710552
10.1007/978-3-030-71055-2 doi
Application software.
Database management.
Machine learning.
Information technology--Management.
Social sciences--Data processing.
Computer and Information Systems Applications.
Database Management.
Machine Learning.
Computer Application in Administrative Data Processing.
Computer Application in Social and Behavioral Sciences.
QA76.76.A65
005.3