Heterogeneous Data Management, Polystores, and Analytics for Healthcare
Description
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.
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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.
