Heterogeneous Data Management, Polystores, and Analytics for Healthcare
Springer International Publishing (Verlag)
978-3-030-71054-5 (ISBN)
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.
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.
| Erscheinungsdatum | 08.04.2021 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science | Security and Cryptology |
| Zusatzinfo | XIII, 233 p. 84 illus., 71 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 385 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Applications • biomedical informatics • Computer Networks • Computer Science • Computer systems • computer vision • conference proceedings • Covid-19 pandemic • Databases • Data Mining • Deep learning • gdpr • Image Analysis • Image Processing • Informatics • machine learning • Network Protocols • Neural networks • polystores • privacy • Research • security • Signal Processing |
| ISBN-10 | 3-030-71054-8 / 3030710548 |
| ISBN-13 | 978-3-030-71054-5 / 9783030710545 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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