IoT-PMA: Patient Health Monitoring in Medical IoT Ecosystems

The emergence of the Internet of Things (IoT) and the increasing number of cheap medical devices enable geographically distributed healthcare ecosystems of various stakeholders. Such ecosystems contain different application scenarios, e.g., (mobile) patient monitoring using various vital parameters such as heart rate signals. The increasing number of data producers and the transfer of data between medical stakeholders introduce several challenges to the data processing environment, e.g., heterogeneity and distribution of computing and data, lowlatency processing, as well as data security and privacy. Current approaches propose cloud-based solutions introducing latency bottlenecks and high risks for companies dealing with sensitive patient data. In this paper, we address the challenges of medical IoT applications by proposing an end-to-end patient monitoring application that includes NebulaStream as the data processing system, an easy-to-use UI that provides ad-hoc views on the available vital parameters, and the integration of ML models to enable predictions on the patients' health state. Using our end-to-end solution, we implement a real-world patient monitoring scenario for hemodynamic and pulmonary decompensations, which are dynamic and life-threatening deteriorations of lung and cardiovascular functions. Our application provides ad-hoc views of the vital parameters and derived decompensation severity scores with continuous updates on the latest data readings to support timely decision-making by physicians. Furthermore, we envision the infrastructure of an IoT ecosystem for a multi-hospital scenario that enables geo-distributed medical participants to contribute data to the application in a secure, private, and timely manner

NebulaStream: Complex Analytics Beyond the Cloud

The arising Internet of Things (IoT) will require significant changes to current stream processing engines (SPEs) to enable large-scale IoT applications. In this paper, we present challenges and opportunities for an IoT data management system to enable complex analytics beyond the cloud. As one of the most important upcoming IoT applications, we focus on the vision of a smart city. The goal of this paper is to bridge the gap between the requirements of upcoming IoT applications and the supported features of an IoT data management system. To this end, we outline how state-of-the-art SPEs have to change to exploit the new capabilities of the IoT and showcase how we tackle IoT challenges in our own system, NebulaStream. This paper lays the foundation for a new type of systems that leverages the IoT to enable large-scale applications over millions of IoT devices in highly dynamic and geo-distributed environments

Particulate Matter Matters

For the second time, the Data Science Challenge took place as part of the 18th symposium “Database Systems for Business, Technology and Web” (BTW) of the Gesellschaft für Informatik (GI). The Challenge was organized by the University of Rostock and sponsored by IBM and SAP. This year, the integration, analysis and visualization around the topic of particulate matter pollution was the focus of the challenge. After a preselection round, the accepted participants had one month to adapt their developed approach to a substantiated problem, the real challenge. The final presentation took place at BTW 2019 in front of the prize jury and the attending audience. In this article, we give a brief overview of the schedule and the organization of the Data Science Challenge. In addition, the problem to be solved and its solution will be presented by the participants

Particulate Matter Matters---The Data Science Challenge @ BTW 2019

For the second time, the Data Science Challenge took place as part of the 18th symposium ``Database Systems for Business, Technology and Web'' (BTW) of the Gesellschaft für Informatik (GI). The Challenge was organized by the University of Rostock and sponsored by IBM and SAP. This year, the integration, analysis and visualization around the topic of particulate matter pollution was the focus of the challenge. After a preselection round, the accepted participants had one month to adapt their developed approach to a substantiated problem, the real challenge. The final presentation took place at BTW 2019 in front of the prize jury and the attending audience. In this article, we give a brief overview of the schedule and the organization of the Data Science Challenge. In addition, the problem to be solved and its solution will be presented by the participants