Advancing neuro-oncology of glial tumors from big data and multidisciplinary studies

A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata

Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results.

Distributed messaging and light streaming system for combating pandemics: A case study on spatial analysis of COVID-19 Geo-tagged Twitter dataset

Real-time data processing and distributed messaging are problems that have been worked on for a long time. As the amount of spatial data being produced has increased, coupled with increasingly complex software solutions being developed, there is a need for platforms that address these needs. In this paper, we present a distributed and light streaming system for combating pandemics and give a case study on spatial analysis of the COVID-19 geo-tagged Twitter dataset. In this system, three of the major components are the translation of tweets matching with user-defined bounding boxes, name entity recognition in tweets, and skyline queries. Apache Pulsar addresses all these components in this paper. With the proposed system, end-users have the capability of getting COVID-19 related information within foreign regions, filtering/searching location, organization, person, and miscellaneous based tweets, and performing skyline based queries. The evaluation of the proposed system is done based on certain characteristics and performance metrics. The study differs greatly from other studies in terms of using distributed computing and big data technologies on spatial data to combat COVID-19. It is concluded that Pulsar is designed to handle large amounts of long-term on disk persistence.