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In the age of start-ups and technical research, the demand for high-end computing power and loads of space is ever increasing. Machine learning techniques have become an inseparable part of the big data analytics. Setting up one’s own infrastructure to deal with all this vastness is usually not feasible due to high expenses and lack of desired expertise. As a solution to this problem, this paper proposes a system for Big-Data Analytics and Machine Learning based on Hadoop and Spark frameworks that also supports Operating System (OS) Rental Services. Machine Learning (ML) services provide option to use both existing inbuilt popular models or create one’s own model. OS Rental services provide users with high end infrastructure on their low-end devices on rent. The entire implementation has been made open source for ease of access and facilitating extensibility.

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This paper discusses an ontology based clinical decision support system for the specialty of Geriatric Medicine. We created a domain level ontology based on Handbook of Geriatrics and then mapped it to an upper level Basic Formal Ontology. The decision support system has been developed in Prolog. For accessing this ontology, we created an interactive web and android application which acts as a front-end to the system. Both applications are able to display the ontology structure and to predict the disease based on the symptom entered. We have uploaded our ontology to Bio-Portal, which is one of the most comprehensive biomedical ontology repository. It can be accessed using the URL https://bioportal.bioontology.org/ontologies/G-O.

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