Optimized Management of BIG Data Produced in Brain Disorder Rehabilitation

Abstract

Brain disorders resulting from injury, disease, or health conditions can influence function of most parts of human body. Necessary medical care and rehabilitation is often impossible without close cooperation of several diverse medical specialists who must work jointly to choose methods that improve and support healing processes as well as to discover underlying principles. The key to their decisions are data resulting from careful observation or examination of the patient. We introduce the concept of scientific dataspace that involves and stores numerous and often complex types of data, e.g., the primary data captured from the application, data derived by curation and analytic processes, background data including ontology and workflow specifications, semantic relationships between dataspace items based on ontologies, and available published data. Our contribution applies big data and cloud technologies to ensure efficient exploitation of this dataspace, namely, novel software architectures, algorithms and methodology for its optimized management and utilization. We present its service-oriented architecture using a running case study and results of its data processing that involves mining and visualization of selected patterns optimized towards big and complex data we are dealing with.