A Smart Big Data Analytics and Knowledge Management Framework

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According to an IBM analysis, we create 2.5 quintillion bytes of data every day. We face an enormous challenge in analyzing this large volume of many different types of data being generated at a high speed, and managing the knowledge extracted from data analytics. Currently we need experts for recognizing the data, and thereby, selecting the right analytic tools, workflows, and storage systems. Based on cognitive theories, humans create and link profiles of data after birth, which builds layers of knowledge and enables them to learn and query the knowledge for decision making. We need a data analytic framework that can recognize the data like a human expert, create data profiles by using the right analytic tools and workflows, and store and link this knowledge to be able to retrieve it efficiently for goal based decision making. For example, when deciding on a patient's treatment course, a physician is required to consult the patient's medical records containing structured data such as height and weight, unstructured text notes, relevant MRI-image, and refer to a disease database to investigate diseases that have similar symptoms. *** We propose creating a smart big data analytics and knowledge management framework that will apply machine learning models and a variety of data analytics tools to recognize the data type from structure and semantic content, and thereby, apply the right analytic workflow to extract knowledge to create and link data profiles. The knowledge management part of the framework will store the extracted knowledge into distributed and hybrid knowledge repositories and create a multi-level graph indexing structure to efficiently retrieve the right knowledge pieces for decision support based on goal, knowledge association, context and situation. Due to the necessity of ubiquitous access and elastic resources for big data, we will implement the framework on cloud resources and make it accessible as a data analytic and knowledge service on the cloud. The prototype of the framework will be validated for three interdisciplinary use case scenarios: 1) predicting market status of the clients of our industry partner using news and financial data analytics; 2) defining and diagnosing PTSD (Post Traumatic Stress Disorder) in primary care electronic medical record (EMR) data, and 3) analyzing heart rate variability from streaming monitoring data from the intensive care unit at the Kingston General Hospital. *** The proposed research program will train 3 PhD, 3 MSc and 2 undergraduate students in using cutting edge big data analytics systems and developing data science skills that are highly sought after in the global market today. As evident from the use cases, the research will directly impact the technology industry and health care, build the backbone for the future intelligent decision support systems, and sustain Canada's leadership in building a smart digital world.