Making Sense of Data by Capturing and Analyzing Various Data Types from Different Sources for Effective Decision Making

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Data is a valuable resource for knowledge discovery leading to effective decision making. It is collected stored and processed using variety of techniques from simple, traditional and manual to sophisticated, automated and advanced. Indeed, recent development in technology from handheld devices to sensors to surveillance to microarrays to Web 2.0 and beyond allows for electronically capturing and collecting huge volumes of data leading to big data repositories. Big data is distinguished by having large number of instances characterized by high variety and dimensionality. Such data is analogous to huge underground reserves of valuable resources. Among most popular sources of data are social networking platforms and social media portals. These sources could be watched to capture data valuable for a variety of discoveries based on interest and need, e.g., emotion detection, criminal and terror network analysis, epidemic, risk, safety and rescue, opinion, influence and economic trends, etc.Unfortunately, traditional data processing techniques are not capable of making more sense of data and hence will not be capable of producing valuable nuggets. Generally speaking, improved or new techniques could answer research questions related to foundations and applications of making sense of data by maximizing benefit from (small or big) data repositories populated from various sources, e.g., social networking platforms and social media portals. Foundation questions cover unbalanced and dynamic data with variety and high dimensionality, missing values and noise, data semantics like capturing, monitoring and analysis of behavior and trend, etc. Successfully addressing these foundation questions in this proposal, will serve applications questions that could reveal valuable discoveries related to emotions of people and how this could be linked to happiness, sadness, etc., identifying potential criminals and terrorists leading to early warning, handling risk, safety and rescue in case of disaster, watching and identifying change in opinion, influential fellows, discussions related to certain disease, its potential spread, risk, etc.To address these questions and the like, the following interrelated tasks will be completed as components of the proposed research program: data capturing and integration, incomplete/missing value discovery, scalable techniques for effective analysis of dynamic data repositories, as well as trend, influence and behavior capturing and analysis by researching innovative technologies with the aim to build an automated model capable of substituting human observer and scaling well for large and dynamic sources and networks, a scope costly if at all possible for human observers to cover at large scale in a dynamic environment. To tackle these tasks, we will develop, expand and integrate various techniques from data mining, machine learning and network analysis.