Improving intelligent analytics through guidance: analysis and refinement of patterns of use and recommendation methods for data mining and analytics systems

In conjunction with the proliferation of data collection applications, systems that provide functionality to analyze and mine this resource also increase in count and complexity. As a part of this growth, understanding how users navigate these systems, and how that navigation influences the resulting extracted information and subsequent decisions becomes a critical component of their design. A central theme of improving the understanding of user behavior and tools for their support within these systems focuses the effort to gain a context-aware view of analytics system optimization. Through distinct, but interwoven, articles this research examines the specific characteristics of usage patterns of a specific example of these types of systems, construction of and educational support system for new and existing users, and a decision-tree supported workflow optimization recommender system. These components combine to yield a method for guided intelligent analytics that uses behavior, system knowledge, and workflow optimization to improve the user experience and promote efficiency of use for systems of this type.