With the proliferation of sensor data, a critical challenge is to interpret and extract knowledge from large-scale heterogeneous observational data. Most knowledge discovery frameworks relay on data mining techniques to extract interesting patterns. The problem of finding such patterns is NP-complete and the property of interestingness is not monotone since a pattern may be interesting, even if its subpatterns are not. In this dissertation a framework for interactive knowledge discovery from heterogeneous high-dimensional temporal data is presented. First, a high-level pattern formulation language is introduced. The language consists of an event model for fusing and abstracting data streams, a semi-interval time model for effectively representing temporal relations, and a set of expressive operators. Based on these operators, a visual and interactive framework is proposed which combines data-driven (bottom-up) and hypothesis-driven (top-down) analyses.

This framework takes advantage of data-driven operators for pattern mining and investi- gating unknown unknowns to generate a basic model and derive a preliminary knowledge. It also uses domain expert knowledge to guide the process of revealing known unknowns. An expert can seed a hypothesis, based on prior knowledge or the knowledge derived from data-driven analysis, and grow it interactively using hypothesis-driven operators. In the con- text of the pattern mining component, novel time efficient algorithms are introduced which allow discovery of hidden event co-occurrences from multiple event streams. A prototype of the framework is implemented as a web based system which can be utilized as an effective tool for explanation and decision making in almost all disciplines. The applicability of this framework is evaluated in a healthcare application for asthma risk management and a human behavior understanding application, called Objective Self. These applications and experiments highlight the actionable knowledge that the framework can help uncover.