Skip to main content
Open Access Publications from the University of California

UC Irvine

UC Irvine Electronic Theses and Dissertations bannerUC Irvine

Interactive Event-driven Knowledge Discovery from Data Streams

Creative Commons 'BY' version 4.0 license

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.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View