A Work-Pattern Centric Approach to Building a Personal Knowledge Advantage Machine

Abstract

A work pattern, also known as a usage pattern, can be broadly defined as the methods by which a user typically utilizes a particular system. Data mining has been applied to web usage patterns for a variety of purposes. This thesis presents a framework by which data mining techniques could be used to extract patterns from an individual's work flow data in order facilitate a new type of architecture known as a knowledge advantage machine. This knowledge advantage machine is a type of semantic desktop and semantic web application that would assist people in constructing their own personal knowledge networks, as well as sharing that information in an efficient manner with colleagues using the same system. A knowledge advantage machine would be capable of automatically discovering new knowledge which is relevant to the user's personal ontology. Through experimentation, we demonstrate that a user's file usage patterns can be utilized by software in order to automatically and seamlessly learn what is "important" as defined by the user. Further research is necessary to apply this principle to a more realized knowledge advantage machine such that decisions can be fueled by work patterns as well as semantic or contextual information.