OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs

Providing appropriate structures around human resources can streamline operations and thus facilitate the competitiveness of an organization. To achieve this goal, modern organizations need to acquire an accurate and timely understanding of human resource grouping while faced with an ever-changing environment. The use of process mining offers a promising way to help address the need through utilizing event log data stored in information systems. By extracting knowledge about the actual behavior of resources participating in business processes from event logs, organizational models can be constructed, which facilitate the analysis of the de facto grouping of human resources relevant to process execution. Nevertheless, open research gaps remain to be addressed when applying the state-of-the-art process mining to analyze resource grouping. For one, the discovery of organizational models has only limited connections with the context of process execution. For another, a rigorous solution that evaluates organizational models against event log data is yet to be proposed. In this paper, we aim to tackle these research challenges by developing a novel framework built upon a richer definition of organizational models coupling resource grouping with process execution knowledge. By introducing notions of conformance checking for organizational models, the framework allows effective evaluation of organizational models, and therefore provides a foundation for analyzing and improving resource grouping based on event logs. We demonstrate the feasibility of this framework by proposing an approach underpinned by the framework for organizational model discovery, and also conduct experiments on real-life event logs to discover and evaluate organizational models.

Applying Maxi-adjustment to Adaptive Information Filtering Agents

Learning and adaptation is a fundamental property of intelligent agents. In the context of adaptive information filtering, a filtering agent's beliefs about a user's information needs have to be revised regularly with reference to the user's most current information preferences. This learning and adaptation process is essential for maintaining the agent's filtering performance. The AGM belief revision paradigm provides a rigorous foundation for modelling rational and minimal changes to an agent's beliefs. In particular, the maxi-adjustment method, which follows the AGM rationale of belief change, offers a sound and robust computational mechanism to develop adaptive agents so that learning autonomy of these agents can be enhanced. This paper describes how the maxi-adjustment method is applied to develop the learning components of adaptive information filtering agents, and discusses possible difficulties of applying such a framework to these agents.