Workflow technology has become a standard solution for managing increasingly complex business processes. Successful business process management depends on effective workflow modeling and analysis. One of the important aspects of workflow analysis is the data-flow perspective because, given a syntactically correct process sequence, errors can still occur during workflow execution due to incorrect data-flow specifications. However, there have been only scant treatments of the data-flow perspective in the literature and no formal methodologies are available for systematically discovering data-flow errors in a workflow model. As an indication of this research gap, existing commercial workflow management systems do not provide tools for data-flow analysis at design time. In this paper, we provide a data-flow perspective for detecting data-flow anomalies such as missing data, redundant data, and potential data conflicts. Our data-flow framework includes two basic components: data-flow specification and data-flow analysis; these components add more analytical rigor to business process management.
Retrieving information from heterogeneous database systems involves a complex process and remains a challenging research area.We propose a cognitively guided approach for developing an information-retrieval agent that takes the user's information request, identifies relevant information sources,and generates a multidatabase access plan. Our work is distinctive in that the agent design is based on an empirical study of how human experts retrieve information from multiple, heterogeneous database systems. To improve on empirically observed information-retrieval capabilities, the design incorporates mathematical models and algorithmic components. These components optimize the set of information sources that need to be considered to respond to a user query and are used to develop efficient multidatabase-access plans. This agent design, which integrates cognitive and mathematical models, has been implemented using Soar, a knowledge-based architecture.
Organizations require ways to efficiently distribute information such as news releases, seminar announcements, and memos. While the machinery for information storage, manipulation, and retrieval exists, research dealing directly with its distribution in an organizational context is scarce. In this paper, we address this need by first examining the pros and cons of the conventional "mailing lists" approach and then proposing new workflow mechanisms that improve the efficiency and effectiveness of information distribution through e-mail. The proposed approach is relevant to other information distribution approaches beyond e-mail. The main contributions of this study include: (1) offering a workflow perspective on organizational information distribution; (2) analysis of workflows in two new information distribution methods based on dynamic mailing lists and profile matching, respectively; and (3) proposing a new way of matching supply and demand of information that extends existing information filtering algorithms.