To overcome some limitations of existing models that organize knowledge on the World Wide Web for online learning, this paper proposes a novel method of using graphical process models to organize knowledge on the Web and to guide the learning process. Process models can not only visually represent the logical relationships (i.e., if … then, all, at least one, and exactly one) of knowledge elements, but also display learning paths that consist of logically connected knowledge elements. This paper also presents a design of an innovative learning system that integrates process models with some existing knowledge organization models to facilitate online learning. This paper makes a useful contribution by proposing process-based knowledge organization as well as providing a visual representation of learning paths that explicitly prompt people to follow a logical process for effective learning.

A number of information systems have been developed to automate business processes. For process modeling, verification, and automation in information systems, a formal semantics of control-flow process models is needed. Usually process modeling languages (e.g., BPMN, EPC, IDEF3, UML, and WfMC standards) are used to represent control-flow process models. When these process modeling languages are developed, their informal semantics are typically described using examples, but their formal semantics are not defined. Although many different semantics for control-flow process models have been proposed, the existing semantics specifications have limitations because they do not support certain desirable features. In this paper, we propose a new formal semantics for control-flow process models. We show that it is more accurate, complete, and applicable than the existing semantics specifications.

The existing Science Citation Index only counts direct citations, whereas PageRank disregards the number of direct citations. We propose a new Comprehensive Citation Index (CCI) that evaluates both direct and indirect intellectual influence of research papers, and show that CCI is more reliable in discovering research papers with far-reaching influence.