“…All these approaches are focused on user activities and assume that the process documents are either rigorously defined (explicitly or implicitly) or that the documents have no impact on adaptation and thus recommendation. This contrasts with existing work in the area of document-driven processes, where, for example, [9] presents an approach for dynamic workflows supported by software agents. Documents trigger events in the system such as document arrival, document updating, or document rejection.…”
Abstract. Contemporary organisational processes evolve with people's skills and changing business environments. For instance, process documents vary with respect to their structure and occurrence in the process. Supporting users in such settings requires sophisticated learning mechanisms using a range of inputs overlooked by current dynamic process systems. We argue that analysing a document's semantics is of uttermost importance to identify the most appropriate activity which should be carried out next. For a system to reliably recommend the next steps suitable for its user, it should consider both the process structure and the involved documents' semantics. Here we propose a self-learning mechanism which dynamically aggregates a process-based document prediction with a semantic analysis of documents. We present a set of experiments testing the prediction accuracy of the approaches individually then compare them with the aggregated mechanism showing a better accuracy.
“…All these approaches are focused on user activities and assume that the process documents are either rigorously defined (explicitly or implicitly) or that the documents have no impact on adaptation and thus recommendation. This contrasts with existing work in the area of document-driven processes, where, for example, [9] presents an approach for dynamic workflows supported by software agents. Documents trigger events in the system such as document arrival, document updating, or document rejection.…”
Abstract. Contemporary organisational processes evolve with people's skills and changing business environments. For instance, process documents vary with respect to their structure and occurrence in the process. Supporting users in such settings requires sophisticated learning mechanisms using a range of inputs overlooked by current dynamic process systems. We argue that analysing a document's semantics is of uttermost importance to identify the most appropriate activity which should be carried out next. For a system to reliably recommend the next steps suitable for its user, it should consider both the process structure and the involved documents' semantics. Here we propose a self-learning mechanism which dynamically aggregates a process-based document prediction with a semantic analysis of documents. We present a set of experiments testing the prediction accuracy of the approaches individually then compare them with the aggregated mechanism showing a better accuracy.
“…A workflow coordinator in , initiates process instances requested by users, by creating proxy agents and dispatching them to workflow engines. Similarly, the users can control through their interface a stationary agent that creates and dispatch a messenger agent into the right server for certain tasks (Kuo 2004). Agents being e-forms that accept users' invocations are suggested both in Inamoto 1999), while ) generalizes this idea by conceptualizing mobile agents as work-items that are circulated among users.…”
Section: Process Controlmentioning
confidence: 99%
“…Performance agents may also be incorporated in the system for evaluation reasons Ehrler et al 2006;Trappey et al 2009;Kuo 2004;. Sophisticated features for audit, such as learning from previous experiences (Inamoto 1999), recommendation for future enactments , reputation mechanism , and adaptation to modified instances , are fairly advantaged by the features of agents.…”
Section: Audit Managementmentioning
confidence: 99%
“…Their destinations (either engines, machines, resources in general) act upon agents, thereby the process steps forward Inamoto 1999;Barbara et al 1996;Suh et al 2001). Agents could even act upon themselves, by executing an internal method or by modifying their state or behavior Borghoff et al 1997;Kuo 2004). As a final point, we regard agents to wrap services which do the actual work Zeng et al 2001).…”
Section: Execution Of Tasksmentioning
confidence: 99%
“…The WF Engine or another central entity applies either special rules (Joeris 2000;Kappel et al 2000;Borghoff et al 1997;Muller et al 2004); or provides agents with scheduling modules Valetto et al 2001); or utilizes special techniques and algorithms (e.g., temporal logic (Singh 2003), AI planning ), or finally, it follows some prioritization discipline (Fakas and Karakostas 1999;Kuo 2004). Some approaches that jointly use methods of both these categories (negotiation together with some optimization method ) are also proposed.…”
Workflow management systems are an emerging category of information systems, currently under dynamic evolution. On the other hand software agents are a distinct research area as well as an emerging paradigm for information systems design and development. This paper tries to examine the integration of these two fields; reveal the stimulation and the advantages of such a mixing. Popular standards of the workflow management field are used to derive a classification scheme, which is exploited to map existing approaches. As a significant number of existing approaches is reviewed, a plethora of integration patterns are identified and grouped according to the proposed classification scheme. The overall goal of the paper is to clear the vague picture of the consolidation of workflow management systems and software agents and to provide an unifying framework for this intersected area.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.