Workflow-aware attention tracking to enhance collaboration management

Shaokun Fan (), Lele Kang () and J. Leon Zhao ()
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Shaokun Fan: West Texas A&M University
Lele Kang: Nanjing University
J. Leon Zhao: City University of Hong Kong

Information Systems Frontiers, 2015, vol. 17, issue 6, No 6, 1253-1264

Abstract: Abstract Current Workflow Management Systems only capture the start time and end time of tasks and it is difficult for managers to track how much time is actually spent on a particular task if multiple tasks are performed concurrently. Thus, managers may have problems accurately arranging tasks and evaluating costs. In order to facilitate collaboration management in multitasking environments, we propose a workflow-aware attention tracking framework that integrates the concept of attention with workflow models. We first design the meta-model of workflow-aware attention tracking. We further derive seven rules for attention tracking in multitasking collaboration using Object Constraint Language. Based on the rules and meta-model, we develop a procedure for attention rule verification, and present an architecture for workflow-aware attention tracking systems. This research contributes to workflow management by adding attention awareness for collaboration management in multitasking environments.

Keywords: Attention tracking; Collaboration management; Workflow management system; Meta-model; Accountability (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10796-015-9565-2

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