A Closed-loop Workflow Management Technique Based on Process Mining

Liu, Tingyu; Hong, Yi; Zhang, Ni; Liu, Xiaojun

doi:10.1109/mmvip.2008.4749573

Cited by 3 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our previous work in Refs. [38], we present a closed-loop workflow management framework: we apply a statistic approach to derive resource allocation knowledge from the workflow log to assist assigning the resources for the upcoming workflow tasks. The mined staff assignment information is then feedback to construct a closed-loop in workflow resource management.…”

Section: Related Workmentioning

confidence: 99%

Mining event logs to support workflow resource allocation

Liu

Cheng²,

Zhang³

2012

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

a b s t r a c tCurrently, workflow technology is widely used to facilitate the business process in enterprise information systems (EIS), and it has the potential to reduce design time, enhance product quality and decrease product cost. However, significant limitations still exist: as an important task in the context of workflow, many present resource allocation (also known as ''staff assignment'') operations are still performed manually, which are time-consuming. This paper presents a data mining approach to address the resource allocation problem (RAP) and improve the productivity of workflow resource management. Specifically, an Apriorilike algorithm is used to find the frequent patterns from the event log, and association rules are generated according to predefined resource allocation constraints. Subsequently, a correlation measure named lift is utilized to annotate the negatively correlated resource allocation rules for resource reservation. Finally, the rules are ranked using the confidence measures as resource allocation rules. Comparative experiments are performed using C4.5, SVM, ID3, Naïve Bayes and the presented approach, and the results show that the presented approach is effective in both accuracy and candidate resource recommendations.

show abstract

Section: Related Workmentioning

confidence: 99%

Mining event logs to support workflow resource allocation

Liu

Cheng²,

Zhang³

2012

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to the authors, the most significant field for PM implementation is the industrial domain, which stems from the Industry 4.0 objectives of turning traditional manufacturing systems into Cyber-Physical Systems (CPS). Some use cases of PM in the context of manufacturing can be found in [51][52][53][54][55][56]. 1.…”

mentioning

confidence: 99%

Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case

Brzychczy

Gackowiec

Liebetrau³

2020

Resources

View full text Add to dashboard Cite

This paper investigates the application of process mining methodology on the processes of a mobile asset in mining operations as a means of identifying opportunities to improve the operational efficiency of such. Industry 4.0 concepts with related extensive digitalization of industrial processes enable the acquisition of a huge amount of data that can and should be used for improving processes and decision-making. Utilizing this data requires appropriate data processing and data analysis schemes. In the processing and analysis stage, most often, a broad spectrum of data mining algorithms is applied. These are data-oriented methods and they are incapable of mapping the cause-effect relationships between process activities. However, in this scope, the importance of process-oriented analytical methods is increasingly emphasized, namely process mining (PM). PM techniques are a relatively new approach, which enable the construction of process models and their analytics based on data from enterprise IT systems (data are provided in the form of so-called event logs). The specific working environment and a multitude of sensors relevant for the working process causes the complexity of mining processes, especially in underground operations. Hence, an individual approach for event log preparation and gathering contextual information to be utilized in process analysis and improvement is mandatory. This paper describes the first application of the concept of PM to investigate the normal working process of a roof bolter, operating in an underground mine. By applying PM, the irregularities of the operational scheme of this mobile asset have been identified. Some irregularities were categorized as inefficiencies that are caused by either failure of machinery or suboptimal utilization of the same. In both cases, the results achieved by applying PM to the activity log of the mobile asset are relevant for identifying the potential for improving the efficiency of the overall working process.

show abstract