Agent Assignment for Process Management: Goal Modeling for Continuous Resource Management

Talib, Ramzan; Volz, Bernhard; Jablonski, Stefan

doi:10.1007/978-3-642-20511-8_3

Cited by 3 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conventional, DM techniques such as association [31], clustering [28], classification [33], regression applied to the data generated by information systems to predict the behavior and performance of the students deprived of concentrating on the underlying process being executed in the system. EDM techniques [34] are not process-centric and don't concentrate on the event log generated data.…”

Section: Case Studymentioning

confidence: 99%

A Paradigm-Shifting From Domain-Driven Data Mining Frameworks to Process-Based Domain-Driven Data Mining-Actionable Knowledge Discovery Framework

et al. 2020

Self Cite

View full text Add to dashboard Cite

The success of data mining learned rules highly depends on its actionability: how useful it is to perform suitable actions in any real business environment. To improve rule actionability, different researchers have initially presented various Data Mining (DM) frameworks by focusing on different factors only from the business domain dataset. Afterward, different Domain-Driven Data Mining (D3M) frameworks were introduced by focusing on domain knowledge factors from the context of the overall business environment. Despite considering these several dataset factors and domain knowledge factors in different phases of their frameworks, the learned rules still lacked actionability. The objective of our research is to improve the learned rules' actionability. For this purpose, we have analyzed: (1) what overall actions or tasks are being performed in the overall business process, (2) in which sequence different tasks are being performed, (3) under what certain conditions these tasks are being performed, (4) by whom the tasks are being performed (5) what data is provided and produced in performing these tasks. We observed that the inclusion of rule learning factors only from dataset or from domain knowledge is not sufficient. Our Process-based Domain-Driven Data Mining-Actionable Knowledge Discovery (PD3M-AKD) framework explains its different phases to consider and include additional factors from five perspectives of the business process. This PD3M-AKD framework is also in line with the existing phases of current DM and D3M frameworks for considering and including dataset and domain knowledge accordingly. Finally, we evaluated and validated our case study results from different real-life scenarios from education, engineering, and business process domains at the end. INDEX TERMS Actionable knowledge, business process, data mining, data mining framework, domain-driven data mining framework, data privacy.

show abstract

Section: Case Studymentioning

confidence: 99%

A Paradigm-Shifting From Domain-Driven Data Mining Frameworks to Process-Based Domain-Driven Data Mining-Actionable Knowledge Discovery Framework

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, human intelligence, business success factors and expectations etc are eventually incorporated into this mutually-agreed-upon performance measurement infrastructure by these participants. Detailed collaboration of these technical participants along different phases of workflow life cycle is described in [8].…”

Section: Knowledgementioning

confidence: 99%

“…It is domain specific because first, as it defines a precisely and neatly defined performance measurement infrastructure that provides consistent support for domain knowledge in different phases of DDID-PD Framework (Domain Driven In-Depth Pattern Discovery Framework [22]), we will also justify it onward. Second, according to [8], goal construct is defines with the collaborative efforts of process designer, domain expert, data expert and most importantly agents (employee who execute the process) through different phases of workflow lifecycle (workflow analysis, design, implementation execution and evaluation). Therefore, human intelligence, business success factors and expectations etc are eventually incorporated into this mutually-agreed-upon performance measurement infrastructure by these participants.…”

Section: Knowledgementioning

confidence: 99%

“…According to [8], goal definition consists of two parts: Context Definition and Measurement Definition. Context Definition describes the information about different perspectives (i.e.…”

Section: Knowledgementioning

confidence: 99%

“…But, in contrast to competitive business environments, WfMS lack to model measurable goals in business process models and because of that do not support evaluating the performance of certain agents. We identified this issue and recently provided support for goal modeling [8] in a process modeling environment i>PM [9]. This paper addresses static assignment strategies as one reason of poor business process performance in WfMS.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Agent Assignment for Process Management: Agent Performance Evaluation Framework

Talib

Volz

Jablonski

2010

2010 IEEE International Conference on Data Mining Workshops

Self Cite

View full text Add to dashboard Cite

Convergence of data mining and process management is ideal -but still limited. An example of such a convergence is presented in the form of APE Framework that addresses the problem of static-agent-assignment-strategies in Workflow Management Systems (WfMS) -one cause of poor business process performance since all eligible agents may be assigned to a task instead of only assigning those which are expected to be "successful". To solve this problem, the APE Framework first identifies successful agents by investigating the history of workflow executions with methods from data mining and taking the process model into account as a source for domain knowledge. It then updates its findings into an organizational database -a data source wherefrom WfMS make assignments. Thus, through this convergence, the APE Framework enables WfMS to allocate only successful agentsinstead of merely static list of all eligible agents.

show abstract

Agent Assignment for Process Management: Competency-Driven Dynamic Resource Management Methodology

Talib

Volz

Jablonski

2011

2011 IEEE 11th International Conference on Data Mining Workshops

View full text Add to dashboard Cite

Agent Assignment for Process Management: Goal Modeling for Continuous Resource Management

Cited by 3 publications

References 11 publications

A Paradigm-Shifting From Domain-Driven Data Mining Frameworks to Process-Based Domain-Driven Data Mining-Actionable Knowledge Discovery Framework

A Paradigm-Shifting From Domain-Driven Data Mining Frameworks to Process-Based Domain-Driven Data Mining-Actionable Knowledge Discovery Framework

Agent Assignment for Process Management: Agent Performance Evaluation Framework

Agent Assignment for Process Management: Competency-Driven Dynamic Resource Management Methodology

Contact Info

Product

Resources

About