On-the-Fly Performance-Aware Human Resource Allocation in the Business Process Management Systems Environment Using Naïve Bayes

2015 ◽  
pp. 70-80 ◽  
Author(s):  
Arif Wibisono ◽  
Amna Shifia Nisafani ◽  
Hyerim Bae ◽  
You-Jin Park
Keyword(s):  
Resource Allocation ◽  
Human Resource ◽  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Management Systems

Human resource allocation in business process management and process mining

2018 ◽  
Vol 56 (2) ◽  
pp. 376-405 ◽  
Author(s):  
Michael Arias ◽  
Rodrigo Saavedra ◽  
Maira R. Marques ◽  
Jorge Munoz-Gama ◽  
Marcos Sepúlveda
Keyword(s):  
Resource Allocation ◽  
Case Studies ◽  
Human Resource ◽  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Process Mining ◽  
Research Area ◽  
Systematic Mapping Study ◽  
Content Type

Purpose Human resource allocation is considered a relevant problem in business process management (BPM). The successful allocation of available resources for the execution of process activities can impact on process performance, reduce costs and obtain a better productivity of the resources. In particular, process mining is an emerging discipline that allows improvement of the resource allocation based on the analysis of historical data. The purpose of this paper is to provide a broad review of primary studies published in the research area of human resource allocation in BPM and process mining. Design/methodology/approach A systematic mapping study (SMS) was conducted in order to classify the proposed approaches to allocate human resources. A total of 2,370 studies published between January 2005 and July 2016 were identified. Through a selection protocol, a group of 95 studies were selected. Findings Human resource allocation is an emerging research area that has been evolving over time, generating new proposals that are increasingly applied to real case studies. The majority of proposed approaches relate to the period 2011-2016. Journals and conference proceedings are the most common venues. Validation research and evaluation research are the most common research types. There are two main evaluation methods: simulation and case studies. Originality/value This study aims to provide an initial assessment of the state of the art in the research area of human resource allocation in BPM and process mining. To the best of the authors’ knowledge, this is the first research that has been conducted to date that generates a SMS in this research area.

Design and Realisation of Scalable Business Process Management Systems for Deployment in the Cloud

2021 ◽  
Vol 12 (4) ◽  
pp. 1-26
Author(s):  
Chun Ouyang ◽  
Michael Adams ◽  
Arthur H. M. Ter Hofstede ◽  
Yang Yu
Keyword(s):  
Load Balancing ◽  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Business Processes ◽  
Management Systems ◽  
User Demand ◽  
Process Engine ◽  
Automated Support ◽  
Dynamic Execution

Business Process Management Systems ( BPMSs ) provide automated support for the execution of business processes in modern organisations. With the emergence of cloud computing, BPMS deployment considerations are shifting from traditional on-premise models to the Software-as-a-Service ( SaaS ) paradigm, aiming at delivering Business Process Automation as a Service. However, scaling up a traditional BPMS to cope with simultaneous demand from multiple organisations in the cloud is challenging, since its underlying system architecture has been designed to serve a single organisation with a single process engine. Moreover, the complexity in addressing both the dynamic execution environment and the elasticity requirements of users impose further challenges to deploying a traditional BPMS in the cloud. A typical SaaS often deploys multiple instances of its core applications and distributes workload to these application instances via load balancing. But, for stateful and often long-running process instances, standard stateless load balancing strategies are inadequate. In this article, we propose a conceptual design of BPMS capable of addressing dynamically varying demands of end users in the cloud, and present a prototypical implementation using an open source traditional BPMS platform. Both the design and system realisation offer focused strategies on achieving scalability and demonstrates the system capabilities for supporting both upscaling, to address large volumes of user demand or workload, and downscaling, to release underutilised computing resources, in a cloud environment.

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