Elsevier

Decision Support Systems

Volume 83, March 2016, Pages 1-9
Decision Support Systems

Enabling flexible location-aware business process modeling and execution

https://doi.org/10.1016/j.dss.2015.12.003Get rights and content

Highlights

  • A novel business process modeling approach is presented that is able to couple control-flow concerns with geospatial constraints;

  • A formalized mapping is defined to convert our approach to coloured Petri nets (CPN) so as to enact and validate the models;

  • A prototype implementation is developed to illustrate the feasibility ofour approach;

  • Our approach allows to constrain and monitor process behavior based on location-driven constraints during their execution.

Abstract

Business process management (BPM) has emerged as one of the abiding systematic management approaches in order to design, execute and govern organizational business processes. Traditionally, most attention within the BPM community has been given to studying control-flow aspects, without taking other contextual aspects into account. This paper contributes to the existing body of work by focusing on the particular context of geospatial information. We argue that explicitly taking this context into consideration in the modeling and execution of business processes can contribute to improve their effectiveness and efficiency. As such, the goal of this paper is to make the modeling and execution aspects of BPM location-aware, i.e. to govern and constrain control-flow and process behavior based on location-based constraints. We do so by proposing a Petri net modeling extension which is formalized by means of a mapping to colored Petri nets (CPNs). Our approach has been implemented using CPN Tools and a simulation extension was developed to support the execution of location-aware process models. We also illustrate the feasibility of coupling business process support systems with geographic information systems by means of an experimental case.

Introduction

Throughout the past two decades, business process management (BPM) has emerged as one of the abiding systematic management approaches to align organizational business processes to the needs of clients [34]. BPM encompasses a broad scope, including the design, modeling, execution, monitoring and optimization of business processes — the so-called BPM life cycle [33]. The main driving rationale for BPM is that it enables organizations to be more efficient and more capable to react to changes. From this viewpoint, BPM regards processes as core strategic assets of an organization, which hence need to be understood, managed, and improved to increase the value added by products or services delivered to clients.

The emergence of BPM has caused a shift in the realm of information systems and information technology from data-based information systems to process-aware ones, i.e. “Process-Aware Information Systems”, or PAIS. The support provided by PAIS — be it for the modeling, execution, validation or monitoring of business processes — is only able to capture and describe an idealized or simplified version of reality. Traditionally, most attention within the BPM community has been focused on studying control-flow aspects of business processes, i.e. the aspects governing the flow of business activities (i.e. the sequence in which activities can be performed). In recent years, however, integrating other perspectives and “contexts” within this view has received increased attention, as support systems which adopt a control-flow-centric view are unable to adequately capture human behavior due to lack of descriptions of possible constraints against activity modeling. Similarly, support systems focusing only on data aspects fail to capture the flow and sequence aspects of the data as it moves through a business process. As such, many scholars have shifted towards studying various approaches that integrate control-flow with other contexts. In this paradigm, processes can be rapidly changed and adapted to a new external data-governed context (e.g., location and weather). It is recognized that contextualizing processes in this manner allows for a more explicit consideration of the environmental setting of a process [30].

This paper contributes to the research field of BPM by focusing on the particular context of geospatial information, an aspect which is becoming more and more important in all information system related areas, given the increased usage of mobile devices and tracking as well as other recent developments such as the Internet of Things or sensor-based data gathering. We hence argue that taking this context into account in the various life cycle steps of BPM can contribute to improve the effectiveness and efficiency of process management. Especially in environments where a need arises to apply both process-aware and Geographic Information Systems (GIS), it is highly valuable to combine and integrate these two perspectives, instead of considering them in isolation [24]. The goal of this paper is thus to make the modeling and execution aspects of BPM “location-aware”. We do so by proposing business process modeling language based on a formal Petri net extension which incorporates location aspects and ways to constrain the execution of activities by location-based constraints. Next, we formalize the execution semantics of our extension by describing a unambiguous mapping to colored Petri nets. This also allows us to develop a prototype implementation of our approach using CPN Tools [17], with which a simulation extension was developed to support the execution and validation of models created using our approach and to illustrate the feasibility of coupling business process support systems with geographic information systems.

The remainder of this paper is structured as follows. Section 2 provides an overview of related work and preliminaries used throughout the paper. Section 3 outlines a running example which will be used to illustrate the developed artifacts. Section 4 introduces our proposed modeling language to design location-aware processes, after which Section 5 discusses the execution semantics of such models by means of a mapping to colored Petri nets. Section 6 discusses the developed implementation. Section 7 concludes the paper and provides outlines for future work.

Section snippets

Related work

We regard location as one of the key variables in the wider context of a business process. In the layered process context model proposed by Rosemann et al. [30], location describes an important variable situated in the environmental context layer, which describes process-related variables that reside beyond the business network in which an organization is embedded, but still pose a contingency effect on the business processes. Scholars have argued that the inclusion of location contextual

Running example

To illustrate our location-aware modeling approach and its execution semantics, we will utilize a running example throughout this paper, extending an example provided in [11]. Looping and parallel behavior was added to show how our approach can be applied on more complex control-flow constructs. The basic process model (no location-awareness) is depicted as a WF-net in Fig. 1. The example process describes a technical maintenance service, which is executed as follows. The process is started

Location-aware process modeling

This section discusses our proposed modeling approach to model location-aware processes. Our methodology is based on an extension of Petri nets, incorporating two new constructs, namely location dependent transitions and location constraints.

Fig. 2 shows the running example modeled using our location-aware extension. Using this modeling method, transitions can be made location dependent (indicated visually with a flag,

), which means that a feature, belonging to a specified feature type, will

Executing location-aware process models

Our LAWF-net modeling extension provides a straightforward and understandable means to merge location aspects with control-flow concerns. Although we have provided execution semantics in the section above in accordance with those of a WF-net, we also define a mapping from LAWF-nets to CPN models, driven by the following reasons. First, as we will see later, mapping LAWF-nets to CPN models enables to use existing tools to drive the execution of location-aware processes. Second, as we will show

Implementation and system integration

The converted running example shown in Fig. 3 was implemented as a CPN model using the well-known CPN Tools program [17]. Due to some limitations of this tool, the CPN model in Fig. 3 contains some additional constructs which are not part of the formalization. First, the addition of “dummy” unlabeled transitions before some location dependent transitions. For CRT, for instance, this is necessary due to the fact that repair teams might take some time to be in the vicinity of the customer's site.

Conclusions

For the most part, the modeling and execution of business process models has so far been confined to a rather limiting environment, focusing mainly on control-flow aspects only, without taking rich contextual aspects into account. In this paper, we have focused our attention towards making the modeling and execution of business processes location-aware, focusing on the particular context of geospatial information. A Petri net modeling extension was proposed which incorporates location aspects

Acknowledgments

This work is supported by the National Key Technology R&D Program, China (grant 2012BAH01F02), the KU Leuven Research Council (grant OT/10/010) and the Flemish Research Council (Odysseus grant B.0915.09).

Xinwei Zhu received a PhD in Software Engineering from Wuhan University, China (ranked number five of the best universities there) after obtaining a Master's degree in the same field. Currently, she is working on topics such as business process management, business process modeling, geospatial-based process innovation, and flexible process design. She has published in various internationally renowned journals.

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  • Cited by (0)

    Xinwei Zhu received a PhD in Software Engineering from Wuhan University, China (ranked number five of the best universities there) after obtaining a Master's degree in the same field. Currently, she is working on topics such as business process management, business process modeling, geospatial-based process innovation, and flexible process design. She has published in various internationally renowned journals.

    Seppe vanden Broucke received a PhD in Applied Economics at KU Leuven (Catholic University Leuven), Belgium in 2014 after obtaining a Master's degree (magna cum laude) in Business Economics: Information Systems Engineer from the same institution. Currently, he is working as a postdoctoral researcher at the Department of Decision Sciences and Information Management at KU Leuven. His research interests include business data mining and analytics, machine learning, process management, and process mining. His work has been published in well-known international journals and presented at top conferences.

    Guobin Zhu received his doctorate from the Ben-Gurion University of the Negev, Israel. Currently, he is working as a professor in the International School of Software, Wuhan University, China. He is a member of the Association of American Geographers (AAG), the Israeli Society for Photogrammetry and Remote Sensing (ILSPRS) and a member of the Chinese Association of Photogrammetry and Remote Sensing. He is working on large-scale geographic information systems topics, involving spatial data standards, spatial databases and so on. He has published more than thirty articles in renowned journal and international conferences.

    Jan Vanthienen received his PhD degree in Applied Economics from KU Leuven, Belgium. He is a full professor of Information Systems with the Department of Decision Sciences and Information Management, KU Leuven. He is the author or co-author of numerous papers published in international journals and conference proceedings. His current research interests include information and knowledge management, business intelligence and business rules, and information systems analysis and design.

    Bart Baesens is a full professor at KU Leuven, Belgium, and a lecturer at the University of Southampton, United Kingdom. He has done extensive research on predictive analytics, data mining, web analytics, fraud detection, and credit risk management. His findings have been published in well-known international journals and presented at international top conferences. He is also co-author of the book Credit Risk Management: Basic Concepts, published in 2008. He regularly tutors, advices and provides consulting support to international firms with respect to their data mining, predictive analytics, CRM, and credit risk management policy.

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