Managing process model collections with AProMoRe

As organizations reach higher levels of Business Process Management maturity, they tend to collect numerous business process models. Such models may be linked with each other or mutually overlap, supersede one another and evolve over time. Moreover, they may be represented at different abstraction levels depending on the target audience and modeling purpose, and may be available in multiple languages (e.g. due to company mergers). Thus, it is common that organizations struggle with keeping track of their process models. This demonstration introduces AProMoRe (Advanced Process Model Repository) which aims to facilitate the management of (large) process model collections.

Efficient querying of large process model repositories

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective and less error-prone than developing them from scratch. Since process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. To make our approach more applicable, we consider the semantic similarity between labels. Experiments are conducted to demonstrate that our approach is efficient.

Managing large collections of business process models - Current techniques and challenges

Nowadays, business process management is an important approach for managing organizations from an operational perspective. As a consequence, it is common to see organizations develop collections of hundreds or even thousands of business process models. Such large collections of process models bring new challenges and provide new opportunities, as the knowledge that they encapsulate requires to be properly managed. Therefore, a variety of techniques for managing large collections of business process models is being developed. The goal of this paper is to provide an overview of the management techniques that currently exist, as well as the open research challenges that they pose.

Fast detection of exact clones in business process model repositories

As organizations reach higher levels of business process management maturity, they often find themselves maintaining very large process model repositories, representing valuable knowledge about their operations. A common practice within these repositories is to create new process models, or extend existing ones, by copying and merging fragments from other models. We contend that if these duplicate fragments, a.k.a. ex- act clones, can be identified and factored out as shared subprocesses, the repository’s maintainability can be greatly improved. With this purpose in mind, we propose an indexing structure to support fast detection of clones in process model repositories. Moreover, we show how this index can be used to efficiently query a process model repository for fragments. This index, called RPSDAG, is based on a novel combination of a method for process model decomposition (namely the Refined Process Structure Tree), with established graph canonization and string matching techniques. We evaluated the RPSDAG with large process model repositories from industrial practice. The experiments show that a significant number of non-trivial clones can be efficiently found in such repositories, and that fragment queries can be handled efficiently.

Factors of process model comprehension : findings from a series of experiments

In order to make good decisions about the design of information systems, an essential skill is to understand process models of the business domain the system is intended to support. Yet, little knowledge to date has been established about the factors that affect how model users comprehend the content of process models. In this study, we use theories of semiotics and cognitive load to theorize how model and personal factors influence how model viewers comprehend the syntactical information of process models. We then report on a four-part series of experiments, in which we examined these factors. Our results show that additional semantical information impedes syntax comprehension, and that theoretical knowledge eases syntax comprehension. Modeling experience further contributes positively to comprehension efficiency, measured as the ratio of correct answers to the time taken to provide answers. We discuss implications for practice and research.

A formative measurement model of business process model quality

Business process management (BPM) is becoming the dominant management paradigm. Business process modelling is central to BPM, and the resultant business process model the core artefact guiding subsequent process change. Thus, model quality is at the centre, mediating between the modelling effort and related growing investment in ultimate process improvements. Nonetheless, though research interest in the properties that differentiate high quality process models is longstanding, there have been no past reports of a valid, operationalised, holistic measure of business process model quality. In attention to this gap, this paper reports validation of a Business Process Model Quality measurement model, conceptualised as a single-order, formative index. Such a measurement model has value as the dependent variable in rigorously researching the drivers of model quality; as antecedent of ultimate process improvements; and potentially as an economical comparator and diagnostic for practice.

Detecting approximate clones in process model repositories with Apromore

Approximate clone detection is the process of identifying similar process fragments in business process model collections. The tool presented in this paper can efficiently cluster approximate clones in large process model repositories. Once a repository is clustered, users can filter and browse the clusters using different filtering parameters. Our tool can also visualize clusters in the 2D space, allowing a better understanding of clusters and their member fragments. This demonstration will be useful for researchers and practitioners working on large process model repositories, where process standardization is a critical task for increasing the consistency and reducing the complexity of the repository.

A study on the effects of routing symbol design on process model comprehension

Process modeling grammars are used to create models of business processes. In this paper, we discuss how different routing symbol designs affect an individual's ability to comprehend process models. We conduct an experiment with 154 students to ascertain which visual design principles influence process model comprehension. Our findings suggest that design principles related to perceptual discriminability and pop out improve comprehension accuracy. Furthermore, semantic transparency and aesthetic design of symbols lower the perceived difficulty of comprehension. Our results inform important principles about notational design of process modeling grammars and the effective use of process modeling in practice.

A flexible asset maintenance decision-making process model

Optimal Asset Maintenance decisions are imperative for efficient asset management. Decision Support Systems are often used to help asset managers make maintenance decisions, but high quality decision support must be based on sound decision-making principles. For long-lived assets, a successful Asset Maintenance decision-making process must effectively handle multiple time scales. For example, high-level strategic plans are normally made for periods of years, while daily operational decisions may need to be made within a space of mere minutes. When making strategic decisions, one usually has the luxury of time to explore alternatives, whereas routine operational decisions must often be made with no time for contemplation. In this paper, we present an innovative, flexible decision-making process model which distinguishes meta-level decision making, i.e., deciding how to make decisions, from the information gathering and analysis steps required to make the decisions themselves. The new model can accommodate various decision types. Three industrial case studies are given to demonstrate its applicability.

APQL : a process-model query language

As business process management technology matures, organisations acquire more and more business process models. The management of the resulting collections of process models poses real challenges. One of these challenges concerns model retrieval where support should be provided for the formulation and efficient execution of business process model queries. As queries based on only structural information cannot deal with all querying requirements in practice, there should be support for queries that require knowledge of process model semantics. In this paper we formally define a process model query language that is based on semantic relationships between tasks in process models and is independent of any particular process modelling notation.

Efficient querying of large process model repositories

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective and less error-prone than developing them from scratch. Since process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. To make our approach more applicable, we consider the semantic similarity between labels. Experiments are conducted to demonstrate that our approach is efficient.

Integrated configuration of enterprise systems for interoperability : towards process model and business document specification alignment

Enterprise Systems (ES) can be understood as the de facto standard for holistic operational and managerial support within an organization. Most commonly ES are offered as commercial off-the-shelf packages, requiring customization in the user organization. This process is a complex and resource-intensive task, which often prevents small and midsize enterprises (SME) from undertaking configuration projects. Especially in the SME market independent software vendors provide pre-configured ES for a small customer base. The problem of ES configuration is shifted from the customer to the vendor, but remains critical. We argue that the yet unexplored link between process configuration and business document configuration must be closer examined as both types of configuration are closely tied to one another.

Open Simulator BPMN Process Model Editor

Video presented as part of APCCM 2010 conference in Brisbane Australia. Video illustrating the main components of an Open Simulator BPMN Editor we have developed.

Determining process model precision and generalization with weighted artificial negative events

Process mining encompasses the research area which is concerned with knowledge discovery from event logs. One common process mining task focuses on conformance checking, comparing discovered or designed process models with actual real-life behavior as captured in event logs in order to assess the “goodness” of the process model. This paper introduces a novel conformance checking method to measure how well a process model performs in terms of precision and generalization with respect to the actual executions of a process as recorded in an event log. Our approach differs from related work in the sense that we apply the concept of so-called weighted artificial negative events towards conformance checking, leading to more robust results, especially when dealing with less complete event logs that only contain a subset of all possible process execution behavior. In addition, our technique offers a novel way to estimate a process model’s ability to generalize. Existing literature has focused mainly on the fitness (recall) and precision (appropriateness) of process models, whereas generalization has been much more difficult to estimate. The described algorithms are implemented in a number of ProM plugins, and a Petri net conformance checking tool was developed to inspect process model conformance in a visual manner.