Search

Conformance checking enables organizations to automatically identify compliance violations based on the analysis of observed event data. A crucial requirement for conformance-checking techniques is that observed events can be mapped to normative process models used to specify allowed behavior. Without a mapping, it is not possible to determine if an observed event trace conforms to the specification or not. A considerable problem in this regard is that establishing a mapping between events and process model activities is an inherently uncertain task. Since the use of a particular mapping directly influences the conformance of an event trace to a specification, this uncertainty represents a major issue for conformance checking. To overcome this issue, we introduce a probabilistic conformance-checking technique that can deal with uncertain mappings. Our technique avoids the need to select a single mapping by taking the entire spectrum of possible mappings into account. A quantitative evaluation demonstrates that our technique can be applied on a considerable number of real-world processes where existing conformance-checking techniques fail.

Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models’ activity labels. Since these labels are typically short and diverse in terms of their grammatical style, standard natural language processing tools are not suitable to analyze them. While a dedicated technique for the analysis of process model activity labels was proposed in the past, it suffers from considerable limitations. First of all, its performance varies greatly among data sets with different characteristics and it cannot handle uncommon grammatical styles. What is more, adapting the technique requires in-depth domain knowledge. We use this paper to propose a machine learning-based technique for activity label analysis that overcomes the issues associated with this rule-based state of the art. Our technique conceptualizes activity label analysis as a tagging task based on a Hidden Markov Model. By doing so, the analysis of activity labels no longer requires the manual specification of rules. An evaluation using a collection of 15,000 activity labels demonstrates that our machine learning-based technique outperforms the state of the art in all aspects.

Despite the widespread availability of process modeling tools, the first version of a process model is often drawn by hand on a piece of paper or whiteboard, especially when several people are involved in its elicitation. Though this has been found to be beneficial for the modeling task itself, it also creates the need to manually convert hand-drawn models afterward, such that they can be further used in a modeling tool. This manual transformation is associated with considerable time and effort and, furthermore, creates undesirable friction in the modeling workflow. In this paper, we alleviate this problem by presenting a technique that can automatically recognize and convert a sketch process model into a digital BPMN model. A key driver and contribution of our work is the creation of a publicly available dataset consisting of 502 manually annotated, hand-drawn BPMN models, covering 25 different BPMN elements. Based on this data set, we have established a neural network-based recognition technique that can reliably recognize and transform hand-drawn BPMN models. Our evaluation shows that our technique considerably outperforms available baselines and, therefore, provides a valuable basis to smoothen the modeling process.

Business & information systems engineering 63(2), 145–156 (2020). doi:10.1007/s12599-020-00646-z, Published by AIS, Atlanta, Georgia

Textual process descriptions are widely used in organizations since they can be created and understood by virtually everyone. Because of their widespread use, they also provide a valuable source for process analysis, such as compliance checking. However, the inherent ambiguity of natural language impedes the automated analysis of textual process descriptions. While human readers can use their context knowledge to correctly understand statements with multiple possible interpretations, automated tools currently have to make assumptions about their correct meaning. As a result, compliance-checking techniques are prone to draw incorrect conclusions about the proper execution of a process. To provide a comprehensive solution to these reasoning problems, we use this paper to introduce the concept of a behavioral space as a means to deal with behavioral ambiguity in textual process descriptions. A behavioral space captures all possible interpretations of a textual process description in a systematic manner. Thus, it avoids the problem of focusing on a single, possibly incorrect interpretation. We use a quantitative evaluation with a set of 47 textual process descriptions to demonstrate the usefulness of a behavioral space for compliance checking in the context of ambiguous texts.

Anomaly detection in process mining aims to recognize outlying or unexpected behavior in event logs for purposes such as the removal of noise and identification of conformance violations. Existing techniques for this task are primarily frequency-based, arguing that behavior is anomalous because it is uncommon. However, such techniques ignore the semantics of recorded events and, therefore, do not take the meaning of potential anomalies into consideration. In this work, we overcome this caveat and focus on the detection of anomalies from a semantic perspective, arguing that anomalies can be recognized when process behavior does not make sense. To achieve this, we propose an approach that exploits the natural language associated with events. Our key idea is to detect anomalous process behavior by identifying semantically inconsistent execution patterns. To detect such patterns, we first automatically extract business objects and actions from the textual labels of events. We then compare these against a process-independent knowledge base. By populating this knowledge base with patterns from various kinds of resources, our approach can be used in a range of contexts and domains. We demonstrate the capability of our approach to successfully detect semantic execution anomalies through an evaluation based on a set of real-world and synthetic event logs and show the complementary nature of semantics-based anomaly detection to existing frequency-based techniques.

Monitoring process performance is an important means for organizations to identify opportunities to improve their operations. The definition of suitable Process Performance Indicators (PPIs) is a crucial task in this regard. Because PPIs need to be in line with strategic business objectives, the formulation of PPIs is a managerial concern. Managers typically start out to provide relevant indicators in the form of natural language PPI descriptions. Therefore, considerable time and effort have to be invested to transform these descriptions into PPI definitions that can actually be monitored. This work presents an approach that automates this task. The presented approach transforms an unstructured natural language PPI description into a structured notation that is aligned with the implementation underlying a business process. To do so, we combine Hidden Markov Models and semantic matching techniques. A quantitative evaluation on the basis of a data collection obtained from practice demonstrates that our approach works accurately. Therefore, it represents a viable automated alternative to an otherwise laborious manual endeavor

Documenting business processes using process models is common practice in many organizations. However, not all process information is best captured in process models. Hence, many organizations complement these models with textual descriptions that specify additional details. The problem with this supplementary use of textual descriptions is that existing techniques for automatically searching process repositories are limited to process models. They are not capable of taking the information from textual descriptions into account and, therefore, provide incomplete search results. In this paper, we address this problem and propose a technique that is capable of searching textual as well as model-based process descriptions. It automatically extracts activity-related and behavioral information from both descriptions types and stores it in a unified data format. An evaluation with a large Austrian bank demonstrates that the additional consideration of textual descriptions allows us to identify more relevant processes from a repository.

Process models are an important means to capture information on organizational operations and often represent the starting point for process analysis and improvement. Since the manual elicitation and creation of process models is a time-intensive endeavor, a variety of techniques have been developed that automatically derive process models from textual process descriptions. However, these techniques, so far, only focus on the extraction of traditional, imperative process models. The extraction of declarative process models, which allow to effectively capture complex process behavior in a compact fashion, has not been addressed. In this paper we close this gap by presenting the first automated approach for the extraction of declarative process models from natural language. To achieve this, we developed tailored Natural Language Processing techniques that identify activities and their inter-relations from textual constraint descriptions. A quantitative evaluation shows that our approach is able to generate constraints that closely resemble those established by humans. Therefore, our approach provides automated support for an otherwise tedious and complex manual endeavor.

The continuous digitization requires organizations to improve the automation of their business processes. Among others, this has lead to an increased interest in Robotic Process Automation (RPA). RPA solutions emerge in the form of software that automatically executes repetitive and routine tasks. While the benefits of RPA on cost savings and other relevant performance indicators have been demonstrated in different contexts, one of the key challenges for RPA endeavors is to effectively identify processes and tasks that are suitable for automation. Textual process descriptions, such as work instructions, provide rich and important insights about this matter. However, organizations often maintain hundreds or even thousands of them, which makes a manual analysis unfeasible for larger organizations. Recognizing the large manual effort required to determine the current degree of automation in an organization’s business processes, we use this paper to propose an approach that is able to automatically do so. More specifically, we leverage supervised machine learning to automatically identify whether a task described in a textual process description is manual, an interaction of a human with an information system or automated. An evaluation with a set of 424 activities from a total of 47 textual process descriptions demonstrates that our approach produces satisfactory results.

System-related engineering tasks are often conducted using process models. In this context, it is essential that these models do not contain structural or terminological inconsistencies. To this end, several automatic analysis techniques have been proposed to support quality assurance. While formal properties of control flow can be checked in an automated fashion, there is a lack of techniques addressing textual quality. More specifically, there is currently no technique available for handling the issue of lexical ambiguity caused by homonyms and synonyms. In this paper, we address this research gap and propose a technique that detects and resolves lexical ambiguities in process models. We evaluate the technique using three process model collections from practice varying in size, domain, and degree of standardization. The evaluation demonstrates that the technique significantly reduces the level of lexical ambiguity and that meaningful candidates are proposed for resolving ambiguity.

Although several approaches for service identification have been defined in research and practice, there is a notable lack of fully automated techniques. In this paper, we address the problem of manual work in the context of service derivation and present an approach for automatically deriving service candidates from business process model repositories. Our approach leverages semantic technology in order to derive ranked lists of useful service candidates. An evaluation of the approach with three large process model collection from practice indicates that the approach can effectively identify useful services with hardly any manual effort. The evaluation further demonstrates that our approach can address varying degrees of service cohesion by applying different aggregation mechanisms. Hence, the presented approach represents a useful artifact for enabling business and IT managers to quickly spot reuse potential in their company. In addition, our approach improves the alignment between business and IT. As the ranked service candidates give a good impression on the relative importance of a business operation, they can provide companies with first clues on where IT support is needed and where it could be reduced.

Healthcare can be considerably expensive for both patients and insurance companies. In some cases, high costs in healthcare are an indirect outcome of a low quality of care, for example, when treatments have to be repeated. Unfortunately, identifying the factors that lead to such repetitions is a complex and challenging task. In this paper, we focus on the domain of dental healthcare and develop an approach that can predict treatment repetitions in the context of the implant denture therapy process. The challenges associated with predicting treatment repetitions in this setting are considerable. First, hardly any patient undergoes the exact same series of treatments like another. This results in a high degree of variation in the data. Second, only a few patients experience treatment repetitions. This lead to a highly imbalance in the data. To address these challenges, we develop a prediction technique that particularly exploits the process perspective. What is more, we apply so-called resampling methods to deal with the imbalance in the data. Our resulting model is able to predict treatment repetitions with an AUC value of 0.69.

Process models play an important role for specifying requirements of business-related software. However, the usefulness of process models is highly dependent on their quality. Recognizing this, researches have proposed various techniques for the automated quality assurance of process models. A considerable shortcoming of these techniques is the assumption that each activity label consistently refers to a single stream of action. If, however, activities textually describe control flow related aspects such as decisions or conditions, the analysis results of these tools are distorted. Due to the ambiguity that is associated with this misuse of natural language, also humans struggle with drawing valid conclusions from such inconsistently specified activities. In this paper, we therefore introduce the notion of canonicity to prevent the mixing of natural language and modeling language. We identify and formalize non-canonical patterns, which we then use to define automated techniques for detecting and refactoring activities that do not comply with it. We evaluated these techniques by the help of four process model collections from industry, which confirmed the applicability and accuracy of these techniques.

In recent years, a considerable number of process model matching techniques have been proposed. The goal of these techniques is to identify correspondences between the activities of two process models. However, the results from the Process Model Matching Contest 2015 reveal that there is still no universally applicable matching technique and that each technique has particular strengths and weaknesses. It is hard or even impossible to choose the best technique for a given matching problem. We propose to cope with this problem by running an ensemble of matching techniques and automatically selecting a subset of the generated correspondences. To this end, we propose a Markov Logic based optimization approach that automatically selects the best correspondences. The approach builds on an adaption of a voting technique from the domain of schema matching and combines it with process model specific constraints. Our experiments show that our approach is capable of generating results that are significantly better than alternative approaches.

Many organizations maintain textual process descriptions alongside graphical process models. The purpose is to make process information accessible to various stakeholders, including those who are not familiar with reading and interpreting the complex execution logic of process models. Despite this merit, there is a clear risk that model and text become misaligned when changes are not applied to both descriptions consistently. For organizations with hundreds of different processes, the effort required to identify and clear up such conflicts is considerable. To support organizations in keeping their process descriptions consistent, we present an approach to automatically identify inconsistencies between a process model and a corresponding textual description. Our approach detects cases where the two process representations describe activities in different orders and detect process model activities not contained in the textual description. A quantitative evaluation with 53 real-life model-text pairs demonstrates that our approach accurately identifies inconsistencies between model and text. HighlightsWe propose an approach to detect conflicts between textual and model-based process descriptions.The approach is fully automatic based on tailored natural language processing techniques.Quantitative evaluation demonstrates the applicability of the approach on real-life data.

Process model matching refers to the automatic detection of semantically equivalent or similar activities between two process models. The output of process model matchers is the basis for many advanced process model analysis techniques and, therefore, must be as accurate as possible. Measuring the performance of process model matchers, however, is a difficult task. On the one hand, it is hard to define which correspondences are actually correct. On the other hand, it is challenging to appropriately take the output of matchers into account, because they often produce confidence values between zero and one. In this paper, we propose the first evaluation procedure for process model matchers that addresses both of these challenges. The core idea is to rank both the computed and the desired correspondences based on their confidence values and compare them using the Spearman’s rank correlation coefficient. We perform an in-depth evaluation in which we apply the new evaluation procedure and illustrate how it helps gaining interesting insights.

Checklists are in use in many work domains, including aviation, manufacturing, quality control, and healthcare. Despite their adoption, the literature shows both breadth and persistence of problems with the organizational usage of checklists. In this paper, we conduct a structured literature survey to analyze checklists from the perspective of informational artifacts. Our contribution is a respective conceptualization of checklists and a rigorous analysis of their problems. As we will argue, these insights help to consider how the capabilities of IT systems can be leveraged to improve checklists and address their problematic aspects. We present our work as a basis for IT-oriented research into a relevant yet under-examined information practice in organizational work routines.

Context: The analysis of requirements for business-related software systems is often supported by using business process models. However, the final requirements are typically still specified in natural language. This means that the knowledge captured in process models must be consistently transferred to the specified requirements. Possible inconsistencies between process models and requirements represent a serious threat for the successful development of the software system and may require the repetition of process analysis activities. Objective: The objective of this paper is to address the problem of inconsistency between process models and natural language requirements in the context of software development. Method: We define a semi-automated approach that consists of a process model-based procedure for capturing execution-related data in requirements models and an algorithm that takes these models as input for generating natural language requirements. We evaluated our approach in the context of a multiple case study with three organizations and a total of 13 software development projects. Results: We found that our approach can successfully generate well-readable requirements, which do not only positively contribute to consistency, but also to the completeness and maintainability of requirements. The practical use of our approach to identify a suitable subcontractor on the market in 11 of the 13 projects further highlights the practical value of our approach. Conclusion: Our approach provides a structured way to obtain high-quality requirements documents from process models and to maintain textual and visual representations of requirements in a consistent way., European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 660646

Companies increasingly use business process modeling for documenting and redesigning their operations. However, due to the size of such modeling initiatives, they often struggle with the quality assurance of their model collections. While many model properties can already be checked automatically, there is a notable gap of techniques for checking linguistic aspects such as naming conventions of process model elements. In this paper, we address this problem by introducing an automatic technique for detecting violations of naming conventions. This technique is based on text corpora and independent of linguistic resources such as WordNet. Therefore, it can be easily adapted to the broad set of languages for which corpora exist. We demonstrate the applicability of the technique by analyzing nine process model collections from practice, including over 27,000 labels and covering three different languages. The results of the evaluation show that our technique yields stable results and can reliably deal with ambiguous cases. In this way, this paper provides an important contribution to the field of automated quality assurance of conceptual models. We present an automatic technique for detecting violations of naming conventions.The technique is based on text corpora and independent of linguistic resources.Because of its design, the approach can be easily adapted to other languages.The evaluation includes 27,000 labels and three different languages.

In many organizations business process modeling plays an important role for understanding, documenting and also redesigning operations. However, often the validation and the usage of process models is hampered by the fact that many domain experts or not able to understand the models in detail. In order to overcome this problem, we propose an approach for automatically generating description texts from Petri Nets. Our technique combines different techniques from linguistics and graph decomposition in order to generate accurate natural language texts. Further, we support our technique with insights from the multimedia learning theory which explicitly recommends the addressing of different channels such as the auditory and the visual channel. As a proof of concept we implemented our technique in the open source modeling tool WoPeD.

Part 2: Regular Papers; International audience; The increasing number of process models in an organization has led to the development of process model repositories, which allow to efficiently and effectively manage these large number of models. Searching process models is an inherent feature of such process repositories. However, the effectiveness of searching depends upon the accuracy of the underlying matching technique that is used to compute the degree of similarity between query-source process model pairs. Most of the existing matching techniques rely on the use of labels, structure or execution behavior of process models. The effectiveness of these techniques is, however, quiet low and far from being usable in practice. In this paper, we address this problem and propose the use of a combination of textual descriptions of process models and text matching techniques for process matching. The proposed approach is evaluated using the established metrics, precision, recall and F1 score. The results show that the use of textual descriptions is slightly more effective than activity labels.

Many organizations use business process models for documenting their business opera- tions. In recent years, the Business Process Model and Notation (BPMN) evolved into the leading standard for process modeling. However, BPMN is complex: the specification offers a huge variety of different elements and often several representational choices for the same semantics. This raises the question of how well modelers can deal with these choices. Empirical insights into BPMN us- age from the perspective of practitioners are still missing. We close this gap by analyzing a large set of BPMN 2.0 process models from practice. We found that particularly representational choices for splits and joins, the correct use of message flow, the proper decomposition of models, and the consistent labeling appear to be connected with quality issues. Based on our findings we give five recommendations how these issues can be avoided in the future. The work summarized in this ex- tended abstract has been published in [LMG16].

Textual process descriptions are widely used in organizations since they can be created and understood by virtually everyone. The inherent ambiguity of natural language, however, impedes the automated analysis of textual process descriptions. While human readers can use their context knowledge to correctly understand statements with multiple possible interpretations, automated analysis techniques currently have to make assumptions about the correct meaning. As a result, automated analysis techniques are prone to draw incorrect conclusions about the correct execution of a process. To overcome this issue, we introduce the concept of a behavioral space as a means to deal with behavioral ambiguity in textual process descriptions. A behavioral space captures all possible interpretations of a textual process description in a systematic manner. Thus, it avoids the problem of focusing on a single interpretation. We use a compliance checking scenario and a quantitative evaluation with a set of 47 textual process descriptions to demonstrate the usefulness of a behavioral space for reasoning about a process described by a text. Our evaluation demonstrates that a behavioral space strikes a balance between ignoring ambiguous statements and imposing fixed interpretations on them.

Many techniques for the advanced analysis of process models build on the annotation of process models with elements from predefined vocabularies such as taxonomies. However, the manual annotation of process models is cumbersome and sometimes even hardly manageable taking the size of taxonomies into account. In this paper, we present the first approach for automatically annotating process models with the concepts of a taxonomy. Our approach builds on the corpus-based method of second-order similarity, different similarity functions, and a Markov Logic formalization. An evaluation with a set of 12 process models consisting of 148 activities and the PCF taxonomy consisting of 1,131 concepts demonstrates that our approach produces satisfying results.

Process model matching refers to the automatic identification of corresponding activities between two process models. It represents the basis for many advanced process model analysis techniques such as the identification of similar process parts or process model search. A central problem is how to evaluate the performance of process model matching techniques. Often, not even humans can agree on a set of correct correspondences. Current evaluation methods, however, require a binary gold standard, which clearly defines which correspondences are correct. The disadvantage of this evaluation method is that it does not take the true complexity of the matching problem into account and does not fairly assess the capabilities of a matching technique. In this paper, we propose a novel evaluation method for process model matching techniques. In particular, we build on the assessment of multiple annotators to define probabilistic notions of precision and recall. We use the dataset and the results of the Process Model Matching Contest 2015 to assess and compare our evaluation method. We find that our probabilistic evaluation method assigns different ranks to the matching techniques from the contest and allows to gain more detailed insights into their performance.

Large corporations increasingly utilize business process models for documenting and redesigning their operations. The extent of such modeling initiatives with several hundred models and dozens of often hardly trained modelers calls for automated quality assurance. While formal properties of control flow can easily be checked by existing tools, there is a notable gap for checking the quality of the textual content of models, in particular, its activity labels. In this paper, we address the problem of activity label quality in business process models. We designed a technique for the recognition of labeling styles, and the automatic refactoring of labels with quality issues. More specifically, we developed a parsing algorithm that is able to deal with the shortness of activity labels, which integrates natural language tools like WordNet and the Stanford Parser. Using three business process model collections from practice with differing labeling style distributions, we demonstrate the applicability of our technique. In comparison to a straightforward application of standard natural language tools, our technique provides much more stable results. As an outcome, the technique shifts the boundary of process model quality issues that can be checked automatically from syntactic to semantic aspects.

Business process management (BPM) is a widely adopted approach for identifying, documenting, and improving business processes. One of the main goals of BPM is to assure that the business processes of an organization constantly produce the desired outcome. One considerable challenge in this context is that not every process can be fully anticipated in advance. Particularly so-called knowledge intensive processes are characterized by a high level of complexity, reduced repeatability, and the occurrence of unexpected events. Many authors argue that knowledge intensive processes may benefit from informal work practices as they exhibit more potential for improvements. Non knowledge intensive processes, on the other hand, are typically considered to be less frequently affected by negative deviations from management intended structures. In this paper, we conduct a positivist case study to challenge these viewpoints from literature. In particular, we empirically investigate the effect of informal work practices on knowledge intensive as well as non knowledge intensive business processes in a German IT company that offers one of Europe's leading online project platforms. Our results show that existing view points are too general and that a more balanced discussion of knowledge intensity is needed.

It is widely accepted that presenting data in the form of pictures or models can enhance comprehension, decision making and communication of the underlying information. However, there are few systematic studies that examine whether graphical models are more effective than other representation (such as textual descriptions). Process models provide an abstract graphical view of organizational procedures by reducing the complex reality of the work performed by a company to its most important activities. Such models are useful to train new employees, to document and allow improving organizational procedures and policies. This paper describes an experiment to address if there are significant differences in terms of process understand ability depending on whether textual work instructions or process models are used to represent a business process. We compared a control group of subjects that received textual work instructions to a second group, which received process models, in terms of their ability to understand the process. We found empirical support that using textual work instructions or process models do not influence process understand ability for non-expert users but do influence for experienced users.

Business process modeling has become an integral part of many organizations for documenting and redesigning complex organizational operations. However, the increasing size of process model repositories calls for automated quality assurance techniques. While many aspects such as formal and structural problems are well understood, there is only a limited understanding of semantic issues caused by natural language. One particularly severe problem arises when modelers employ natural language for expressing control-flow constructs such as gateways or loops. This may not only negatively affect the understandability of process models, but also the performance of analysis tools, which typically assume that process model elements do not encode control-flow related information in natural language. In this paper, we aim at increasing the current understanding of mixing natural and modeling language and therefore exploratively investigate three process model collections from practice. As a result, we identify a set of nine anti patterns for mixing natural and modeling language.

Process maps provide a holistic view of all processes of an organization and the essential relationships between them. The design of a process map is of central importance as many organizations create them at the start of a business process management (BPM) initiative to serve as a framework. Despite this importance, the design of process maps is still more art than science, essentially because there is no standardized modeling language available for process map design. In this paper, we address the research question of which concepts are currently used in process maps in practice. To this end, we investigate a collection of 67 process maps from industry. Our contribution is a meta-model for process map design which is grounded in actual usage. Furthermore, we discuss the importance of different concepts for process map design.

The design and development of process-aware information systems is often supported by specifying requirements as business process models. Although this approach is generally accepted as an effective strategy, it remains a fundamental challenge to adequately validate these models given the diverging skill set of domain experts and system analysts. As domain experts often do not feel confident in judging the correctness and completeness of process models that system analysts create, the validation often has to regress to a discourse using natural language. In order to support such a discourse appropriately, so-called verbalization techniques have been defined for different types of conceptual models. However, there is currently no sophisticated technique available that is capable of generating natural-looking text from process models. In this paper, we address this research gap and propose a technique for generating natural language texts from business process models. A comparison with manually created process descriptions demonstrates that the generated texts are superior in terms of completeness, structure, and linguistic complexity. An evaluation with users further demonstrates that the texts are very understandable and effectively allow the reader to infer the process model semantics. Hence, the generated texts represent a useful input for process model validation.

Many use cases in business process management rely on the identification of correspondences between process models. However, the sparse information in process models makes matching a fundamentally hard problem. Consequently, existing approaches yield a matching quality which is too low to be useful in practice. Therefore, we investigate incorporating user feedback to improve matching quality. To this end, we examine which information is suitable for feedback analysis. On this basis, we design an approach that performs matching in an iterative, mixed-initiative approach: we determine correspondences between two models automatically, let the user correct them, and analyze this input to adapt the matching algorithm. Then, we continue with matching the next two models, and so forth. This approach improves the matching quality, as showcased by a comparative evaluation. From this study, we also derive strategies on how to maximize the quality while limiting the additional effort required from the user.

The increased adoption of business process management approaches, tools, and practices has led organizations to accumulate large collections of business process models. These collections can easily include from a hundred to a thousand models, especially in the context of multinational corporations or as a result of organizational mergers and acquisitions. A concrete problem is thus how to maintain these large repositories in such a way that their complexity does not hamper their practical usefulness as a means to describe and communicate business operations. This paper proposes a technique to automatically infer suitable names for business process models and fragments thereof. This technique is useful for model abstraction scenarios, as for instance when user-specific views of a repository are required, or as part of a refactoring initiative aimed to simplify the repository's complexity. The technique is grounded in an adaptation of the theory of meaning to the realm of business process models. We implemented the technique in a prototype tool and conducted an extensive evaluation using three process model collections from practice and a case study involving process modelers with different experience. Keywords: Business process model; Model name; Process model repository; Refactoring; Model abstraction

Thinking in business processes and using process models for their documentation has become common practice in companies. In many cases this documentation encompasses more than thousands of models. One of the key challenges is achieving consistency of the process model terminology. Especially, the usage of synonym and homonym words is one of the most severe problems for terminological consistency. Therefore, this paper presents an automatic approach to identify synonym and homonym words in model repositories. We challenged the approach against three model collection from practice that are assumed to have different levels of terminological consistency. The evaluation shows that the approach is capable to fulfill these goals and to identify meaningful synonym and homonym candidates for follow-up resolution.

The aim of this book is to provide the means for exploiting and analyzing the linguistic information that is captured by process models. This chapter represents the first step in this direction by introducing a technique for automatically parsing and annotating process models. The technique can deal with the specific challenges of process model element labels and uses insights about grammatical structures and a tailored disambiguation algorithm to yield accurate results.

Nowadays, many enterprises use business process models for documenting and supporting their operations. As many enterprises have branches in several countries and provide similar services throughout the globe, there is high potential for re-using these process models. However, the language barrier is a major obstacle for the successful re-use of process models, especially in multi-national companies. In this paper, we address this problem by presenting the Business Process Model Translator (BPMT), a technique for the automated translation of business process models that eases the re-use of business process models and reduces redundant work in multi-national companies. It builds upon the state-of-the-art machine translation system Moses and extends it with word and translation disambiguation considering the context of the domain. As a result, the BPMT can successfully deal with the compact and special language fragments that are typically found in business process models. A two-fold evaluation with the BLEU metric and an expert survey showed improvements of our approach over Moses.

Process models are widely used for documenting and redesigning the operations of companies. The audience of these models ranges from well-trained system analysts and developers to casual staff members who are unexperienced in terms of modeling. Hence, most of the latter lack the ability to understand process model in detail. This is a particular problem as this impedes the usage of process models to the desired extent. In this chapter, we investigate in how far the concept of natural language generation can be adapted to process models in order to provide all staff members with a understandable process description. To this end, we define an approach which automatically transforms BPMN process models into understandable natural language texts.

Business process management (BPM) represents one of the core concepts enabling companies to flexibly react to the constantly changing business environment. The actual relevance of business process management is, for instance, illustrated by the size of the BPM software market. A recent study of Global Industry Analysts forecasts that the global market for BPM software will reach a volume of over 5 billion US dollars by the year 2017 [18]. The importance of BPM in academia is demonstrated by its constant presence among top-ranked information system conferences [32, 2, 1, 271]. In fact, this also highlights that BPM has become one of the core areas of information systems research. The range of addressed topics goes from general organizational aspects of BPM to specific technical issues concerning business process models. Due to the importance of business process models for documenting and redesigning the operations of companies, many researchers have focused on aspects of process model design and process model quality. Nevertheless, there are still many significant aspects that have not been addressed by prior research.

Process model matching refers to the task of creating correspondences among activities of different process models. This task is crucial whenever comparison and alignment of process models are called for. In recent years, there have been a few attempts to tackle process model matching. Yet, evaluating the obtained sets of correspondences reveals high variability in the results. Addressing this issue, we propose a method for predicting the quality of results derived by process model matchers. As such, prediction serves as a case-by-case decision making tool in estimating the amount of trust one should put into automatic matching. This paper proposes a model of prediction for process matching based on both process properties and preliminary match results.

Comparing process models and matching similar activities has recently emerged as a research area of business process management. However, the problem is fundamentally hard when considering realistic scenarios: e.g., there is a huge variety of terms and various options for the grammatical structure of activity labels exist. While prior research has established important conceptual foundations, recall values have been fairly low (around 0.26) --- arguably too low to be useful in practice. In this paper, we present techniques for activity label matching which improve current results (recall of 0.44, without sacrificing precision). Furthermore, we identify categories of matching challenges to guide future research.

Many companies use business process modeling to support various improvements initiatives leading to an increasing number of process models. Typically, these models are stored in a collection containing several hundreds of process models. In many cases, process models are overlapping, although parts could be easily reused saving costs and efforts. Different labeling styles and evolving process models complicate the detection of reusable model parts. In this paper, we propose a novel approach for the detection of equivalent and similar process model parts that exploits semantic comparison of activity labels and behavioral comparison of control flow. We evaluate our approach on the SAP Reference Model, a collection with 604 process models. The evaluation reveals insights for the thresholds of semantic and behavioral similarity of process models as well as their influence for similar process part detection. Hence, we identify five candidate groups with specific similarity properties that contain reoccurring process parts.

Process Modeling is a widely used concept for understanding, documenting and also redesigning the operations of organizations. The validation and usage of process models is however affected by the fact that only business analysts fully understand them in detail. This is in particular a problem because they are typically not domain experts. In this paper, we investigate in how far the concept of verbalization can be adapted from object-role modeling to process models. To this end, we define an approach which automatically transforms BPMN process models into natural language texts and combines different techniques from linguistics and graph decomposition in a flexible and accurate manner. The evaluation of the technique is based on a prototypical implementation and involves a test set of 53 BPMN process models showing that natural language texts can be generated in a reliable fashion.

Although several approaches for service identification have been defined in research and practice, there is a notable lack of automatic analysis techniques. In this paper we take the integrated approach by Kohlborn et al. as a starting point, and combine different analysis techniques in a novel way. Our contribution is an automated approach for the identification and detailing of service candidates. Its output is meant to provide a transparent basis for making decisions about which services to implement with which priority. The approach has been implemented and evaluated for an industry collection of process models.

Recently many companies have expanded their business process modeling projects such that thousands of process models are designed and maintained. Activity labels of these models are related to different styles according to their grammatical structure. There are several guidelines that suggest using a verb-object labeling style. Meanwhile, real-world process models often include labels that do not follow this style. In this paper we investigate the potential to improve the label quality automatically. We define and implement an approach for automatic refactoring of labels following action-noun style into verb-object labels. We evaluate the proposed techniques using a collection of real-world process models--the SAP Reference Model.

Blockchain technology promises a sizable potential for executing inter-organizational business processes without requiring a central party serving as a single point of trust (and failure). This paper analyzes its impact on business process management (BPM). We structure the discussion using two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle. This paper provides research directions for investigating the application of blockchain technology to BPM., Comment: Preprint for ACM TMIS