Prof. Dr. Henrik Leopold

Assistant Professor for Data Science and Business Intelligence

Publications

Journal Articles (Peer-Reviewed)

DOI: 10.1007/s10796-017-9823-6 

Abstract: Understanding conceptual models of business domains is a key skill for practitioners tasked with systems analysis and design. Research in this field predominantly uses experiments with specific user proxy cohorts to examine factors that explain how well different types of conceptual models can be comprehended by model viewers. However, the results from these studies are difficult to compare. One key difficulty rests in the unsystematic and fluctuating consideration of model viewer characteristics (MVCs) to date. In this paper, we review MVCs used in prominent prior studies on conceptual model comprehension. We then design an empirical review of the influence of MVCS through a global, cross-sectional experimental study in which over 500 student and practitioner users were asked to answer comprehension questions about a prominent type of conceptual model - BPMN process models. As an experimental treatment, we used good versus bad layout in order to increase the variance of performance. Our results show MVC to be a multi-dimensional construct. Moreover, process model comprehension is related in different ways to different traits of the MVC construct. Based on these findings, we offer guidance for experimental designs in this area of research and provide implications for the study of MVCs.

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Open reference in new window "An Empirical Review of the Connection Between Model Viewer Characteristics and the Comprehension of Conceptual Process Models"

DOI: 10.1016/j.is.2018.01.007 

Abstract: 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.

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Open reference in new window "Checking process compliance against natural language specifications using behavioral spaces"

DOI: 10.1016/j.datak.2018.04.008 

Abstract: 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. Current evaluation methods require a binary gold standard that clearly defines which correspondences are correct. The problem is that often not even humans can agree on a set of correct correspondences. Hence, evaluating the performance of matching techniques based on a binary gold standard 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 procedure for process model matching techniques. In particular, we build on the assessments of multiple annotators to define the notion of a non-binary gold standard. In this way, we avoid the problem of agreeing on a single set of correct correspondences. Based on this non-binary gold standard, we introduce probabilistic versions of precision, recall, and F-measure as well as a distance-based performance measure. We use a dataset from the Process Model Matching Contest 2015 and a total of 16 matching systems to assess and compare the insights that can be obtained by using our evaluation procedure. We find that our probabilistic evaluation procedure allows us to gain more detailed insights into the performance of matching systems than a traditional evaluation based on a binary gold standard.

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Open reference in new window "A probabilistic evaluation procedure for process model matching techniques"

DOI: 10.1016/j.datak.2017.03.010 

Abstract: 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.

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Open reference in new window "Ensuring the canonicity of process models"

DOI: 10.1016/j.is.2016.07.010 

Abstract: 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.

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Open reference in new window "Comparing textual descriptions to process models – The automatic detection of inconsistencies"

DOI: 10.1016/j.is.2017.06.005 

Abstract: 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.

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Open reference in new window "Transforming unstructured natural language descriptions into measurable process performance indicators using Hidden Markov Models"

DOI: 10.1016/j.dss.2017.02.013 

Abstract: 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.

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Open reference in new window "Overcoming individual process model matcher weaknesses using ensemble matching"

DOI: 10.1109/MS.2015.81 

Abstract: Many organizations use business process models to document business operations and formalize business requirements in software-engineering projects. The Business Process Model and Notation (BPMN), a specification by the Object Management Group, has evolved into the leading standard for process modeling. One challenge is BPMN's complexity: it offers a huge variety of 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 use from the practitioners' perspective are still missing. To close this gap, researchers analyzed 585 BPMN 2.0 process models from six companies. They found that split and join representations, message flow, the lack of proper model decomposition, and labeling related to quality issues. They give five specific recommendations on how to avoid these issues.

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Open reference in new window "Learning from Quality Issues of BPMN Models from Industry"

DOI: 10.1016/j.jss.2015.06.007 

Abstract: 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.

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Open reference in new window "Automatic service derivation from business process model repositories via semantic technology"

DOI: 10.1109/TSE.2015.2396895 

Abstract: 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.

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Open reference in new window "Automatic Detection and Resolution of Lexical Ambiguity in Process Models"

DOI: 10.1109/TSE.2014.2327044 

Abstract: 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.

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Open reference in new window "Supporting Process Model Validation through Natural Language Generation"

DOI: 10.1016/j.is.2013.06.007 

Abstract: 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.

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Open reference in new window "Simplifying process model abstraction: Techniques for generating model names"

DOI: 10.1016/j.dss.2013.06.014 

Abstract: 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.

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Open reference in new window "Detection of naming convention violations in process models for different languages"

DOI: 10.1016/j.is.2012.01.004 

Abstract: 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.

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Open reference in new window "On the refactoring of activity labels in business process models"

DOI: 10.18417/emisa.6.1.2 

Abstract: Quality assurance is a serious issue for large-scale process modelling initiatives. While formal control flow analysis has been extensively studied in prior research, there is little work on how the textual content of a process model and its activity labels can be systematically analysed. In this context, it is a major challenge to systematically identify and to consequently assure high label quality. As many large process model collections contain more than thousand models, each including several activity labels, there is a strong need for an automatic detection of labels that might be of bad quality. Recent research has shown that different grammatical styles correlate with potential ambiguity of a label. In this paper, we propose an algorithm for recognition of activity labeling styles. The developed algorithm exploits natural language processing techniques, e.g., part of speech tagging and analysis of the grammatical structure. We also study how ontologies, like WordNet, can support the solution. We conduct a thorough evaluation of the developed techniques utilising about 6,000 activity labels from the SAP Reference Model. The evaluation of this algorithm shows that spurious labels can be identified with a significant level of precision and recall. In this way, our approach can be used as a means of quality assurance for process repository management by listing bad quality labels, which a human modeler should correct.

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Open reference in new window "Recognising Activity Labeling Styles in Business Process Models"

Journal Articles (Professional)

DOI: 10.1016/j.infsof.2017.08.009 

Abstract: 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.

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Open reference in new window "A semi-automated approach for generating natural language requirements documents based on business process models"

Abstract: Process modeling has become an essential part of many organizations for documenting, analyzing and redesigning their business operations and to support them with suitable information systems. In order to serve this purpose, it is important for process models to be well grounded in formal and precise semantics. While behavioural semantics of process models are well understood, there is a considerable gap of research into the semantic aspects of their text labels and natural language descriptions. The aim of this paper is to make this research gap more transparent. To this end, we clarify the role of textual content in process models and the challenges that are associated with the interpretation, analysis, and improvement of their natural language parts. More specifically, we discuss particular use cases of semantic process modeling to identify 25 challenges. For each challenge, we identify prior research and discuss directions for addressing them.

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Open reference in new window "25 Challenges of Semantic Process Modeling"

Books

Conference Proceedings

Abstract: The ongoing digitalization changes the nature of work. Nowadays, even complex tasks can be auto-mated and reliably performed by machines. This new wave of automation has led to an increased interest in predicting the effects of automation on job design. A recent study suggests that around half of today’s jobs could disappear in the coming twenty years. However, these results are heavily debated. Other studies claim that the effect of automation will be much less dramatic. A fundamental issue underlying all these studies is the question of how to categorize tasks. Some authors simply divide tasks into routine and non-routine tasks, others also consider which kind of cognitive abilities are required. Since the predicted effect of automation directly relates to the categories considered, a sound task framework is essential for useful predictions. Recognizing that existing task models are limited in terms of granularity and time, we use a literature study, interviews, and an analysis of historical data to systemically develop a new task framework for predicting the effects of automation. We conduct an evaluation of our framework to demonstrate the generalizability of the framework and compare the framework with existing models.

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Open reference in new window "A Task Framework for Predicting the Effects of Automation"

DOI: 10.1007/978-3-319-91704-7_5 

Abstract: 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.

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Open reference in new window "Identifying Candidate Tasks for Robotic Process Automation in Textual Process Descriptions"

Abstract: The Business Process Management (BPM) field focuses in the coordination of labor so that or-ganizational processes are smoothly executed in a way that products and services are properlydelivered. At the same time, NLP has reached a maturity level that enables its widespread ap-plication in many contexts, thanks to publicly available frameworks. In this position paper, weshow how NLP has potential in raising the benefits of BPM practices at different levels. In-stead of being exhaustive, we show selected key challenges were a successful application of NLPtechniques would facilitate the automation of particular tasks that nowadays require a significanteffort to accomplish. Finally, we report on applications that consider both the process perspectiveand its enhancement through NLP.

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Open reference in new window "Challenges and opportunities of applying natural language processing in business process management"

DOI: 10.1007/978-3-319-59536-8_6 

Abstract: A crucial requirement for compliance checking techniques is that observed behavior, captured in event traces, can be mapped to the process models that specify allowed behavior. Without a mapping, it is not possible to determine if observed behavior is compliant 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 compliance of a trace to a specification, this uncertainty represents a major issue for compliance checking. To overcome this issue, we introduce a probabilistic compliance checking method that can deal with uncertain mappings. Our method avoids the need to select a single mapping, but rather works on a spectrum of possible mappings. A quantitative evaluation demonstrates that our method can be applied on a considerable number of real-world processes where traditional compliance checking methods fail.

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Open reference in new window "Checking process compliance on the basis of uncertain event-to-activity mappings"

Abstract: 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.

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Open reference in new window "Towards a science of checklists"

DOI: 10.1109/BigData.2017.8258052 

Abstract: 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.

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Open reference in new window "Predicting treatment repetitions in the implant denture therapy process"

DOI: 10.1007/978-3-319-59336-4_13 

Abstract: Process model matching techniques aim at automatically identifying activity correspondences between two process models that represent the same or similar behavior. By doing so, they provide essential input for many advanced process model analysis techniques such as process model search. Despite their importance, the performance of process model matching techniques is not yet convincing and several attempts to improve the performance have not been successful. This raises the question of whether it is really not possible to further improve the performance of process model matching techniques. In this paper, we aim to answer this question by conducting two consecutive analyses. First, we review existing process model matching techniques and give an overview of the specific technologies they use to identify similar activities. Second, we analyze the correspondences of the Process Model Matching Contest 2015 and reflect on the suitability of the identified technologies to identify the missing correspondences. As a result of these analyses, we present a list of three specific recommendations to improve the performance of process model matching techniques in the future.

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Open reference in new window "How to make process model matching work better? An analysis of current similarity measures"

DOI: 10.1007/978-3-319-69462-7_19 

Abstract: 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.

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Open reference in new window "Ranking-based evaluation of process model matching"

DOI: 10.1007/978-3-319-59536-8_18 

Abstract: Process model matching provides the basis for many process analysis techniques such as inconsistency detection and process querying. The matching task refers to the automatic identification of correspondences between activities in two process models. Numerous techniques have been developed for this purpose, all share a focus on process-level information. In this paper we introduce instance-based process matching, which specifically focuses on information related to instances of a process. In particular, we introduce six similarity metrics that each use a different type of instance information stored in the event logs associated with processes. The proposed metrics can be used as standalone matching techniques or to complement existing process model matching techniques. A quantitative evaluation on real-world data demonstrates that the use of information from event logs is essential in identifying a considerable amount of correspondences.

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Open reference in new window "Instance-based process matching using event-log information"

Abstract: Having access to the right information is vital to the effective and efficient execution of an organization’s business processes. A major challenge in this regard is that information on a single process is often spread out over numerous models, documents, and systems. Despite the potential consequences of this situation, there is a lack of insights on how to mitigate its impact. Against this background, we conducted an explorative case study to analyze the causes and consequences of the fragmentation of process information. We found that the widespread fragmentation of information had a considerable impact on the investigated organization. In particular, fragmentation led to severe maintenance issues, reduced process execution efficiency, and had a negative effect on the quality of process results. Our findings provide useful insights for both practice and research on how to mitigate the negative aspects associated with the fragmentation of process information.

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Open reference in new window "Causes and consequences of fragmented process information: Insights from a case study"

DOI: 10.1007/978-3-319-39696-5_33 

Abstract: To determine whether strategic goals are met, organizations must monitor how their business processes perform. Process Performance Indicators (PPIs) are used to specify relevant performance requirements. The formulation of PPIs is typically a managerial concern. Therefore, considerable effort has to be invested to relate PPIs, described by management, to the exact operational and technical characteristics of business processes. This work presents an approach to support this task, which would otherwise be a laborious and time-consuming endeavor. The presented approach can automatically establish links between PPIs, as formulated in natural language, with operational details, as described in process models. To do so, we employ machine learning and natural language processing techniques. A quantitative evaluation on the basis of a collection of 173 real-world PPIs demonstrates that the proposed approach works well.

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Open reference in new window "Narrowing the business-IT gap in process performance measurement"

DOI: 10.1007/978-3-319-45348-4_16 

Abstract: 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.

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Open reference in new window "Dealing with behavioral ambiguity in textual process descriptions"

DOI: 10.1007/978-3-319-39429-9_4 

Abstract: 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 process 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.

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Open reference in new window "Integrating textual and model-based process descriptions for comprehensive process search"

DOI: 10.1007/978-3-319-46397-1_22 

Abstract: 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.

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Open reference in new window "Probabilistic evaluation of process model matching techniques"

Abstract: Many organizations face noncompliance in their business processes. Such noncompliant behavior can range from well-intended actions to the deliberate omission of essential tasks. The current view on noncompliance is mostly negative and many researchers discuss how to avoid it altogether. A gap in the research is a lack of empirical insights on when noncompliance has positive and when it has negative effects. Against this background, we conduct a qualitative study in the customer service department of a company hosting one of Europe’s leading online project platforms. Differing from previous studies on business process noncompliance, the starting point of our study is direct observations of how employees conduct their work. We found that noncompliant behavior with a positive intention had a mostly positive effect on business process outcomes. Unintended factors of noncompliance, such as a lack of knowledge or carelessness, caused the most severe negative impact on business process outcomes.

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Open reference in new window "Factors leading to business process noncompliance and its positive and negative effects: Empirical insights from a case study"

DOI: 10.1007/978-3-319-19069-3_25 

Abstract: 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.

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Open reference in new window "Towards the automated annotation of process models"

DOI: 10.1109/HICSS.2015.591 

Abstract: 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.

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Open reference in new window "How much flexibility is good for knowledge intensive business processes: A study of the effects of informal work practices"

DOI: 10.1007/978-3-319-19237-6_1 

Abstract: An organization’s knowledge on its business processes represents valuable corporate knowledge because it can be used to enhance the performance of these processes. In many organizations, documentation of process knowledge is scattered around various process information sources. Such information fragmentation poses considerable problems if, for example, stakeholders wish to develop a comprehensive understanding of their operations. The existence of efficient techniques to combine and integrate process information from different sources can therefore provide much value to an organization. In this work, we identify the general challenges that must be overcome to develop such techniques. This paper illustrates how these challenges should be and, to some extent, are being met in research. Based on these insights, we present three main frontiers that must be further expanded to successfully counter the fragmentation of process information in organizations.

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Open reference in new window "On the fragmentation of process information: Challenges, solutions, and outlook"

DOI: 10.1007/978-3-319-23063-4_6 

Abstract: Text-based and model-based process descriptions have their own particular strengths and, as such, appeal to different stakeholders. For this reason, it is not unusual to find within an organization descriptions of the same business processes in both modes. When considering that hundreds of such descriptions may be in use in a particular organization by dozens of people, using a variety of editors, there is a clear risk that such models become misaligned. To reduce the time and effort needed to repair such situations, this paper presents the first approach to automatically identify inconsistencies between a process model and a corresponding textual description. Our approach leverages natural language processing techniques to identify cases where the two process representations describe activities in different orders, as well as model activities that are missing from the textual description. A quantitative evaluation with 46 real-life model-text pairs demonstrates that our approach allows users to quickly and effectively identify those descriptions in a process repository that are inconsistent.

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Open reference in new window "Detecting inconsistencies between process models and textual descriptions"

Abstract: Nowadays business process management (BPM) is integral part of many organizations in the private sector. Considering the implementation and maturity of BPM in public authorities, this does not hold true to the same degree. In particular, the willingness to share knowledge about business processes is very limited. This represents a severe problem since authorities have huge overlaps with regard to the services they provide. Hence, the exchange of process knowledge could efficiently support authorities with lower maturity in identifying optimization opportunities. This research paper investigates the circumstances as well as drivers and inhibitors of process knowledge sharing in public organizations. We conduct 15 interviews and use the Grounded Theory method in order to derive a conceptual framework that provides important insights into how process knowledge sharing can be improved in public organizations.

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Open reference in new window "Barriers and strategies of process knowledge sharing in public sector organizations"

DOI: 10.1007/978-3-642-41641-5_14 

Abstract: The increasing adoption of process-aware information systems (PAISs) has resulted in large process model collections. To support users having different perspectives on complex processes and related data, a PAIS should enable personalized process views, i.e., user-specific abstractions of process models. Despite the abstraction achieved through views of the graphical process models, many end users still struggle with understanding these graphical models and their details. For selected user groups, therefore, a PAIS should provide verbalized process descriptions describing their role in the process. Existing PAISs neither provide mechanisms for managing process views nor verbalized process descriptions. While process views have been used as visual abstractions for large process models, so far no work exists on how to provide both personalized and verbalized process descriptions based on respective views. This paper presents an approach for creating such personalized and verbalized process descriptions based on process views. Furthermore, textual changes of a personalized and verbalized process description are correctly mapped to corresponding updates of the underlying process model. In this context, all other views and process descriptions related to this process model are migrated to the new version of the process model as well. Overall, our approach enables end users to understand and evolve large process models based on personalized and verbalized process descriptions.

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Open reference in new window "Creating and updating personalized and verbalized business process descriptions"

DOI: 10.1007/978-3-642-38484-4_21 

Abstract: 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.

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Open reference in new window "Spotting terminology deficiencies in process model repositories"

Abstract: Large-scale enterprises struggle with an effective alignment of business processes and IT services with business strategy. While process models play an important role for bridging between strategy and IT, there is a need to systematically organize the huge number of models. Process architecture defines an overarching structure for the organization of processes. However, there is a notable research gap on how process architectures are designed in practice. In this paper we address this problem by integrating insights and approaches from practice. We use Grounded Theory to analyze eleven in-depth interviews we conducted. Further, we present findings from studying documents provided by the interviewees. Our contribution is a conceptual framework about process architecture design, along with a classification of process architecture archetypes found in practice. Our results have strong implications since they demonstrate that process architecture design is more complex and context-dependent than assumed.

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Open reference in new window "An empirical investigation on the design of process architectures"

DOI: 10.1007/978-3-642-30359-3_8 

Abstract: 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.

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Open reference in new window "Automatic derivation of service candidates from business process model repositories"

DOI: 10.1007/978-3-642-31095-9_5 

Abstract: 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.

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Open reference in new window "Generating natural language texts from business process models"

DOI: 10.1007/978-3-642-32885-5_25 

Abstract: Business process models are increasingly used by companies, often yielding repositories of several thousand models. These models are of great value for business analysis such as service identification or process standardization. A problem is though that many of these analyses require the pairwise comparison of process models, which is hardly feasible to do manually given an extensive number of models. While the computation of similarity between a pair of process models has been intensively studied in recent years, there is a notable gap on automatically matching activities of two process models. In this paper, we develop an approach based on semantic techniques and probabilistic optimization. We evaluate our approach using a sample of admission processes from different universities.

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Open reference in new window "Probabilistic optimization of semantic process model matching"

DOI: 10.1007/978-3-642-21640-4_38 

Abstract: Process models are essential tools for managing, understanding and changing business processes. Yet, from a user perspective they can quickly become too complex to deal with. Abstraction – aggregating detailed fragments into more coarse-grained ones – has proven to be a valuable technique to simplify the view on a process model. Various techniques that automate the decision of which model fragments to aggregate have been defined and validated by recent research, but their application is hampered by the lack of abilities to generate meaningful names for such aggregated parts. In this paper, we address this problem by investigating naming strategies for individual model fragments and process models as a whole. Our contribution is an automatic naming approach that builds on the linguistic analysis of process models from industry.

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Open reference in new window "On the automatic labeling of process models"

DOI: 10.1007/978-3-642-13881-2_28 

Abstract: 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.

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Open reference in new window "Refactoring of process model activity labels"

DOI: 10.1007/978-3-319-10172-9_6 

Abstract: 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.

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Open reference in new window "Listen to me: Improving process model matching through user feedback"

Abstract: 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.

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Open reference in new window "Listen to Me: Improving Process Model Matching through User Feedback"

Book Chapters

DOI: 10.1007/978-3-642-40176-3_16 

Abstract: This book constitutes the proceedings of the 11th International Conference on Business Process Management, BPM 2013, held in Beijing, China, in August 2013. The 17 regular papers and 8 short papers included in this volume were carefully reviewed and selected from 118 submissions. The papers are organized in 7 topical sections named: process mining; conformance checking; process data; process model matching; process architectures and collaboration; as well as alternative perspectives, and industry paper.

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Open reference in new window "Predicting the quality of process model matching"

DOI: 10.1007/978-3-642-36926-1_34 

Abstract: Process model similarity has developed into a prolific field of investigation. This paper summarizes the research after the CAISE 2008 paper on this topic. We identify categories of problems and provide an outlook on future directions.

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Open reference in new window "A Short Survey on Process Model Similarity"