Business Process Management Workshops

Process support for knowledge work is far from being mastered in existing information systems. Predominant workflow management solutions are too rigid and provide no means to deal with unpredictable situations. Various case management approaches have been proposed to support this flexibility for unstructured processes. Recently the Object Management Group published the Case Management Model and Notation (CMMN) as a standard notation for case management. In this paper we compare prominent definitions of case management over the last twenty-three years against characteristics of knowledge-intensive processes (KiPs). Our goal is to evaluate the applicability of case management and CMMN for KiPs. We provide requirements for execution environments implementing CMMN and delineate existing case management approaches to advance the understanding of this important domain. We concluded that CMMN seems to be a suitable approach to KiPs when combined with an appropriate execution environment.

The spectrum of business processes can be divided into two types: well-structured routine processes and agile processes with control flow that evolves at run time. In a similar way, two different representational paradigms can be distinguished: procedural models and declarative models which define rules that a process has to satisfy. Agile processes can often be captured more easily using a declarative approach. While in procedural languages the organisational perspective can be modelled adequatly, in declarative languages, however, an adequate representation of organisational patterns is often still not possible. Agile processes, however, need to explicitly integrate organisational coherencies due to the importance of human decision-making. This paper presents a review of declarative modeling languages, outlines missing aspects and suggests research roadmaps for the future.

Conventional business process management has been very successful for routine work but has deficiencies in dealing with the flexibility of knowledge workers’ work, since the tasks are hard to determine and highly dependent on the current situation. For knowledge workers it is useful to structure the processes just in part as process variants, which can be adapted, modified and even newly created at runtime by them. This paper describes an application of a case-based reasoning approach and introduces a process variant modelling language that supports the manual generation and refinement of generalized process variants. This approach is demonstrated in a public administration scenario.

Enabling flexibility in unpredictable situations with ad hoc actions decided at runtime by knowledge workers is the main focus of Adaptive Case Management (ACM) systems. However, ad hoc actions added during case execution and ACM templates prepared at design time need to be within the boundaries defined by business constraints, company regulations and legal systems. In this paper we report our experience in addressing this challenge by using model checking and runtime monitoring techniques for behavioral consistency checking that can handle both ACM aspects: support by means of predefined process templates and high flexibility by allowing ad hoc actions at runtime. Our study is conducted using a practical ACM system for repair service management handling different customer requirements under diverse compliance and law regulations.

Process manager plays the central role in crisis management. In order to capture the intentions of the process manager, the process should be specified at a strategic level. In order to analyze how these intentions are fulfilled during the process execution, the process should be specified at an operational level. Whereas the variety of techniques for goal modeling and process modeling is presented in the market, possibility to design a process seamlessly, from intentions (process goals) to executable scenarios, remains a challenging task. In this paper, we introduce an approach for modeling and simulating a crisis management process from goals to scenarios. We consider an example of flood management process specified for floods on Oka River in the Moscow region in Russia. In order to specify the intentions behind the flood management process, we use MAP formalism. For representing the process at the operational level, we use Statecharts formalism. To align the strategic and operation process levels, we translate the MAP model of flood management process to statecharts. We simulate the flood management process, showing how the process goals defined on the strategic level can be achieved by various scenarios executed in the operational level.

In the rehabilitation management domain, we find many situations where actors have to manage complex cases. To facilitate patients’ quick recovery from their individual conditions, case managers need a high degree of flexibility in organizing their tasks. Unfortunately, giving them complete flexibility is challenging for two reasons: Firstly, process owners may want to tame the flexibility to conform with compliance policies. Secondly, without complete information about the possible processes in the problem domain, software engineers are struggling to design information systems that can support these case management processes effectively. In this paper, we will therefore show how semantic web technologies can complement adaptive case management techniques in order to cope with the cases’ flexibility. Following the ideas of linked data and the open-world assumption, these techniques facilitate (1) a data structure that is easily extendable, (2) data quality improvements, and (3) the definition and checking of business rules using domain concepts. As a proof of concept, we integrated the method into a case management tool and conducted a small case study using real-life examples from the rehabilitation domain.

Speech acts have been proposed to improve the design of interactive systems for decades. Nevertheless, they have not yet made their way to common practice in software engineering or even process modeling. Various types of workflow management systems have been successful to support or even automate mostly predictable schema based process patterns without the explicit use of speech acts as design primitives. Yet, todays work is increasingly characterized by unpredictable collaborative processes, called knowledge work. Some types of knowledge work are supported by case management tools which typically provide regulated access to case-related information. But communicative acts are not supported sufficiently. Since knowledge workers are well aware of the pragmatic dimension of their communicative acts, we believe that bringing this awareness of the nature of a speech act to a case management tool will allow for better support of unregulated knowledge intensive processes. In this paper we propose a speech-act-based approach to improve the effectivity of knowledge work. We thereby enhance case management systems by making them aware of speech acts. Speech act related micro processes can then be used to prevent misunderstandings, increase process transparency and make useful inferences.

This paper proposes structural consistency checking for Adaptive Case Management (ACM). Structures such as a hierarchical organization of business goals and dependencies among tasks are either created at design time or evolve over time while working on cases. In this paper, we identify structures specific to current ACM systems (as opposed to other BPM systems), discuss which inconsistencies can occur, and outline how to discover these issues through model checking and graph algorithms.

There are a number of methods for business process improvement that are used in practice and investigated in theory, such as Lean or Six Sigma. Most of these methods are activity based and they are aimed at optimizing the activities flow, and/or the usage of resources in the process. These methods suit well the workflow-based processes and thinking, but they are not easily adaptable to Case/Adaptive Case Management (CM/ACM) processes, the goal of improvement for which is improving the overall result from the knowledge workers cooperative work. Another distinctive feature of CM/ACM is that the process is guided not through which flow of activities to use in certain situations, but through a set of templates to use in these situations. This paper outlines a possible method of improving CM/ACM processes based on the Viable System Model (VSM). Though the usage of VSM for process improvement has been reported in the literature, it was not specifically applied to CM/ACM processes. The outline is based on the analysis of the process of organizing a series of scientific events, such as the AdaptiveCM workshop.

Process models need to reflect the real behavior of an organization’s processes to be beneficial for several use cases, such as process analysis, process documentation and process improvement. One quality criterion for a process model is that they should precise and not express more behavior than what is observed in logging data. Existing precision measures for process models purely focus on the control-flow dimension of a process model, thereby ignoring other perspectives, such as the data objects manipulated by the process, the resources executing process activities, and time-related aspects (e.g., activity deadlines). Focusing on the control-flow only, the results may be misleading. This paper extends existing precision measures to incorporate the other perspectives and, through an evaluation with a real-life process and corresponding logging data, demonstrates how the new measure matches our intuitive understanding of precision.

Deviation detection is a set of techniques that identify deviations from normative processes in real process executions. These diagnostics are used to derive recommendations for improving business processes. Existing detection techniques identify deviations either only on the process instance level or rely on a normative process model to locate deviating behavior on the event level. However, when normative models are not available, these techniques detect deviations against a less accurate model discovered from the actual behavior, resulting in incorrect diagnostics. In this paper, we propose a novel approach to detect deviation on the event level by identifying frequent common behavior and uncommon behavior among executed process instances, without discovering any normative model. The approach is implemented in ProM and was evaluated in a controlled setting with artificial logs and real-life logs. We compare our approach to existing approaches to investigate its possibilities and limitations. We show that in some cases, it is possible to detect deviating events without a model as accurately as against a given precise normative model.

Process mining is an emerging discipline whose aim is to discover, monitor and improve real processes by extracting knowledge from event logs representing actual process executions in a given organizational setting. In this light, it can be applied only if faithful event logs, adhering to accepted standards (such as XES), are available. In many real-world settings, though, such event logs are not explicitly given, but are instead implicitly represented inside legacy information systems of organizations, which are typically managed through relational technology. In this work, we devise a novel framework that supports domain experts in the extraction of XES event log information from legacy relational databases, and consequently enables the application of standard process mining tools on such data. Differently from previous work, the extraction is driven by a conceptual representation of the domain of interest in terms of an ontology. On the one hand, this ontology is linked to the underlying legacy data leveraging the well-established ontology-based data access (OBDA) paradigm. On the other hand, our framework allows one to enrich the ontology through user-oriented log extraction annotations, which can be flexibly used to provide different log-oriented views over the data. Different data access modes are then devised so as to view the legacy data through the lens of XES.

Detecting and measuring resource queues is central to business process optimization. Queue mining techniques allow for the identification of bottlenecks and other process inefficiencies, based on event data. This work focuses on the discovery of resource queues. In particular, we investigate the impact of available information in an event log on the ability to accurately discover queue lengths, i.e. the number of cases waiting for an activity. Full queueing information, i.e. timestamps of enqueueing and exiting the queue, makes queue discovery trivial. However, often we see only the completions of activities. Therefore, we focus our analysis on logs with partial information, such as missing enqueueing times or missing both enqueueing and service start times. The proposed discovery algorithms handle concurrency and make use of statistical methods for discovering queues under this uncertainty. We evaluate the techniques using real-life event logs. A thorough analysis of the empirical results provides insights into the influence of information levels in the log on the accuracy of the measurements.

Process mining provides a set of techniques to discover process models from recorded event data or to analyze and improve given process models. Typically, these techniques give a single point of view on the process. However, some domains need to differentiate the process according to the characteristic features of their cases. The healthcare domain, for example, needs to distinguish between different groups of patients, defined by the patients’ properties like age or gender, to get more precise insights into the treatment process. The emerging concept of multidimensional process mining aims to overcome this gap by the notion of data cubes that can be used to spread data over multiple cells. This paper introduces PMCube, a novel approach for multidimensional process mining based on the multidimensional modeling of event logs that can be queried by OLAP operators to mine sophisticated process models. An optional step of consolidation allows to reduce the complexity of results to ease its interpretation. We implemented this approach in a prototype and applied it in a case study to analyze the perioperative processes in a large German hospital.

Process mining discovers process models from event logs. Logs containing heterogeneous sets of traces can lead to complex process models that try to account for very different behaviour in a single model. Trace clustering identifies homogeneous sets of traces within a heterogeneous log and allows for the discovery of multiple, simpler process models. In this paper, we present a trace clustering method based on local alignment of sequences, subsequent multidimensional scaling, and k-means clustering. We describe its implementation and show that its performance compares favourably to state-of-the-art clustering approaches on two evaluation problems.

Human resources are of central importance for executing and supervising business processes. An optimal resource allocation can dramatically improve undesirable consequences of resource shortages. However, existing approaches for resource allocation have some limitations, e.g., they do not consider concurrent process instances or loops in business processes, which may greatly alter resource requirements. This paper introduces a novel approach for automatically allocating resources to process activities in a time optimal way that is designed to tackle the aforementioned shortcomings. We achieve this by representing the resource allocation problem in Answer Set Programming (ASP), which allows us to model the problem in an extensible, modular, and thus maintainable way, and which is supported by various efficient solvers.

Understanding the performance of business processes is an important part of any business process intelligence project. From historical information recorded in event logs, performance can be measured and visualized on a discovered process model. Thereby the accuracy of the measured performance, e.g., waiting time, greatly depends on (1) the availability of start and completion events for activities in the event log, i.e. transactional information, and (2) the ability to differentiate between subtle control flow aspects, e.g. concurrent and interleaved execution. Current process discovery algorithms either do not use activity life cycle information in a systematic way or cannot distinguish subtle control-flow aspects, leading to less accurate performance measurements. In this paper, we investigate the automatic discovery of process models from event logs, such that performance can be measured more accurately. We discuss ways of systematically treating life cycle information in process discovery and their implications. We introduce a process discovery technique that is able to handle life cycle data and that distinguishes concurrency and interleaving. Finally, we show that it can discover models and reliable performance information from event logs only.

This paper addresses the following predictive business process monitoring problem: Given the execution trace of an ongoing case, and given a set of traces of historical (completed) cases, predict the most likely outcome of the ongoing case. In this context, a trace refers to a sequence of events with corresponding payloads, where a payload consists of a set of attribute-value pairs. Meanwhile, an outcome refers to a label associated to completed cases, like, for example, a label indicating that a given case completed “on time” (with respect to a given desired duration) or “late”, or a label indicating that a given case led to a customer complaint or not. The paper tackles this problem via a two-phased approach. In the first phase, prefixes of historical cases are encoded using complex symbolic sequences and clustered. In the second phase, a classifier is built for each of the clusters. To predict the outcome of an ongoing case at runtime given its (uncompleted) trace, we select the closest cluster(s) to the trace in question and apply the respective classifier(s), taking into account the Euclidean distance of the trace from the center of the clusters. We consider two families of clustering algorithms – hierarchical clustering and k-medoids – and use random forests for classification. The approach was evaluated on four real-life datasets.

Process Aware Information Systems (PAIS) are IT systems which support business processes and generate event-logs as a result of execution of the supported business processes. Alpha Miner is a popular algorithm within Process Mining which consists of discovering a process model from the event-logs. Discovering process models from large volumes of event-logs is a computationally intensive and a time consuming task. In this paper, we investigate the application of parallelization on Alpha Miner algorithm. We apply implicit multithreading parallelism and explicit parallelism through parfor on it offered by MATLAB (Matrix Laboratory) for multi-core Central Processing Unit (CPU). We measure performance gain with respect to serial implementation. Further, we use Graphics Processor Unit (GPU) to run computationally intensive parts of Alpha Miner algorithm in parallel. We achieve highest speedup on GPU reaching till $$39.3\times $$ from the same program run over multi-core CPU. We conduct experiments on real world and synthetic datasets.

Event logs are invaluable sources of knowledge about the actual execution of processes. A large number of techniques to mine, check conformance and analyze performance have been developed based on logs. All these techniques require at least case ID, activity ID and the timestamp to be in the log. If one of those is missing, these techniques cannot be applied. Real life logs are rarely originating from a centrally orchestrated process execution. Thus, case ID might be missing, known as unlabeled log. This requires a manual preprocessing of the log to assign case ID to events in the log.In this paper, we propose a new approach to deduce case ID for the unlabeled event log depending on the knowledge about the process model. We provide a set of labeled logs instead of a single labeled log with different rankings. We evaluate our prototypical implementation against similar approaches.

The construction of a business process simulation (BPS) model requires significant modeling efforts. This paper focuses on modeling the interarrival time (IAT) of entities, i.e. the time between the arrival of consecutive entities. Accurately modeling entity arrival is crucial as it influences process performance metrics such as the average waiting time. In this respect, the analysis of event logs can be useful. Given the limited process mining support for this BPS modeling task, the contribution of this paper is twofold. Firstly, an IAT input model taxonomy for process mining is introduced, describing event log use depending on process and event log characteristics. Secondly, ARPRA is introduced and operationalized for gamma distributed IATs. This novel approach to mine an IAT input model is the first to explicitly integrate the notion of queues. ARPRA is shown to significantly outperform a benchmark approach which ignores queue formation.

Traditional approaches for process modeling usually comprise the control flow of well-structured activities that an organization performs in order to achieve its objectives. However, many processes involving decision-making and creativity do not follow a well-structured flow of activities, having rather a more ad-hoc nature at each instance. Knowledge Intensive Processes (KIP) is an example of this kind of process. It is difficult to gather information about a KIP and create a representative model, since it might vary from instance to instance due to decisions made by its participants. The contextual information of each activity - as well as the desires and intentions of the participants - are vital to the complete understanding of the process itself. In this paper, we propose a method to extract intentions and desires from KIP participants using NLP Techniques and social media content, as well as exploring its possibilities on a real case study using Twitter.

A process sharing platform will be launched in 2015 for the Swiss E-Government BPM community. The platform will be open to public administrations, private BPM practitioners, and academics. We reflect in this paper on the general opportunities and challenges of such a platform. We have identified four domains in which it can greatly improve the efficiency of BPM projects and the general BPM uptake in public administrations: planning, process modeling, comparative analysis and systemic analysis. Despite all the potential of such a platform, we believe that there is also a risk that the users might not engage sufficiently in the community for the platform to be successful. We believe the communication on and about the platform should be given particular attention, by the use of professional marketing tools and methods. We also advise that a step-by-step BPM methodology serving as a general guideline for the public administration should be published on the platform and maintained by the community. This methodology could not only help in rising the success rate of BPM projects in the administration, but could also improve the perception of added value of the platform amongst practitioners and decision-makers by raising their understanding of BPM to the required level.

Business Process Standardization (BPS) is an important instrument to enhance an organizations’ competitiveness. A crucial element to achieve BPS is the change-supportive attitude by the affected employees. Previous studies have drawn attention on various determinants of attitude such as the employee’s motivation and culture but also on the broader process and work environment and on an employee’s perception of the embeddedness of their tasks. In this paper, we draw on the latter and on the theoretical concepts of self-construal and task interdependence to develop a theoretical concept named ‘job construals’. To measure the construct, we develop a valid measurement scale. Therefore, we use a card sorting approach with BPM experts to assess the validity of the measure. The contribution of our work lies in understanding the drivers of BPS acceptance and consequently the successful implementation of process standardization initiatives.

Local clothing retailers compete with online retailers but have difficulties to increase cross-selling revenues. Therefore, a data-driven sales process is conceptualized that uses data from social software in order to increase revenue. It identifies and tracks the customer using RFIDs in customer loyalty cards. By these means, social data can be used in all phases of the purchase and both for major and minor purchases. Individual product suggestions and offerings can be tailored. Local retailers are able to catch up with online retailers in their cross- and upselling revenues. In consequence, local retailers are able to stay competitive.

Improving the quality and efficiency of modeling is an important goal of many research approaches. A number of influence factors outside the modeling environment are investigated. To measure the influence objective measures are necessary. Therefore, this paper strives to create a foundation for such a measures, by measuring the influence of caffeine on the modeling performance. Choosing caffeine has the important advantage to provide an unbiased setting. The possible path of influence of caffeine on the modeling performance is analyzed. A research model is developed and the result of a pre-study are discussed.

This study considers the manner in which business process modeling (BPM) effect the business process change such as business process reengineering (BPR) or business process improvement. The expectations of top management regarding information technology (IT) are considered to be increasing, therefore BPM with IT implementation is becoming increasingly important. Although Japanese firms tend to spend much more on improving business operational efficiency compared with firms in Western countries, however, the results do not seem to be effective enough [1]. One of the reasons for this situation is considered to be the Japanese style of business process change. The authors analyzed this issue by the results of a survey that the authors had conducted on BPM methods and the effect of business process change.

Business processes involve data that can be modified and updated by various activities at any time. The data involved in a business process can be associated with flow elements or stored data. This data must satisfy the business compliance rules associated with the process, where business compliance rules are policies or statements that govern the behaviour of a company. To validate the correctness of a business process, it is necessary to validate the managed data before and during the process instantiation, since none of the activities of a process can work correctly using incorrect data. The analysis of the correctness of the business process is typically related to the activity executed according to the value of a data variable in each case, that verifies whether the model and the log conform to each other. The incorporation of the study of the correctness of the semantic of data value (called Business Data Constraints) is also consider essential. The execution of the correct activity according to the data is fundamental: it is no less important, however, to validate the correctness of the input data of a business process, and whether it affects the workflow and the compliance of the policies. In this paper, every special characteristic for the analysis of the correctness of data in a business process is studied, as is how the classic techniques can be improved for the validation, diagnosis and decision-making support concerning data in Business Processes.

Data-centric approaches are very promising. The inference of business processes from the structure of data provides more flexibility than the activity-based approaches which enforce a specific flow of behavior. However, the price for this flexibility is the lack of a standard organizational behavior. Actually, there is a tension between the standardization of workers’ behavior and allowing them to deal with unexpected situations by using their own tacit knowledge. The blended workflow approach [1] integrates activity-based with goal-based representations of a workflow to balance these two aspects. In this paper we describe how to design two workflow models, an activity-based and a goal-based, from a common data-model. The overall approach consists on a stepwise generation of models departing from an annotated data model where an intermediate state model is used to define the set of conditions that both workflow models, activity and goal, have to support.

There exist two major modeling paradigms for business process modeling: The predominant activity centric one and the artifact centric paradigm. Both are suitable for modeling and for executing business processes. However, process models are typically designed from different perspectives. Current translation methods operate on the syntactic level, preserving the point of view of the source process. The results of such translations are not particularly useful, understandable and insightful for stakeholders. In this paper we motivate the need for ontology-guided translations by comparing the results of a purely syntactic translation with a manual translation. We discuss shortcomings of the generated solutions and propose an ontology-based framework and sketch corresponding translation method for the generation of semantic translations, which allow to incorporate the point of view of the target modeling paradigm.

Analytics Process Management (APM) is an emerging branch of Business Process Management that is focused on supporting Business Analysts and others as they apply analytics approaches, algorithms, and outputs in order to discover and/or repeatedly produce business-relevant insights and apply them into on-going business operations. While APM is now occurring in many businesses, it is typically managed in ad hoc ways using a variety of different tools and practices. This paper proposes to use principles from Case Management (or equivalently, Business Artifacts) to provide a foundational structure for APM. In particular, six key classes of Case Types are identified, that can model the vast majority of activities and data being manipulated in APM contexts. These Case Types can simplify support for managing provenance, auditability, repeatability, and explanation of analytics results. The paper also identifies two key adaptations of the classical Case Management paradigm that are needed to support APM. The paper validates the proposed Case Types and adaptations by examining two recent systems built at IBM Research that support Business Analysists in the use of analytics tools.

The execution of cross-organization business processes often implies the exchange of physical goods without necessarily changing the ownership of such goods. Typical examples are logistic processes where goods are managed by shipping companies that are not the owner of the goods. To ensure that these goods are properly handled, while the service is executed, a monitoring system needs to be put in place.The goal of this paper is to propose a novel approach for monitoring physical goods while executing cross-organization business processes. The approach envisions the usage of Smart Objects attached to the physical goods, or to their containers. To this aim, an extension of the Guard-Stage-Milestone framework is proposed to allow the Smart Objects to monitor the process execution and take into account the limitations of their power and computational resources.

While business process models have been proven to represent useful artifacts for organizations, they are not suitable to represent the detailed decision logic underlying processes. Ignoring this limitation often results in complex, spaghetti-like process models for workflows driven by data-based decisions. To avoid this, decision logic should be isolated from process logic, following a separation of concerns paradigm. To support this practice, we present an approach that automatically derives process models for which this paradigm applies. It takes as input structural data-flow relations underlying a workflow and produces a process model that emphasizes the most important decisions in a process, while detailed decision logic is outsourced to dedicated decision models.

While executing business processes, regularly decisions need to be made such as which activities to execute next or what kind of resource to assign to a task. Such a decision-making process is often case-dependent and carried out under uncertainty, yet requiring compliance with organization’s service level agreements. In this paper, we address these challenges by presenting an approach for dynamic decision-making. It is able to automatically propose case-dependent decisions during process execution. Finally, we evaluate it with a use case that highlights the improvements of process executions based on our dynamic decision-making approach.

Recently, a surge in the use of declarative process models has been witnessed. These constraint-driven models excel at representing and enacting flexible and adaptable decision processes in application areas such as scheduling and workflow management. This work examines the intricacies of the most widespread declarative process language, Declare, which are commonly referred to as hidden dependencies. These dependencies typically increase the steepness of the learning curve of Declare models and making them explicit can lower the threshold for modelers to use Declare in a sense-making and intuitive way. This work proposes a way to gamify Declare models for novice users by annotating such models with extra constraint and dependency information, and feedback. Hence, it offers the ability of discovering Declare and its intricacies in a game-like fashion which lowers the threshold for learning these cognitively demanding models, as well as to use them for assessing modeling efforts by verifying that the desired behavior is present.

Optimal decision making during the business process execution is crucial for achieving the business goals of an enterprise. Process execution often involves the usage of the decision logic specified in terms of business rules represented as atomic elements of conditions leading to conclusions. However, the question of using and integrating the process- and decision-centric approaches, i.e. harmonization of the widely accepted Business Process Model and Notation (BPMN) and the recent Decision Model and Notation (DMN) proposed by the OMG group, is important. In this paper, we propose a four-step approach to derive decision models from process models on the examples of DMN and BPMN: (1) Identification of decision points in a process model; (2) Extraction of decision logic encapsulating the data dependencies affecting the decisions in the process model; (3) Construction of a decision model; (4) Adaptation of the process model with respect to the derived decision logic. Our contribution also consists in proposing an enrichment of the extracted decision logic by taking into account the predictions of process performance measures corresponding to different decision outcomes. We demonstrate the applicability of the approach on an exemplary business process from the banking domain.

Dynamically allocating the most appropriate resource to execute the different activities of a business process is an important challenge in business process management. An ineffective allocation may lead to an inadequate resources usage, higher costs, or a poor process performance. Different approaches have been used to solve this challenge: data mining techniques, probabilistic allocation, or even manual allocation. However, there is a need for methods that support resource allocation based on multi-factor criteria. We propose a framework for recommending resource allocation based on Process Mining that does the recommendation at sub-process level, instead of activity-level. We introduce a resource process cube that provides a flexible, extensible and fine-grained mechanism to abstract historical information about past process executions. Then, several metrics are computed considering different criteria to obtain a final recommendation ranking based on the BPA algorithm. The approach is applied to a help desk scenario to demonstrate its usefulness.

The need for constant adaptation to address emerging demands or undesired events within organizations has grown. Unplanned situations may occur at any time during process execution and the design of a process model that predicts all paths should give place to flexible “organic” design. This paper addresses the problem of how to provide mechanisms for dynamic adaptation in Process-Aware Information Systems. On top of a theory for context-aware information systems, we propose a context management framework that aims to automate dynamic process adaptation by re-planning a process instance. The proposal was partially implemented and evaluated in a real case scenario.

Today, a new infrastructure provisioning approach called Cloud Elasticity is evolving, covering three dimensions of elasticity: resource, cost, and quality. Recently, Cloud Elasticity has been utilized for Business Process Enactment in the Cloud as the involved services face highly volatile demand levels. Through treating the three dimensions equally, so-called Elastic (Business) Processes can be achieved, i.e., by leasing and releasing resources on-demand, and customer’s requirements regarding quality and cost can now be met more easily. However, information technology infrastructures are now counted as a problem linked to global warming, and accounting for energy efficiency is an adequate response towards “Green” initiatives. This paper is focused on the fulfillment of the principles of Green Computing and Green Business Process Management on the basis of Cloud Elasticity to support Elastic Processes. We describe an approach for the enactment of energy-efficient Elastic Processes by means of the ViePEP platform.

Successful applications of the Internet of Things such as smart cities, smart logistics, and predictive maintenance, build on observing and analyzing business-related objects in the real world for business process execution and monitoring. In this context, complex event processing is increasingly used to integrate events from sensors with events stemming from business process management systems. This paper describes a methodology to combine the areas and engineer an event-driven logistics processes application. Thereby, we describe the requirements, use cases and lessons learned to design and implement such an architecture.

During the development of a process-aware information system, there might exist multiple process models that describe the system’s behavior at different levels of abstraction. Thus, containment checking is important for detecting unwanted deviations of process models to ensure a refined low-level model still conforms to its high-level counterpart. In our earlier work, we have interpreted the containment checking problem as a model checking problem and leveraged existing powerful model checkers for this purpose. The model checker will detect any discordance of the input models and yield corresponding counterexamples. The counterexamples, however, are often difficult for developers with limited knowledge of the underlying formal methods to understand. In this paper, we present an approach for interpreting the outcomes of containment checking of process models. Our approach aims to analyze the input models and counterexamples to identify the actual causes of containment inconsistencies. Based on the analysis, we can suggest a set of countermeasures to resolve the inconsistencies. The analysis results and countermeasures are visually presented along with the involved model elements such that the developers can easily understand and fix the problems.

An increase of process awareness within organizations and advances in IT systems led to a development of process-aware information systems (PAIS) in many organizations. UPROM is developed as a unified BPM methodology to conduct business process and user requirements analysis for PAIS in an integrated way. However, due to the purpose, granularity and form of UPROM artifacts, one cannot analyze the software requirements in detail with (semi-)formal methods for properties such as completeness, compliance and quality. In contrast, Problem Frames modeled using the UML4PF tool can be used for such analysis. But using the Problem Frames notation and corresponding methods alone does not cover a direct support for building a PAIS. Hence, in this work we propose to integrate UPROM and UML4PF using model transformation. We use eCompany, a project which is part of an e-government program, as running example.

Nowadays, business process modeling is heavily used in various business contexts. For instance, process models help to obtain a common understanding of a company’s business processes [1], facilitate inter–organizational business processes [2], and support the development of information systems [3]. Still, process models in industrial process model collections often display a wide range of quality problems [4], calling for a deeper investigation of process model quality.

Process models abstract a dynamic phenomenon in the form of a static representation. This contrast makes them difficult to comprehend. Innovative usage of dynamic multimedia techniques in combination with static process model visualization knowledge remains an opportunity to address this problem. In this paper, we unfold our research plan to invigorate process models through the development of eight different embellishment components to enhance process comprehension.

Business process models are commonly used for communication purposes among stakeholders. They are often distributed by means of web portals in the form of images. As the organizational population accessing these web portals has diverse needs and prior knowledge, the process models should be intuitive, likeable, well-accepted, and easily understandable [8] to reach their communication goal. Up to now, process models are mostly represented in a graphical but static, one-size-fits-all way. Visualization techniques have been applied at design time only to improve on the communication power of the model and support the model user in reading the model. The opportunity to dynamically guide model users to relevant parts of the diagram when reading the model is missed, and model users may not know where to focus their attention on. This paper provides a conceptual design for the dynamic visualization of process models. Our design is implemented in a prototype for a case study and evaluated by process participants. From this we conclude that such a dynamic visualization is preferred over a static visualization.

Process model element labels are critical for an appropriate association between a symbol instance in a model and the corresponding real world meaning. Disciplines, in which an efficient presentation of text labels is crucial (e.g., cartography) have continuously improved their visualization design techniques for labels since they serve as effective cognitive aids in problem solving. Despite the relevance of labels for information exploration, surprisingly little research has been undertaken on the visual design of element labels of business process models. This paper fills this gap and provides a comprehensive overview of visual design options for process model element labels. First, we summarize the findings existing in the diverse areas of literature relevant to visual display of process model element labels. Second, we analyze the status quo of visual design of element labels in common business process modeling tools indicating only little layouting support. Third, we give recommendations regarding the visual design of element labels. To our knowledge, this is the first comprehensive analysis of visual design of process model element labels.

To analyze large amounts of data, visual analysis tools offer filter mechanisms for drilling down into multi-dimensional information spaces, or slicing and dicing them according to given criteria. This paper introduces an analysis approach for navigating multi-dimensional process instance execution logs based on business process models. By visually selecting parts of a business process model, a set of available log entries is filtered to include only those entries that result from execution instances of the selected process branches. Using this approach allows to exploratively navigate through process execution logs and analyze them according to the causal-temporal relationships encoded in the underlying business process model. The business process models used by the approach can either be created using model editors, or be statistically derived using process mining techniques. We exemplify our approach with a prototypical implementation.