Business process management employs process models and event logs to represent the behavior of the information systems under study. Traditional case-centric notions consider the order of activities and events in isolated process instances. The emerging field of object-centric processes challenges this assumption by putting objects in the center. Object-centric process mining and modeling approaches identify the structure of co-evolving data objects that influence the behavior of an information system to provide a comprehensive view of the system behavior. Object-centricity has been investigated independently in process modeling and in process mining, which resulted in the coexistence of seemingly contradictory assumptions and definitions. As a community effort, this research manifesto relates and aligns existing terminologies, definitions, and perspectives to provide a common ground for current and future research in object-centric business process management. Based on the current state of research, we propose a conceptualization that sets process models and event logs in relation to the information system’s behavior and the execution data it generates. The conceptualization aims at aligning different terminologies and, thus, providing a basis to model and analyze behavioral characteristics. Building on this common ground, we identify open research challenges along the most relevant research areas in object-centric process management. For each research area, its current status is investigated and an outline of the most relevant research challenges is presented.

To achieve their goals, organizations execute business processes, which require effective allocation of resources to process activities. This results in the decision-making problem: Which resources should be allocated to which process activities? This problem significantly impacts both process efficiency and effectiveness. Over the past decades, various system-initiated (largely automated) resource allocation approaches have been developed. This study presents a comprehensive overview of this field by analyzing 61 primary studies identified through a rigorous, structured literature review covering publications from 1995 to 2023. We investigate resource allocation goals and cardinalities and describe how process models, execution data, and task attributes, as well as resource attributes, are used to specify the resource allocation problem. Additionally, the type of algorithmic solution and evaluation methods are discussed. This study shows that most approaches support 1-to-1 allocation cardinalities only, specify process-oriented goals, focus on process models, and utilize rule-based methods. Based on the results, we call for future research to define common terminology, support evidence-oriented resource allocation and adaptability, and improve reproducibility and comparability by performing benchmarking studies.

Abstract Business process models serve as visual representations of the structured sequence of activities, or activity relationships, aimed at achieving specific organizational objectives. These models explicitly depict only a portion of the activity relationships, leaving other, so-called hidden relationships to be inferred through transitivity and domain knowledge. Because designers lack visibility into these hidden relationships, tasks associated with process redesign (BPR) are more challenging. BPR is a specialized area within business process management focused on modifying process behavior at the model level. While best practices have been introduced, explicit guidance for implementing them is currently lacking. This paper introduces an approach to delineate all possible relationships between any two activities within a BPMN diagram by considering a combination of existential and temporal dependencies. Furthermore, the proposed approach facilitates a detailed specification of change operations related to BPR and their consequential effects on activity relationships. We illustrate this by applying BPR strategies such as resequencing and parallelism to the use case of an order management system.

Abstract Process-aware information systems guide participants through the execution of processes. However, existing systems have limited support for knowledge-intensive processes, which are multi-variant and shaped by informed decisions of knowledge workers. Yet, making such decisions causes high cognitive load, as the effect of the decision on the future process execution must be considered. This may cause errors and/or slow down the process execution. We present an approach based on fragment-based Case Management. It supports the iterative decision making by (i) enabling knowledge workers to define goals and (ii) by giving recommendations on which decision outcomes align with the goals and which do not. For that, we use information from the process model and the running process instance. We show the technical feasibility with a proof-of-concept implementation and the value for knowledge workers in a preliminary user study.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Abstract In order to achieve their business goals, organizations heavily rely on the operational excellence of their business processes. In traditional scenarios, business processes are usually well-structured, clearly specifying when and how certain tasks have to be executed. Flexible and knowledge-intensive processes are gathering momentum, where a knowledge worker drives the execution of a process case and determines the exact process path at runtime. In the case of an exception, the knowledge worker decides on an appropriate handling. While there is initial work on exception handling in well-structured business processes, exceptions in case management have not been sufficiently researched. This paper proposes an exception handling framework for stage-oriented case management languages, namely Guard Stage Milestone Model, Case Management Model and Notation, and Fragment-based Case Management. The effectiveness of the framework is evaluated with two real-world use cases showing that it covers all relevant exceptions and proposed handling strategies.

Process discovery is an important area in the field of process mining, where most discovery algorithms focus on process control-flow, giving little attention to the data-flow perspective. As a result, the discovered process models lack information about data dependencies, and process experts need to manually enrich the discovered process models accordingly. This requires deep domain knowledge, is not scalable, and error-prone. To overcome this limitation, this paper proposes an approach that aims to discover the data objects and their behavior by investigating how event attributes are manipulated during process execution. The resulting data objects are used to enhance the discovered process model. The feasibility of the proposed approach is evaluated with two real-life event logs: Road Traffic Fine Management and Hospital Billing.

In the field of Business Process Management (BPM), modeling business processes and related data is a critical issue since process activities need to manage data stored in databases. The connection between processes and data is usually handled at the implementation level, even if modeling both processes and data at the conceptual level should help designers in improving business process models and identifying requirements for implementation. Especially in data- and decision-intensive contexts, business process activities need to access data stored both in databases and data warehouses. In this paper, we complete our approach for defining a novel conceptual view that bridges process activities and data. The proposed approach allows the designer to model the connection between business processes and database models and define the operations to perform, providing interesting insights on the overall connected perspective and hints for identifying activities that are crucial for decision support.

The traceable execution of business processes and choreographies using smart contracts is one prominent application of blockchain technology in Business Process Management (BPM). Existing approaches support a large set of patterns, modeling languages, and blockchain architectures, which cover a wide range of practical scenarios. However, they largely neglect the important aspect of time, a crucial part of process and choreography models manifested in deadlines, delays, and other temporal constraints. We argue that this deficit is due to inherent limitations of smart contracts-in particular the absence of a natural notion of measuring time-on popular blockchain platforms used in research and practice. We introduce a set of time measures available on blockchain platforms to alleviate these issues, and systematically compare their properties. We also give hints as to their suitability for facilitating various temporal constraints commonly found in process models.

Business process improvement is an endless challenge for many organizations. As long as there is a process, it must be improved. Nowadays, improvement initiatives are driven by professionals. This is no longer practical because people cannot perceive the enormous data of current business environments. Here, we introduce ubiquitous decision-aware business processes. They pervade the physical space, analyze the ever-changing environments, and make decisions accordingly. We explain how they can be built and used for improvement. Our approach can be a valuable improvement option to alleviate the workload of participants by helping focus on the crucial rather than the menial tasks.

Business process management (BPM) enables modeling, executing and monitoring organizational processes to achieve certain business goals. Organizations continue to strive for agility and take advantage of the digital era to bring flexibility in their processes, for example by integrating complex event processing (CEP) techniques. Event handling specifies how a process interacts with its environment and how the environmental occurrences influence the execution of the process. Though highly expressive and feature-rich languages like BPMN exist for process specification, they still lack the flexibility required for event handling in different real-life scenarios. In this work, an event handling model is proposed that take into account the possibilities of event subscription at different points in time with respect to process execution. The model is grounded formally and provides mapping to Petri Nets as implementation semantics. Further, trace analysis ensures correct execution of process behavior while maintaining the temporal dependencies intact among event subscription, event occurrence, event consumption and event unsubscription.

Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.

Ubiquitous business processes are the new generation of processes that pervade the physical space and interact with their environments using a minimum of human involvement. Although they are now widely deployed in the industry, their deployment is stillad hoc. They are implemented after an arbitrary modeling phase or no modeling phase at all. The absence of a solid modeling phase backing up the implementation generates many loopholes that are stressed in the literature. Here, we tackle the issue of modeling ubiquitous business processes. We propose patterns to represent the recent ubiquitous computing features. These patterns are the outcome of an analysis we conducted in the field of human-computer interaction to examine how the features are actually deployed. The patterns' understandability, ease-of-use, usefulness, and completeness are examined via a user experiment. The results indicate that these four indexes are on the positive track. Hence, the patterns may be the backbone of ubiquitous business process modeling in industrial applications.

Nowadays, business process modeling and system-supported executions have become a commodity in many companies. Most systems, however, focus on modeling and execution of static, pre-defined processes with standards like the Business Process Model and Notation (BPMN). While these static process executions are applicable to a number of traditional processes like purchase orderings or back orderings, they fail at representing variant-rich, flexible processes. One solution for supporting flexible processes is Adaptive Case Management (ACM), where a case manager creates an individual execution path for each process instance, such as a doctor defining a clinical pathway for a specific patient. We found out, however, that both approaches are too strict, either supporting static process definitions with only a limited set of pre-defined flexibility or allowing maximum flexibility but requiring a highly skilled knowledge worker. To overcome this problem, we propose an implementation framework for Production Case Management (PCM) that combines concepts from traditional process management and adaptive case management. PCM combines the modeling of small, static process fragments with the execution flexibility of ACM.

Business process automation improves organizations' efficiency to perform work. In existing business process management systems, process instances run independently from each other. However, synchronizing instances carrying similar characteristics, i.e., sharing the same data, can reduce process execution costs. For example, if an online retailer receives two orders from one customer, there is a chance that they can be packed and shipped together to save shipment costs. In this paper, we extend the concept of batch activities to batch regions consisting of a set of connected activities instead of a single activity. Further, we use concepts from the database domain and introduce data views to business processes to distinguish process instances which can be synchronized. We also evaluate the concepts introduced in this paper with a case study comparing costs for normal and batch processing.

This article addresses the transformation of a process model with an arbitrary topology into an equivalent structured process model. In particular, this article studies the subclass of process models that have no equivalent well-structured representation but which, nevertheless, can be partially structured into their maximally-structured representation. The transformations are performed under a behavioral equivalence notion that preserves the observed concurrency of tasks in equivalent process models. The article gives a full characterization of the subclass of acyclic process models that have no equivalent well-structured representation, but do have an equivalent maximally-structured one, as well as proposes a complete structuring method. Together with our previous results, this article completes the solution of the process model structuring problem for the class of acyclic process models.

A growing number of enterprises use complex event processing for monitoring and controlling their operations, while business process models are used to document working procedures. In this work, we propose a comprehensive method for complex event processing optimization using business process models. Our proposed method is based on the extraction of behaviorial constraints that are used, in turn, to rewrite patterns for event detection, and select and transform execution plans. We offer a set of rewriting rules that is shown to be complete with respect to the$all$,$seq$, and$any$patterns. The effectiveness of our method is demonstrated in an experimental evaluation with a large number of processes from an insurance company. We illustrate that the proposed optimization leads to significant savings in query processing. By integrating the optimization in state-of-the-art systems for event pattern matching, we demonstrate that these savings materialize in different technical infrastructures and can be combined with existing optimization techniques.

Business process model collections received much attention as option to store an organization's process models and to make them available for further usage. Recently, also data objects and their states are studied intensively. However, process model collections often do not contain data objects. Generally, they focus on control flow structures of the process models. In this paper, we propose an approach to extract data objects from process models existing in a process model collection based on label analysis. In addition, the states of these data objects are extracted as well. After extraction, the data objects and their states are added to the corresponding process model. These resulting process models then can be used to, for instance, perform consistency checks between the process model and object life cycles in real world process model collections. First, the approach is presented generally for block structured process models independently of their notation before the approach gets adapted exemplarily to BPMN and EPCs to show the simplicity of adaptation to specific modeling notations. Finally, the approach is evaluated in a user experiment using the BPMAI model collection.

The execution of business processes generates a lot of data comprising final process results as well as information about intermediate activities, both communicated as events. Automated process execution environments are centrally controlled by process engines that hold the connection between events and the processes they occure in. In contrast, in manual process execution environments, e.g., logistics, these events may not be correlated to the process they origin from. The correlation information is usually not present in the event but in so-called context data, which exists orthogonally to the corresponding process. However, in the areas of process monitoring and analysis, events need to be correlated to specific process instances. To close the gap between recorded events without process correlation and required events with process correlation, we propose a framework that enriches recorded events with context data to create events correlated to processes, so-called process events.

Once multiple models of a business process are created for different purposes or to capture different variants, verification of behaviour equivalence or compatibility is needed. Equivalence verification ensures that two business process models specify the same behaviour. Since different process models are likely to differ with respect to their assumed level of abstraction and the actions that they take into account, equivalence notions have to cope with correspondences between sets of actions and actions that exist in one process but not in the other. In this paper, we present notions of equivalence and compatibility that can handle these problems. In essence, we present a notion of equivalence that works on correspondences between sets of actions rather than single actions. We then integrate our equivalence notion with work on behaviour inheritance that copes with actions that exist in one process but not in the other, leading to notions of behaviour compatibility. Compatibility notions verify that two models have the same behaviour with respect to the actions that they have in common. As such, our contribution is a collection of behaviour equivalence and compatibility notions that are applicable in more general settings than existing ones.

Analysis of behavioural consistency is an important aspect of software engineering. In process and service management, consistency verification of behavioural models has manifold applications. For instance, a business process model used as system spe

Engineering of process-driven business applications can be supported by process modeling efforts in order to bridge the gap between business requirements and system specifications. However, diverging purposes of business process modeling initiatives have led to significant problems in aligning related models at different abstract levels and different perspectives. Checking the consistency of such corresponding models is a major challenge for process modeling theory and practice. In this paper, we take the inappropriateness of existing strict notions of behavioral equivalence as a starting point. Our contribution is a concept called behavioral profile that captures the essential behavioral constraints of a process model. We show that these profiles can be computed efficiently, i.e., in cubic time for sound free-choice Petri nets w.r.t. their number of places and transitions. We use behavioral profiles for the definition of a formal notion of consistency which is less sensitive to model projections than common criteria of behavioral equivalence and allows for quantifying deviation in a metric way. The derivation of behavioral profiles and the calculation of a degree of consistency have been implemented to demonstrate the applicability of our approach. We also report the findings from checking consistency between partially overlapping models of the SAP reference model.

Determining similarity between business process models has recently gained interest in the business process management community. So far similarity was addressed separately either at semantic or structural aspect of process models. Also, most of the contributions that measure similarity of process models assume an ideal case when process models are enriched with semantics---a description of meaning of process model elements. However, in real life this results in a heavy human effort consuming pre-processing phase which is often not feasible. In this paper we propose an automated approach for querying a business process model repository for structurally and semantically relevant models. Similar to the search on the Internet, a user formulates a BPMN-Q query and as a result receives a list of process models ordered by relevance to the query. We provide a business process model search engine implementation for evaluation of the proposed approach.

Process models provide companies efficient means for managing their business processes. Tasks where process models are employed are different by nature and require models of various abstraction levels. However, maintaining several models of one business process involves a lot of synchronization effort and is erroneous. Business process model abstraction assumes a detailed model of a process to be available and derives coarse grained models from it. The task of abstraction is to tell significant model elements from insignificant ones and to reduce the latter. In this paper we argue that process model abstraction can be driven by different abstraction criteria. Criterion choice depends on a task which abstraction facilitates. We propose an abstraction slider - a mechanism that allows user control of the model abstraction level. We discuss examples of combining the slider with different abstraction criteria and sets of process model transformation rules.

The Business Process Execution Language (BPEL) has become the de-facto standard for the description of Web Service compositions. A variety of formal approaches to decide compatibility and consistency for BPEL processes has been presented. Nevertheless, these approaches suffer from high complexity and state explosion. Therefore we present a lean formalization of BPEL 2.0 based on the pi-calculus, that enables efficient reasoning. Due to our focus on behavioral compatibility and consistency checking (and not on comprehensive formalization), we are able to reduce effort needed for process verification. Besides the exemplary application of our approach, we also compare it to existing BPEL formalizations by means of complexity.

The business process execution language (BPEL) is a language to orchestrate web services into a single business process. In a choreography view, several processes are interconnected and their interaction behavior is described from a global perspective. This paper shows how BPEL can be extended for defining choreographies. The proposed extensions (BPEL4Chor) distinguish between three aspects: (i) participant behavior descriptions, i.e. control flow dependencies in each participant, (ii) the participant topology, i.e. the existing participants and their interconnection using message links and (iii) participant groundings, i.e. concrete configurations for data formats and port types. As BPEL itself is used unchanged, the extensions facilitate a seamless integration between service choreographies and orchestrations. The suitability of the extensions is validated by assessing their support for the Service Interaction Patterns.

Service oriented software architectures will form the core of operational enterprise IT landscapes in the future. This contribution starts with an introduction of the state of the art in service oriented architectures. A concrete case study identifies central requirements that are not satisfied by these architectures so far. The authors argue that semantically rich descriptions of services are essential to tap the full potential of service oriented architectures in enterprise environments. This regards matchmaking and binding of services, integration of new services as well as the cost-efficient development of added value services by composing semantically described basic services. This paper introduces a semantic service platform that implements dynamic matchmaking, composition and binding of semantically described services. Finally its functionality and possible application scenarios are outlined

Article Free Access Share on The WASA2 object-oriented workflow management system Authors: Gottfried Vossen University of Münster, Germany University of Münster, GermanyView Profile , Mathias Weske University of Münster, Germany University of Münster, GermanyView Profile Authors Info & Claims SIGMOD '99: Proceedings of the 1999 ACM SIGMOD international conference on Management of dataJune 1999Pages 587–589https://doi.org/10.1145/304182.304586Published:01 June 1999Publication History 26citation995DownloadsMetricsTotal Citations26Total Downloads995Last 12 Months21Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

DNA fragment assembly is an area which makes intensive use of computers. However, computer users in this field are typically not experts in computer science, but build their working environment on an ad-hoc basis. In this situation, it seems appropriate to offer a kind of support which can contribute to a better organization of working environments, and a better exploitation of computer hardware and software. The authors describe an approach in this direction based on the emerging paradigms of workflow modeling and management. In particular they offer three contributions: first, they discuss why workflow management can be fruitfully adopted in DNA fragment assembly, and describe one way to perceive and model sequencing processes as workflows. Second, they outline an architecture of a system intended to support sequencing applications, whose core component is a workflow management system. Finally, they sketch their experience of building a prototype using commercial workflow management technology.