Enterprise, Business-Process and Information Systems Modeling

In contemporary business process management software, the context in which a process is executed is largely static. While the execution of the process itself may be flexible, on-the-fly changes to the context, i.e., physical or logical surroundings, are either limited or impossible. This paper presents concepts for enabling context switching at runtime for the object-aware process management paradigm. Such context switches are enabled at various granularity levels, such as shifting entire process instances to different systems, or migrating sub-processes between different parent processes. We further contribute the algorithms employed in our proof-of-concept implementation and discuss use cases in which context switching capabilities can be utilized. Implementing these advanced concepts helps showcase the maturity of data-centric BPM.

Case Management supports knowledge workers in defining, executing, and monitoring the handling of their cases, e.g. in healthcare or logistics. Fragment-based case management (fCM) allows to define a case model with the help of several process fragments, which can be flexible combined at run-time based on case characteristics and the case worker’s intuition. Cases are often influenced by unknown exception, e.g., the sudden change of patient condition’s or a storm delaying transports. So far, fCM only reacts to known circumstances. In this paper, we want to extend fCM by an exception handling approach. Thereby, existing exception patterns for workflow systems are used and extended by the fragment-level for handling unknown events. In order to enable direct integration and avoid a duplication of semantics, precise rules are specified in order to clarify how to extend which pattern in detail. The applicability of the developed exception handling technique is exemplified on a last mile delivery for parcels.

The ability to enable a tamper-proof distribution of immutable data has boosted the studies around the adoption of blockchains also in Business Process Management. In this direction, current research work primarily focuses on blockchain-based business process design, or on execution engines able to enact processes through smart contracts. Although very relevant, less studies have been devoted so far on how the adoption of blockchains can be beneficial to business process monitoring. This work goes into this direction by providing an insightful analysis to understand the benefits as well as the hurdles of blockchain-enabled business process monitoring. In particular, this work considers the adoption of programmable blockchain platforms to manage the generation, distribution, and analysis of business process monitoring data.

Business Process Management Suites (BPMS) are being adopted in organisations to increase business process agility across a diverse application landscape. Yet many organisations struggle to achieve agile business processes when using a BPMS. This South African financial services case study explains factors found to negatively impact successful BPMS adoption and use. The Alter work system’s framework and the Rosemann and vom Brocke core BPM elements were used as theoretical lenses to understand the case. The paper describes frustrations of an IT team trying to increase process agility with a BPMS in a large legacy application landscape. The main factors driving this frustration were the difficulty of integrating with other applications and staff bypassing design and code approval procedures. The impact of BPM strategy, culture and governance on BPM methods, resourcing and technology is explained. The paper presents an explanatory model which should be useful for practitioners wanting to adopt a BPMS. The BPM literature lacks empirical qualitative case studies and theoretical models and this paper aimed to contribute to both.

Using chatbots in digital factories, to interact with devices through instant messages and voice commands, can make the understanding of underlying manufacturing, logistic and business processes easier for workers. Intelligent chatbots can provide flexible conversations and tailor them to the specific users who are interacting. The iCHAT framework conceptually represents all the aspects related to a conversation, with different facets for the user, the conversation flow, and the conversation contents, and combining them to obtain a flexible interaction with the user. In digital factories, flexible production is driven by processes combining different services. In this paper, we present an original approach extending iCHAT to be able to chat about processes, aiming at instructing a worker about a process.

Today business processes often exceed organizational boundaries and the participants may not fully trust each other. In the past, this trust was guaranteed solely through legal contracts. To digitally establish trust without requiring a trusted third party, Blockchain technology together with Smart Contracts is used to ensure that involved organizations can not break their agreements. This experience report introduces a workflow enforcement method for a particular use case, the certification of the construction of industrial plants. The method comprises the modelling of the workflow, the verification of the models and the translation into Smart Contracts.

Knowledge-intensive Processes (KiPs) are processes characterized by high levels of unpredictability and dynamism. Their process structure may not be known before their execution. One way to cope with this uncertainty is to defer decisions regarding the process structure until run time. In this paper, we consider the definition of the process structure as a planning problem. Our approach uses automated planning techniques to generate plans that define process models according to the current context. The generated plan model relies on a metamodel called METAKIP that represents the basic elements of KiPs. Our solution explores Markov Decision Processes (MDP) to generate plan models. This technique allows uncertainty representation by defining state transition probabilities, which gives us more flexibility than traditional approaches. We construct an MDP model and solve it with the help of the PRISM model-checker. The solution is evaluated by means of a proof of concept in the medical domain which reveals the feasibility of our approach.

Dynamic controllability is the most general criterion to guarantee that a process can be executed without time failures. However, it admits schedules with an undesirable property: starting an activity without knowing its deadline. We analyze the specific constellations of temporal constraints causing such a sudden termination. Consequently, we introduce the somewhat stricter notion of semi-dynamic controllability, and present necessary and sufficient conditions to guarantee that a process can be executed without time failures and without sudden termination. A sound and complete algorithm for checking whether a process is semi-dynamically controllable complements the approach.

Mining real-life event logs results into process models which provide little value to the process analyst without support for handling complexity. Filtering techniques are specifically helpful to tackle this problem. These techniques have been focusing on leaving out infrequent aspects of the process which are considered outliers. However, it is exactly in these outliers where it is possible to gather important insights on the process. This paper addresses this problem by defining multi-range filtering. Our technique not only allows to combine both frequent and non-frequent aspects of the process but it supports any user-defined intervals of frequency of activities and variants. We evaluate our approach through a prototype based on the PM4Py library and show the benefits in comparison to existing filtering techniques.

We consider truncated traces, which are incomplete sequences of events. This typically happens when dealing with streaming data or when the event log extraction process cuts the end of the trace. The existence of truncated traces in event logs and their negative impacts on process mining outcomes have been widely acknowledged in the literature. Still, there is a lack of research on algorithms to detect them. We propose the Truncated Trace Classifier (TTC), an algorithm that distinguishes truncated traces from the ones that are not truncated. We benchmark 5 TTC implementations that use either LSTM or XGBOOST on 13 real-life event logs. Accurate TTCs have great potential. In fact, filtering truncated traces before applying a process discovery algorithm greatly improves the precision of the discovered process models, by 9.1%. Moreover, we show that TTCs increase the accuracy of a next event prediction algorithm by up to 7.5%.

Process mining is a family of techniques for analyzing business processes based on event logs extracted from information systems. Mainstream process mining tools are designed for intra-organizational settings, insofar as they assume that an event log is available for processing as a whole. The use of such tools for inter-organizational process analysis is hampered by the fact that such processes involve independent parties who are unwilling to, or sometimes legally prevented from, sharing detailed event logs with each other. In this setting, this paper proposes an approach for constructing and querying a common artifact used for process mining, namely the frequency and time-annotated Directly-Follows Graph (DFG), over multiple event logs belonging to different parties, in such a way that the parties do not share the event logs with each other. The proposal leverages an existing platform for secure multi-party computation, namely Sharemind. Since a direct implementation of DFG construction in Sharemind suffers from scalability issues, we propose to rely on vectorization of event logs and to employ a divide-and-conquer scheme for parallel processing of sub-logs. The paper reports on experiments that evaluate the scalability of the approach on real-life logs.

Literature in business process research has recognized that process execution adjusts dynamically to the environment, both intentionally and unintentionally. This dynamic change of frequently followed actions is called process drift. Existing process drift approaches focus to a great extent on drift point detection, i.e., on points in time when a process execution changes significantly. What is largely neglected by process drift approaches is the identification of temporal dynamics of different clusters of process execution, how they interrelate, and how they change in dominance over time. In this paper, we introduce process evolution analysis (PEA) as a technique that aims to support the exploration of process cluster interrelations over time. This approach builds on and synthesizes existing approaches from the process drift, trace clustering, and process visualization literature. Based on the process evolution analysis, we visualize the interrelation of trace clusters over time for descriptive and prescriptive purposes.

Process landscapes define the scope and relationships between an organization’s business processes and are therefore essential for their management. However, in contrast to business process diagrams, where nowadays BPMN prevails, process landscape diagrams lack standardization, which results in numerous process landscape designs. Accordingly, our goal was to investigate how intuitive are current landscape designs to users with low expertise, as well as users having expertise in BPMN and landscape modeling. A total of 302 subjects participated in the research showing that previous expertise impacts the interpretation of landscape elements and designs whereas, in the case of having contextual information, subjects responded more consistently. The results also show that the basic relationships between processes are intuitive to users, also in the case when only proximity between shapes is facilitated. Our findings may imply future designs of languages for process landscapes. They also may be useful for those who actually model process landscape diagrams and search for suitable notations.

Business services are offerings that enable organizations to achieve their strategic objectives by making their functionality accessible to their customers and business partners. Thus, organizations pay significant attention to and invest in the explicit identification and definition of their business services. This is, however, not a trivial endeavor as multiple concerns that are intrinsic to the concept of business service should be taken into consideration in identifying services. Existing business service identification methods used in isolation do not offer adequate coverage for these concerns. Addressing this issue, we propose a novel method assembled by situational method engineering from a set of existing service identification methods, taking the best aspects from each of them. In this paper, we present an instantiation of the situational method engineering approach alongside the details of the constructed method. We also provide a demonstration of the method with an illustrative scenario based on a real-life business case.

Context awareness in complex business environments has been recognized as a major challenge for enterprise information systems. Although, the development of a context aware system in different application domains is convincingly documented in current literature, the design of such systems requires greater attention. Particularly, investigating the context of a context aware system. In this paper, we present a step-wise method to model a complex business environment. The method provides an approach for investigating a context and using the investigation results in subsequent design steps.

Conversational interfaces (also called chatbots) are being increasingly adopted in various domains such as e-commerce or customer service, as a direct communication channel between companies and end-users. Their advantage is that they can be embedded within social networks, and provide a natural language (NL) interface that enables their use by non-technical users. While there are many emerging platforms for building chatbots, their construction remains a highly technical, challenging task.

In this paper, we propose the use of chatbots to facilitate querying domain-specific models. This way, instead of relying on technical query languages (e.g., OCL), models are queried using NL as this can be more suitable for non-technical users. To avoid manual programming, our solution is based on the automatic synthesis of the model query chatbots from a domain meta-model. These chatbots communicate with an EMF-based modelling backend using the Xatkit framework.

Organizations are operating within dynamic environments that present changes, opportunities and threats to which they need to respond by adapting their capabilities. Organizational capabilities can be supported by Information Systems during their design and run-time phases, which often requires the capabilities’ adaptation. Currently, enterprise modeling and capability modeling facilitate the design and analysis of capabilities but improvements regarding capability change can be made. This design science research study introduces a capability change meta-model that will serve as the basis for the development of a method and a supporting tool for capability change. The meta-model is applied to a case study at a Swedish public healthcare organization. This application provides insight on possible opportunities to improve the meta-model in future iterations.

Industry 4.0 is a concept that has attracted much research and development over the last decade. At its core is the need to connect physical devices with their digital representations which essentially means establishing a digital twin. Currently, the technological development of digital twins has gathered much attention while the organizational and business aspects are less investigated. In response, the suitability of enterprise modeling and capability management for the purpose of developing and management of business-driven digital twins has been analyzed. A number of requirements from literature are summarized and two industrial cases have been analyzed for the purpose of investigating how the digital twin initiatives emerge and what forces drive the start of their implementation projects. The findings are discussed with respect to how Enterprise Modeling and the Capability-Driven Development method are able to support the business motivation, design and runtime management of digital twins.

Digital enterprises and their networks increasingly rely on advanced decision-making capabilities, however, development of decision-making models requires significant effort and is often performed independently of other digitalization activities. Additionally, dynamic nature of many decision-making problems requires rapid ramp-up of decision-making capabilities. To addresses these challenges, this position paper proposes to elaborate a method for integrated on-demand decision modeling. The method combines mathematical programming and data analytics models to create case specific models on the basis of generic decision-making models. The integrated model and its data supply pipelines are configured using enterprise models allowing for consistent and rapid model deployment. The integrated model is intended for the trusted ICT supply chain configuration problem though it can be used for solving various types of decision-making problems. The main expected results are formulation of the new type decision-making model and the method for on-demand configuration of such models.

Microservice Architecture (MSA) is an approach to architecting service-based software systems, which aims for decreasing service coupling to enable independent service development and deployment. Consequently, the adoption of MSA is expected to particularly benefit the scalability, maintainability, and reliability of monolithic systems. However, MSA adoption also increases architectural complexity in service design, implementation, and operation. As a result, Software Architecture Reconstruction (SAR) of microservice architectures is aggravated. This paper presents a modeling method that systematizes SAR of microservice architectures with the goal to facilitate its execution. The method yields reconstruction models for certain architecture viewpoints in MSA to enable efficient architecture analysis. We validate the method’s applicability by means of a case study architecture and the assessment of its risk in technical debt using derived reconstruction models.

In the context of digital transformation, speeding up the time-to-market of high-quality software products is a big challenge. Main challenges. Software quality correlates with the success of requirements engineering (RE) sessions. RE sessions demand software analysts to collect all relevant material usually specified on written notes, flip charts, pictures, etc. Afterwards comprehensible requirements need to be specified for software implementation and testing. These activities are mostly performed manually, which causes process delays and software quality attributes like reliability, usability, comprehensibility, etc., are diminished causing software devaluation. Innovative aspects. This research idea paper proposes a framework for automating the tasks of requirements specification. The proposed framework involves computational mechanisms to enable the automatic generation of software design while requirements are discussed. The innovative aspect of this research comes from digitally transforming the software development life cycle (SDLC) where requirements are generated “on the fly” and virtual reality systems are in place. Potential to make change. The proposed framework has the potential to renovate the role of software analysts, which can experience substantial reduction of manual tasks, more efficient communication, dedication to more analytical tasks, and assurance of software quality from conception phases. This research idea paper introduces the framework for automating the task of requirements specification, and report our progress. We conclude the paper by outlining lessons learnt and future lines of work.

Nowadays, software is ubiquitous and present in almost everything we buy and use. Artificial intelligence (AI) is becoming prevalent in software products. The use of AI entices consumer inquisitiveness, promising software products that can make our lives easier, productive, and in some mission-critical applications safer. Similar reasoning can be applied to systems exploring Internet of Things, cloud services, and mobile technologies. However, there is a trust deficit when it comes to accepting AI as well as the other above-mentioned features, as a reliable technology platform. This paper argues that the more critical the domain is, the less consumers seem to trust software to make decisions on their behalf or even to be used. Aspects such as safety, privacy, and ethics challenges the perception of trustworthy computing. In the past two decades, several works have suggested that Corporate Social Responsibility (CSR) may play an essential role in creating a trust paradigm between customers and businesses promoting loyalty, customer retention and thus enhancing customer trust and increasing corporate profit. We believe that the software industry will need soon rather than later to encourage trust in their embedded software. A promising approach lies in adapting principles associated with CSR to guide the software development processes. Such an approach could help to achieve two goals: Deliver trustworthy software and, if desired, deliver socially responsible software. We believe that Non-Functional Requirements (NFR) will play a crucial role in this endeavor. This paper highlights a first approach to establishing a basic set of NFRs that should always be carefully considered when developing software, as to aim socially responsible software.

A prominent theoretical foundation for IT analysis, design and development is general ontology - a branch of philosophy which studies what exists in reality. A widely used general ontology is BWW (Bunge-Wand-Weber) – based on ideas of the philosopher and physicist Mario Bunge, synthesized by Wand and Weber. It is regarded as a major contribution to conceptual modeling, database design, data collection design and information quality, as well as theory of IT. At the same time, the ontology was founded on an early subset of Bunge’s philosophy and Bunge’s ideas have evolved since then. An important question, therefore, is: do the more recent ideas expressed by Bunge call for a new ontology? In this paper we conduct an analysis of research by Bunge aiming at addressing this question. We compare the constructs of BWW with what we call Bunge’s Systemist Ontology (BSO) – a new ontology based on broader and more recent ideas developed by Bunge. Informed by this comparison we offer suggestions for ontology studies as well as future applications of Bunge in conceptual modeling and other areas of IT.

Advances in human action recognition and interaction recognition enable the reliable execution of action classification tasks through machine learning algorithms. However, no systematic approach for developing such classifiers exists and since actions vary between domains, appropriate and usable datasets are uncommon. In this paper, we propose a reference method that assists non-experts in building classifiers for domain action recognition. To demonstrate feasibility, we instantiate it in a case study in the medical domain that concerns the recognition of basic actions of general practitioners. The developed classifier is effective, as it shows a prediction accuracy of 75.6% for the medical action classification task and of more than 90% for three related classification tasks. The study shows that the method can be applied to a specific activity context and that the resulting classifier has an acceptable prediction accuracy. In the future, fine-tuning of the method parameters will endorse the applicability to other domains.

The Process-Goal Alignment (PGA) modeling method is a domain-specific modeling language that aims to achieve strategic fit of the business strategy with the internal infrastructure and processes. To ensure the acceptance and correct understanding of PGA models by business-oriented end-users, an intuitively understandable notation is of paramount importance. However, the current PGA notation was not formally tested up to now. In the paper at hand, we apply an evaluation technique for testing the intuitiveness of domain-specific modeling languages to bridge that research gap. Based on an analysis of the tasks, we propose improvements to six elements of the initial PGA notation. Our research contributes a comprehensive description of the empirical modeling language evaluation, which enables the reproducibility of the evaluation procedure by the conceptual modeling community.

Customer Experience (CE) is often presented as a competitive battlefield in the new digital context. However, it is defined so broadly, so holistically, that companies find it challenging to improve it through well-defined projects with an impact analysis of the different changes that could be brought about. Enterprise Architecture Management (EAM) is supposed to be a suitable means to support the management of such transformation projects. However, the depth and disruptive nature of these changes raise multiple questions concerning the adequacy of EAM for Customer Experience Improvement (CEI). In current corporate practice, there seems to be no regular application of EAM as a central support service for CEI in the digital context. In this paper, we explore how EAM can support CEI and examine how digitalization transforms the customer experience. We further identify the required information inputs for these transformations. Based on this foundation, we identify content elements that EAM can provide by analyzing EAM meta-models. Comparing the requirements by CEI projects and the supply by EAM shows that EAM, in general, provides valuable inputs for organizational issues and roles but shows weaknesses when it comes to information about trends, contextual and environmental information.

Given its essential role in understanding, explaining and structuring digital innovation, we see the increased prevalence of the business model concept as a unit of analysis in IS research. In contemporary, fast-paced markets, business models are volatile in nature and should be continuously innovated to accommodate new customer needs and technology developments. Business model innovation can be considered as an iterative process to guide business models from ideation towards implementation, in which the proper evaluation of business model prototypes is essential. For this evaluation, we need normative guidance, tools and rules to understand the relative performance of a new business model design. In the early design phases, this implies dealing with high levels of uncertainty. A few techniques and methods have been proposed for this purpose, but these lack the formal basis required for systematical application and development of automated evaluation tools. As a novel approach, we have earlier proposed the application of linguistic summarization to support early-phase, soft-quantitative business model evaluation. In this paper, we focus on a structural formalization of this approach as the basis for the development of well-defined user guidelines and automated evaluation tools. In doing so, we bridge the existing gap between qualitative and quantitative business model evaluation. We demonstrate the formalization by means of a running case inspired by a real-world project in the highly dynamic urban mobility domain.