The Practice of Enterprise Modeling

The design of a visual modeling language demands for a large number of decisions to be taken, depending on the intended purposes of the language, the domain context, and the goals and requirements of different stakeholders who are the prospective users of the language. Methodical support for the design and choice of visual modeling languages plays an important role in Enterprise Modeling (EM), because EM strongly relies on the use of visual modeling languages for expressing human-understandable abstractions of complex domain contexts. However, existing research primarily discusses individual design aspects of visual modeling languages. The results of these studies partially overlap or contradict each other. The work at hand introduces an approach for systematically identifying and managing trade-offs between competing design recommendations, as well as for gaining an integrated multi-perspective view on requirements towards visual modeling languages. We demonstrate the feasibility of the approach by reconsidering some design decisions taken for the widely used Business Process Modeling and Notation (BPMN) language.

Business process management systems are used to orchestrate the activities in an organization. These information systems allocate resources to perform activities based on information that describes those resources and activities. It is widely recognized that resource allocation can be enhanced by considering resource characteristics during selection. However, little guidance is available that shows how such characteristics should be specified. Human ability is one such characteristic, with the advantage that it is well-defined in the Fleishman Taxonomy of Human Abilities. This paper presents a method that leverages the Fleishman taxonomy to specify activities and human resources. Those specifications are then used to allocate resources to activities during process run-time. We show how ability-based resource allocation can be implemented in a business process management system and evaluate the method in a real-world scenario.

Enterprise modeling (EM) is an established practice in many organizations, but the majority of stakeholders in organizations who produce content relevant for EM use drawing or presentation tools instead of formalized EM techniques. The model-like content of such drawings or presentations often is very valuable for enterprises which calls for a way of integrating it with “real” models and other structured knowledge sources in organizations. This paper investigates how the model-like content of Powerpoint presentations can be extracted and transformed to EM. The main contributions of the paper are (a) an approach for model extraction from Powerpoint, (b) identification of heterogeneities to be tackled during the extraction process and (c) a prototype implementation demonstrating the approach based on ADO.xx.

In the era of digitalization, IT landscapes keep growing along with complexity and dependencies. This amplifies the need to determine the current elements of an IT landscape for the management and planning of IT landscapes as well as for failure analysis. The field of enterprise architecture documentation sought for more than a decade for solutions to minimize the manual effort to build enterprise architecture models or automation. We summarize the approaches presented in the last decade in a literature survey. Moreover, we present a novel, machine-learning based approach to detect and to identify applications in an IT landscape.

Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider.

Gamification in businesses refers to the use of technology-assisted solutions to boost or change staff attitude, perception and behaviour, about the individual or collective goals and tasks. Previous research indicated that gamification techniques could introduce risks to the business environment, and not only fail to make a positive change, but also raise concerns about ethics, quality of work, and well-being in a workplace. Although the problem is already recognised in principle, there is still a need to clarify and concretise those risks, their factors and their relation to the gamification dynamics and mechanics. In this paper, we focus on gamification risks related to teamwork within the enterprise. To address this, we conducted three-stage empirical research in two large-scale businesses using gamification in their workplace, including two months’ observation and interview study. We outline various risk mitigation strategies and map them to primary types of gamification risks. By accomplishing such conceptualisation, we pave the way towards methods to model, detect and predict gamification risks on teamwork and recommend design practices and strategies to tackle them.

It is very important to understand system behaviors in collective pattern for each knowledge domain. However, there are structural limitations to represent collective behaviors due to the size of system components and the complexity of their interactions, causing the state explosion problem. Further composition with other systems is mostly impractical due to exponential growth of their size and complexity. This paper presents an abstraction method to model the collective behaviors, based on a new concept of domain engineering: behavior ontology. Firstly, the ontology defines each collective behavior of a system from active ontology. Secondly, the behaviors are formed in a quantifiably abstract lattice, called n:2-Lattice. Thirdly, a lattice can be composed with other lattices based on quantifiably common elements. The composition can be interpreted as behavioral composition, and can reduce all the unnecessary composition not related to the behaviors in the lattices. In order to demonstrate the feasibility of the method, two examples, Emergency Medical Service and Health Care Service systems, are selected and implemented on a Behavior Ontology tool, called PRISM, which has been developed on ADOxx Meta-Modelling Platform.

Enterprises of today are faced with rapidly changing technologies and customer needs within unpredictable environments that require a new mindset for creating an agile enterprise. Agile practices gained momentum within software development communities due to their speed-of-delivery and incremental value delivery. Yet, for software development projects at scale, theorists believe that stakeholders first need to have a common understanding of the enterprise operational context, sharing a common big picture as part of requirements elicitation. The design and engineering methodology for organizations (DEMO) encapsulates an organization construction diagram (OCD) that is useful for representing the enterprise operational context, i.e. removing unnecessary clutter of technology implementation detail. Theory indicates that abstract OCD concepts are concise and used in a consistent way. Yet, agile methodologies require models that encourage collaboration, are easy to understand and relate to a concrete world, rather than an abstract world. The main contribution of this article is to present a different means of introducing the OCD to software development stakeholders, relating abstract concepts of the OCD back to a concrete world. Using design science research, this study suggests and evaluates a story-card method that incorporates collaborative and easy-to-use technologies, i.e. sticky notes as story cards. Feedback from 21 research participants indicated that the story-card method indeed facilitated translation of a concrete world into more abstract (and concise) concepts of the OCD, also improving the possibility of adopting the OCD at an enterprise as a means to represent a common understanding of the enterprise operational context.

Knowledge-intensive Processes are difficult to support by traditional workflow oriented Business Process Management approaches. Reasons lie in in ad-hoc decisions and unpredictable workflows that come with them. Adaptive Case Management (ACM) is a paradigm for the management of knowledge-intensive processes that has recently drawn attention in industry and the scientific community. This development led to the standardization of CMMN as a notation for process models for the ACM implementation. This study assess the availability of method support for the use of ACM and the fitness of CMMN for fulfilling the modeling requirements in this context based on a systematic literature review. As a result missing method support, CMMN shortcomings as well as suggestions for the implementation of ACM in combination with CMMN are discussed.

Metamodels play a pivotal role in conceptual modeling as they manifest the abstraction level applied when creating conceptual models. Consequently, design decisions made by the metamodel developer determine utility, capabilities, and expressiveness of the conceptual modeling language - and eventually the created models. However, only limited research defines and applies metrics for analyzing the structure and capabilities of a metamodel, and eventually support the development of new metamodels. This not only concerns general-purpose modeling languages, but also domain-specific ones, which usually undergo shorter update cycles. The paper at hand introduces a generic analysis framework to syntactically analyze modeling languages. The framework is applied to 40 metamodels of domain-specific conceptual modeling languages (DSML). This research establishes a foundation to support metamodel development in the future. The contribution of this paper is threefold: (i) an analysis framework for conceptual modeling method metamodels is proposed, (ii) results from applying this framework to 40 ADOxx-based DSML metamodels are presented, and (iii) a human-based reasoning after comparison of these results with Ecore-based metamodels is conducted.

Ontology integration addresses the problem of reconciling into one single semantic framework different knowledge chunks defined according to its own ontology. This field has been subject of analysis and many consolidated theoretical results are available. Still, in practice, ontology integration is difficult in heterogeneous information systems (HIS) that need to integrate assets already built and running which cannot be changed. Furthermore, in practice, the composed assets are usually not really defined according to an ontology but to a data model which is less rigorous but fit for the purpose of defining a data schema. In this paper, we propose a method for integrating assets participating in a HIS using a domain ontology, aimed at finding an optimal balance between semantic rigour and feasibility in terms of adoption in a real-world setting. The method proposes the use of data models describing the semantics of existing assets; their analysis in order to find commonalities and misalignments; the definition of the domain ontology, considering also other sources as standards, to express the main concepts in the HIS domain; the connection of the local models with this domain ontology; and its abstraction into a metamodel to facilitate further extensions. The method is an outcome of a collaborative software development project, OpenReq, aimed at delivering an ontology for requirements engineering (RE) designed to serve as baseline for the data model of an open platform offering methods and techniques to the RE community. The construction process of this ontology will be used to illustrate the method.

Enterprise knowledge is currently subject to ever-changing, complex and domain-specific modeling requirements. Assimilating these requirements in modeling languages brings the benefits associated to both domain-specific modeling languages (DSMLs) and a baseline of well-established concepts. However, there are two problems that hamper the speed and efficiency of this activity: (1) the separation between the two key expertise: language engineering and domain knowledge, and (2) the sequential modeling language engineering life-cycles. In this work, we tackle these two challenges by introducing an Agile and Ontology-Aided approach implemented in our Modeling Environment - the AOAME. The approach seamlessly integrates meta-modeling and modeling in the same modeling environment, thus cooperation between language engineers and domain experts is fostered. Sequential engineering phases are avoided as the adaptation of the language is done on-the-fly. To this end, a modeling language is grounded with an ontology language providing a clear, unambiguous and machine-interpretable semantics. Mechanisms implemented in the AOAME ensure the propagation of changes from the modeling environment to the graph-based database containing the ontology.

Since the origin of the web, up to social networks, and now to the internet of things, the quantity of personal information produced and shared is uncontrollably increasing. Privacy regulations protect our right to have the control on our personal data. According to the recent General Data Protection Regulation (GDPR), entered into force in May 2018, infringements can be very costly to organizations, ranging from 10s to 100s of thousands of Euros. In order to ensure compliance with such regulations, privacy should be taken into consideration as early as at requirements time, so to avoid expensive after-the-fact fixes. Modeling frameworks have been proposed to support the analysis of requirements in complex socio-technical systems, however, even if a primary role is given to security, for privacy more work need to be done. In this paper, starting from the social concept of consent, we propose a modeling language and define the formal framework for the analysis of privacy-consent requirements. We report on our experience in the analysis of privacy in the medical domain, in the context of a research project with the Trentino health-care provider (APSS).

During enterprise transformations, coordination among the stakeholders involved is key. Shared understanding, agreement, and commitment, is needed on topics such as: the overall strategy of the enterprise, the current affairs of the enterprise and its context, as well as the ideal future affairs.Models, and ultimately enterprise modelling languages and frameworks, are generally seen as an effective way to enable such (informed) coordination. To this end, different languages and frameworks have been developed, including ArchiMate.

ArchiMate, which has evolved to become a widely accepted industry standard, was developed at a time where the digital transformation was not yet that noticeable. At that the time, the focus was more on consolidation and optimisation. As such, it is logical to expect that the existing ArchiMate language may require some “updates” to be ready for digital transformations. The objective of this paper is therefore threefold: (1) posit, based on practical experiences and insights, key challenges which the digital transformation puts on enterprise (architecture) modelling languages, (2) assess to what extent ArchiMate meets these challenges, and (3) provide suggestions on how to possibly improve ArchiMate to better meet these challenges.

At the doorstep of the 4th industrial revolution, standardization and digitalization are high on the agenda of many management boards. With technology having already outpaced organizational development, IT departments may become the engines of the current revolutionary sentiment if they master the transformation of their role and position in the corporate Operations Excellence efforts. CIOs are finding themselves between multiple disrupting fronts: the expectation to be a key driving force in the corporate transformation, while needing to transform their own organization to meet today’s challenges, at times when information technology solutions is becoming “everyone’s” business in the companies. In this journey the key success factor is the right approach to managing the demand from their customer: the business. This practitioner paper offers a response by reflecting on relevant models, describing an integrated approach and validates certain assumptions based on the experience of a pilot implementation of the Integrated Change Management model.

Modern enterprises operate in an unprecedented regulatory environment where increasing regulation and heavy penalties on non-compliance have placed regulatory compliance among the topmost concerns of enterprises worldwide. Previous research in the field of compliance has established that the manual specification/tagging of the regulations not only fails to ensure their proper coverage but also negatively affects the turnaround time both in proving and maintaining the compliance. Our contribution in this paper is a case study using a subset of European Union Regulation in the financial markets, namely, Money Market Statistical Reporting (MMSR) and that we validated it in the context of our model-driven semi-automated compliance framework. The novelty of the framework is the key participation of domain experts to author regulatory rules in a controlled natural language to enable compliance checking. We demonstrate transformation of regulations present in legal natural language text (English) to a model form via authoring of Structured English rules in the context of MMSR regulations for a large European bank. This generated regulatory model is eventually translated to formal logic that enables formal compliance checking contrary to current industry practice, that provides content management-based, document-driven and expert-dependent ways of managing regulatory compliance.

Enterprise Architecture is the process of translating business vision into strategy. Hence, Enterprise Modeling is the process of translating an organization’s strategic intent into mandated socio-technological innovation projects required to reach an agreed future state of the firm’s operation.

This paper uses four longitudinal case studies at DHL Express Europe to introduce a significant paradigm shift to modeling of the enterprise at the work system level as an abstract representation of the desired future state of the enterprise. This representation specifies in full the business capabilities serving current and future customer needs and the work system configuration enhancements needed to satisfy the customer needs. The associated project has four p steps: (1) Assessment of Status Quo, (2) Agreeing on the desired End-State, (3) Evolution of legacy work-systems, (4) Removal of Obsolete components. This process model explicitly includes the necessity to search for, address and remove all strategic, financial, operational and technical legacy issues identified during the baseline of the status quo. Enterprise Architects should be embedded in each implementation project for Project Assurance purposes in order to monitor the delivery of the agreed end-state.

Conceptual modelling remains a challenging topic for educators, as it concerns ill-defined problems and requires substantial amount of practice for reaching even the initial level of proficiency. Year after year, novice modellers tend to make similar errors when learning to design models and some of those errors become persistent even at the higher level of proficiency. Are these errors the unavoidable “necessary evil” or there is a possibility to address them at the very early stage of a modeller’s education? In this work, we examine a novel approach to teaching conceptual modelling by identifying the most frequent errors in students’ models and introducing error-based step-by-step exercises in the framework of a Small Private Online Course for university students.

Conceptual modeling is an important part of Enterprise Modeling, which is a challenging field for both teachers and learners. Creating conceptual models is a so-called ‘ill-structured’ task, i.e. multiple good solutions are possible, and thus students can follow very distinct modeling processes to achieve successful learning outcomes. Nevertheless, it is possible that some principles of modeling behavior are more typical for high-performing rather than low-performing students, and vice versa. In this study, we aimed to discover those patterns by analyzing logged student modeling behavior with process mining, a set of tools for dealing with event-based data. We analyzed data from two individual conceptual modeling assignments in the JMermaid modeling environment based on the MERODE method. The study identified the presence of behavioral patterns in the modeling process that are indicative for better/worse learning outcomes, and showed what these patterns are. Another important finding is that students’ performance in intermediate assignments is as well indicative of their performance in the whole course. Thus, predicting these problems as early as possible can help teachers to support students and change their final outcomes to better ones.

Today’s enterprises and their underlying information systems ask for Multi-view Enterprise Modeling Methods (MVMMs) toward a comprehensive model representation. MVMMs capture the required aspects of complex systems using multiple views – the Multi-view Modeling (MVM) capability. However, not all modeling methods are endowed with a MVM capability. Means for assessing and improving such capability are therefore needed. Based on a comparative analysis of three MVMMs, we define the notion of MVM capability. Drawing on these criteria, an EBNF-based description is proposed, serving as a basis for MVM capability assessment. The strengths of the approach go beyond offering a common understanding of the MVM capability notion by (i) assessing the MVM capability, and (ii) identifying requirements to achieve this capability. Consequently, this approach primarily addresses method engineers aiming to employ MVM capability to a modeling method.

For the past three decades, enterprise modelling (EM) has been emerging as a significant yet complex paradigm to tackle holistic systematic enterprise analysis and design. With a high fluctuation in the global economy, industrial stability and technology shift, the necessity of such paradigms becomes crucial in determining the decisions that an enterprise can make for surviving in such a highly dynamic business ecosystem. EM practices have focused for a long time, on the design-time of enterprise systems. Recently, there has been a rapid development in data analytics, machine learning and intelligent systems from which an EM platform can benefit. EM needs to cope with the new changes in both business and technology; it should also help architects to determine optimum decisions and reduce complexity in technical infrastructure. In this paper, the author discusses several challenges facing enterprise modelling practices and offers an architectural notion for future development focusing on the requirements of a platform that can be called intelligent and adaptive.

OntoREA© is a specification of the Accounting and Finance domain in the OntoUML language [1]. In a previous article [2] the authors use a forward contract financial derivative instrument to demonstrate the validity of the OntoREA© model within the design science research methodology (DSRM) [3]. A forward contract does not change over time and therefore can be modelled as static hedge portfolio composition. However, it is of interest if the OntoREA© model can also hold true for dynamic hedge portfolio compositions, as induced by option contract financial derivative instruments. This article investigates on that and delivers proof that the OntoREA© model is suitable for option contracts as well. Through adequately refining the platform specific database model (PSM) the policy’s dynamic nature can be demonstrated. Moreover, including a Plan/Do/Check/Act (PDCA) process model for the specification of the option contract replication also demonstrates the information processing in the REA accounting infrastructure. The proposed approach is implemented into an R/Shiny software prototype where the 3-tier-architecture is used to integrate the database and the PDCA process model at the R/Shiny implementation specific model (ISM) level. The presented hedge portfolio representation of derivatives can be useful for business analysts in the finance and accounting domain as well as for teaching financial derivative instruments.

In this practice paper, we report the outcomes of a case study in a new Dutch hospital, where enterprise architects are working toward a ‘lean’ and ‘simplified’ EA model to align existing IT systems to new requirements. The objective of the case study was to examine if the developed EA model could support architects in selecting components of an existing IT infrastructure for re-use, with regard to radically new requirements. We have developed an EA model in close collaboration with enterprise architects. This study reflects on the use of this model in the hospital. The approach combines analysis of the content in the model, a study of documents in the organization, and communication with the architects. We signal that the existence of an integrated suite for an Electronic Health Record system largely determined how the model was used. Reflection disclosed that a lack of information on requirements and applications, as well as low adaptability of existing systems, negatively affected the flexibility of IT in the organization.

Many organizations mistakenly or inadvertently focus on tactical aims rather than on strategic goals. “Strategy” commonly denotes long-term objectives and high-level policies while “tactic” refers to deployment concerns and implementation considerations. By focusing on lower-level objectives an organization can potentially overlook or neglect better ways of achieving higher-level goals. Shifting from a short-term to a long-run orientation can be considered a type of pivoting, as the structure and relationships of an organization are substantially reconfigured. The Larger Goal pivot is essential when lower-level options for achieving a higher-level organizational goal are either unavailable or insufficient. It entails shifting focus to a larger or higher goal and exploring strategic alternatives to satisfy that goal. In this paper we present conceptual models of the Larger Goal pivot based on a historic example from Netflix – a movie streaming service.