Abstract: The capability to operate cloud-native applications can generate enormous business growth and value. But enterprise architects should be aware that cloud-native applications are vulnerable to vendor lock-in. We investigated cloud-native application design principles, public cloud service providers, and industrial cloud standards. All results indicate that most cloud service categories seem to foster vendor lock-in situations which might be especially problematic for enterprise architectures. This might sound disillusioning at first. However, we present a reference model for cloud-native applications that relies only on a small subset of well standardized IaaS services. The reference model can be used for codifying cloud technologies. It can guide technology identification, classification, adoption, research and development processes for cloud-native application and for vendor lock-in aware enterprise architecture engineering methodologies.

Abstract: The Internet of Things is getting more and more traction, nevertheless, state-of-the-art approaches only focus on specific aspects, like the integration of heterogeneous devices or the processing of sensor data emitted by these devices. However, such domain-specific approaches slow the adoption rate of the Internet of Things, because users need to select and integrate different approaches in order to build a solution that fits all their requirements. To resolve this shortcoming, we have designed and implemented the VISP ecosystem, which provides a holistic approach for elastic data stream processing in Internet of Things scenarios by supporting the complete lifecycle of designing, deploying, and executing such scenarios. VISP further tackles the challenges of data privacy as well as software reuse, including monetization aspects in today's service landscapes. This paper analyzes challenges for creating solutions for the Internet of Things, presents the VISP ecosystem, and discusses its applicability for use case specific data stream processing topologies.

Abstract: The number of publications, along with the organization of new conferences are a couple of the relevant elements that usually indicate the progress of an area of study over the years. This is definitely true in the case of the Enterprise Architecture (EA) discipline, which went from having its first journal article published in 1989 to over two hundred published articles by 2015. But in spite of this evolution, EA is still suffering from a considerable lack of common understanding. It has become very important to investigate the current state of affairs concerning the EA discipline through its relevant publications in order to shed some light on this challenge. 171 journal papers published between 1990 and 2015 were systematically selected and examined in order to accomplish this investigation. The quantitative and qualitative findings of this examination show that EA is a young discipline which raises a growing interest in recent years. This examination also confirms the lack of common understanding in EA, which can be observed in the different descriptions of the term "enterprise architecture," and in the diversity of perspective with regards to the whole discipline. Several issues related to this lack has been reported, such as multidisciplinary issue, language issue, structure of research and mode of observation issues. The major issue concerns the absence of enough research to shed some light on this challenge. In addition to this investigation, helpful directions for future research in this area was proposed.

Abstract: Enterprise architecture modeling and model maintenance are time- consuming and error-prone activities that are typically performed manually. This position paper presents new and innovative ideas on how to automate the modeling of enterprise architectures. We propose to view the problem of modeling as a probabilistic state estimation problem, which is addressed using Dynamic Bayesian Networks (DBN). The proposed approach is described using a motivating example. Sources of machine-readable data about Enterprise Architecture entities are reviewed.

Abstract: Decision models are usually created to complement business process models and to separate them from additional information regarding the decision-making. Like other conceptual models, their purpose is to exceed the representing capabilities of a textual information representation by providing human readers a more visually expressive and cognitively effective form of representation, and if applicable, to allow for future automation. This paper presents findings from a cognitive analysis on the conformity of the newly specified DMN standard notation with principles for effective visual design. While the principle of semiotic clarity and visual expressiveness appeared to be mostly satisfied, visual expressiveness and perceptual discriminability were perceived as partly violated. It was assumed that the DMN notation satisfies the principles of complexity management and cognitive integration. The goal of this first qualitative analysis is to lay the foundation for follow-up, empirical investigations to investigate these ratings.

Abstract: Business processes involve several perspectives that have an effect in all the phases of the business process management lifecycle. The organisational perspective addresses the way in which human resources take part in process activities. Human resources are of utmost importance as they are responsible for the correct execution of processes. However, the organisational perspective has received less attention than others and the existing support is limited. With the aim of easing the development of advances in human resource management in business processes, in this paper we present a novel framework that collects several aspects to be considered along with the existing support.

Abstract: Modern enterprises face an unprecedented regulatory regime. Traditional compliance practices in enterprises rely heavily on domain experts whose judgement determines what compliance means and how to reflect regulations onto the enterprise processes and data to make them compliant. These activities are mostly manual in nature. We present a machine learning approach to modeling compliance. Our key innovations are a) use of active learning- a semi-supervised system capable of learning interactively from the domain expert to identify regulations and b) informing the feature representation of the active learner based on domain- specific entities and relations to effectively build a domain model of regulations. Early results show that our system reduces the burden on domain experts to a large extent, enables latching domain expert's knowledge, and makes further steps in compliance easier by the use of models.

Abstract: Knowledge workers already face a broad range of tools to support their work, e.g. adaptive case management systems, tailored information systems, groupware, and other (process) support systems. Case data is scattered across many systems, and the overlapping structured, semi-structured, and ad-hoc processes involved further impede keeping track of related data and activities. Organizations are socio-technical entities, and interactions have significant impact on their success. Today, around 50% of the work in the US is knowledge work, and other countries show a similar tendency. Improving integration of appropriate tools for knowledge work and augmenting support for interactions therefore offers to increase productivity in a very influential part of the workforce. Knowledge workers are well aware of the pragmatic intention of their communicative acts, but currently their systems are not. We suggest to use Speech Act Theory to enable useful inferences and to improve integration of the various tools for knowledge work. A focus on interactions raises awareness for the pragmatic intention and commitments in particular. It can help providing line markings for knowledge workers by facilitating compliance monitoring for interactions and artifacts stemming from many participating systems and manual documentation. Interactions already tie many separate systems together, and standardizing as well as partially automating them can therefore further simplify integration. Speech-act-based adaptive case management offers to increase process transparency, enable useful inferences, and integrate structured, semi-structured, and ad-hoc processes.

Abstract: Collaboration in business processes and projects requires a division of responsibilities among the participants. Version control systems allow us to collect profiles of the participants that hint at participants' roles in the collaborative work. The goal of this paper is to automatically classify participants into the roles they fulfill in the collaboration. Two approaches are proposed and compared in this paper. The first approach finds classes of users by applying k-means clustering to users based on attributes calculated for them. The classes identified by the clustering are then used to build a decision tree classification model. The second approach classifies individual commits based on commit messages and file types. The distribution of commit types is used for creating a decision tree classification model. The two approaches are implemented and tested against three real datasets, one from academia and two from industry. Our classification covers 86\% percent of the total commits. The results are evaluated with actual role information that was manually collected from the teams responsible for the analyzed repositories.

Abstract: The aim of process discovery is to build a process model from an event log without prior information about the process. The discovery of declarative process models is useful when a process works in an unpredictable and unstable environment since several allowed paths can be represented as a compact set of rules. One of the tools available in the literature for discovering declarative models from logs is the Declare Miner, a plug-in of the process mining tool ProM. Using this plug-in, the discovered models are represented using Declare, a declarative process modelling language based on LTL for finite traces. In this paper, we use a combination of an Apriori algorithm and a group of algorithms for Sequence Analysis to improve the performances of the Declare Miner. Using synthetic and real life event logs, we show that the new implemented core of the plug-in allows for a significant performance improvement.

Abstract: Traditional business process modeling languages use an imperative style to specify all possible execution flows, leaving little flexibility to process operators. Such languages are appropriate for low-complexity, high-volume, mostly automated processes. However, they are inadequate for case management, which involves low-volume, high- complexity, knowledge-intensive work processes of today's knowledge workers. OMG's Case Management Model and Notation (CMMN), which uses a declarative style to specify constraints placed at a process execution, aims at addressing this need. To the extent that typical case management situations do include at least some measure of imperative control, it is legitimate to ask whether an analyst working exclusively in CMMN can comfortably model the range of behaviors s/he is likely to encounter. This paper aims at answering this question by trying to express the extensive collection of Workflow Patterns in CMMN. Unsurprisingly, our study shows that the workflow patterns fall into three categories: 1) the ones that are handled by CMMN basic constructs, 2) those that rely on CMMN's engine capabilities and 3) the ones that cannot be handled by current CMMN specification. A CMMN tool builder can propose patterns of the second category as companion modeling idioms, which can be translated behind the scenes into standard CMMN. The third category is problematic, however, since its support in CMMN tools will break model interoperability.

Abstract: Cloud computing is becoming the predominant mechanism to seamlessly deploy applications with special requirements such as massive storage sharing or load balancing, usually provided as services by cloud platforms. A developer can improve the application's delivery and productivity by following a multi tenancy approach, where variants of the same application can be quickly customized to the necessities of each tenant. However, managing the inherent variability existing in multi-tenant applications and, even more importantly, managing the evolution of a multi-tenant application with hundreds of tenants and thousands of different valid architectural configurations can become intractable if performed manually. In this paper we propose a product line architecture approach in which: (1) we use cardinality-based variability models to model each tenant as a clonable feature, (2) we automate the process of evolving the multi- tenant application architecture, and (3) we demonstrate that the implemented process is correct and efficient for a high number of tenants in a reasonable time. We use a running case study in the domain of medical software.

Abstract: Typical business process management studios provide support for process design through generic languages such as BPMN. This brings several shortcomings related to process governance over time, process ambiguity and complexity for non-technical users. Domain-specific process languages have the potential to correct these issues but they require strong enterprise tool support and integration in order to be successfully adopted. This paper proposes a mechanism for generating intuitive yet feature-rich graphical process studios for various business domains that are fully integrated with standard business process management solutions. It reduces the need for costly development and maintenance of such studios while ensuring that business users have consistent access to the ever-evolving enterprise body of knowledge. The approach uses model-based transformations to generate and support the entire infrastructure required by the studios. This includes the graphical user interface, the conversion capabilities to and from BPMN, embedding of real-time monitoring data from business process engines and service oriented platforms, live multi-user collaboration support, process governance and evolution, domain know-how management, as well as service-level agreement monitoring. The approach has been fully prototyped and integrated with enterprise-level tools and platforms.

Abstract: Enterprise architecture (EA) network analysis has been attracting researchers' attention lately. The main source of information is the structural components, including the relations among them and how they might be structurally arranged. These relations are studied to generate valuable information for EA professionals. However, to the best of our knowledge, ours is the first attempt to combine structural information with a second source of information: expert's tacit knowledge. We believe combining these sources employing two new methods - what we call cognitive-structural diagnosis analysis and attribute check analysis - can refine the expert's knowledge about the architecture. To demonstrate these methods' feasibility, we apply them with two application architecture datasets collected in two different organizations. We also offer a classification schema for enterprise architecture network analysis at the component level, our focus. Our conclusions indicate that the cognitive- structural diagnosis analysis method minimizes analysis subjectivity while validating important components and also suggesting important structural ones to be further analyzed by experts. The attribute check analysis offers further contributions by helping in the investigation of particular attributes of applications in important architectural positions.

Abstract: ArchiMate is a widely adopted enterprise architecture modeling language that includes the "service" construct as a key structuring element across its enterprise layers. A previous analysis of the use of this construct within ArchiMate's business layer concluded that it fails to represent some important social aspects associated with the dynamics of service relations, which led to recommendations for improvements in the form of modeling patterns with focus on the business layer of ArchiMate. In this paper, we extend that analysis to consider also service relations in the application and technology layers. We explore the importance of addressing two complementary views for service modeling: the capability-based and the commitment-based views. As a result, a more comprehensive modeling strategy for service relations in ArchiMate is proposed; this strategy is able to reflect business models that employ the service notion, including software-as-a-service (S aaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS). We use a reference ontology for services (UFO-S) to support our analysis.

Abstract: Although EA principles have received considerable attention in recent years, there is still little known about how EA principles can be used to govern the transformation of the Information Systems enabled organization. In this research-in- progress paper, we communicate our initial step towards answering the sub-question: how do enforcing EA principles contribute to IS-enabled OT? Based on a comprehensive literature review, we initially propose five testable hypotheses and a research model, which is a pre-requisite to developing a data-driven theory for this important area of research. It is anticipated that the ensuing theory will provide a basis for further research studying the impact of EA on IS-enabled OT. The tested research model will also provide guidance to practitioners on how to effectively design and use EA principles in managing transformative changes caused by IS within their organizations and overall industry sectors.

Abstract: This paper presents a software toolkit that can be used to analyze event data streams in real-time. It has a specific focus on stochastic analysis of business processes, based on event data that is produced during the execution of those processes. The toolkit provides a software environment that facilitates easy connection to event data streams and quick development and testing of analysis and visualization techniques. It is developed by classifying existing techniques for streaming process data analysis, which are identified in the current literature, and by extracting and formalizing the core mechanisms that these techniques are based on. These core mechanisms serve as the basis for the toolkit. The toolkit is implemented and made available as open source. In this way it can facilitate quick prototyping of streaming process data analysis techniques.

Abstract: Artifact-centric workflows describe possible executions of a business process through constraints expressed from the point of view of the documents exchanged between principals. A sequence of manipulations is deemed valid as long as every document in the workflow follows its prescribed lifecycle at all steps of the process. So far, establishing that a given workflow complies with artifact lifecycles has mostly been done through static verification, or by assuming a centralized access to all artifacts where these constraints can be monitored and enforced. We present in this paper an alternate method of enforcing document lifecycles that requires neither static verification nor single-point access. Rather, the document itself is designed to carry fragments of its history, protected from tampering using hashing and public-key encryption. Any principal involved in the process can verify at any time that a document's history complies with a given lifecycle. Moreover, the proposed system also enforces access permissions: not all actions are visible to all principals, and one can only modify and verify what one is allowed to observe.

Abstract: Many enterprise systems are document intensive that requires extensive manual verification in the form of maker and checker. However, a maker-checker based verification raises several challenges with respect to increase in cost and time of verification. Furthermore, any manual labor intensive verification is not free from human oversight and can lead to costly errors. Therefore, to alleviate the challenges arising out of human verification of document intensive systems, we propose a rule based framework that enables automatic verification of document based systems. The framework uses ontology based knowledge representation techniques along with appropriate natural language processing methods to extract operational rules from business documents and then use suitable reasoning engine for verification. The above framework is validated in the light of a real life case study namely International Trade that deals with several critical financial documents like Letter-of-Credit, Bill-of-Lading, Commercial Invoice etc.

Abstract: A major approach for formalizing business policies or compliance rules (e.g., stemming from regulatory laws or standards) are behavioral constraints. Flexible business process management approaches such as Adaptive Case Management provide business users the necessary freedom to react to unforeseeable circumstances by ad-hoc changes, but behavioral constraints are often defined and maintained on a technical level which is inaccessible for business users. Consequently, long update cycles of these constraints might result in the enactment of obsolete, incomplete or faulty constraints which hinder the work of the business user instead of supporting it. In this paper, an ontology-based approach for defining and maintaining behavioral constraints in the context of flexible business processes is proposed. The approach aims at enabling business users to take active part in the creation and maintenance of behavioral constraints. The practical applicability of the approach is discussed by means of a realistic scenario on compliance in the context of renovation, repairs, and maintenance of buildings.

Abstract: Assembly processes are a particular class of production processes that have gained increasing importance as to their impact on flexibility and efficiency of the overall process. Assembly processes are characterized by high variability concerning the type and logical flow of tasks to be performed, the devices and tools used, the materials and information to be processed. Due to the complexity of such processes a relatively high ratio of tasks is still performed by human workers. Keeping track of materials, devices and tools used and at the same time providing digital assistance for increasingly complex assembly tasks requires adequate modeling techniques as a prerequisite for respective systems design. In this paper we discuss challenges for modeling assembly processes based on a real-world assembly line from our learning factory lab. Finally, we propose an architecture for a process-driven assistive assembly system which shows the interplay between process based monitoring, task assistance and workplace sensors.

Abstract: Enterprise architecture management (EAM) is a prominent discipline that aims at guiding decisions in local information systems (IS) investments toward organization-wide objectives. Due to shortcomings resulting from the guidance of EAM as a strong hierarchical, top-down driven coordination practice, scholars have recently introduced the concept of architectural thinking. Complementary to top-down driven coordination, architectural thinking aims at local decisionmakers for applying collectivistic considerations in their decisions and hence guiding IS endeavors beyond local utilities. Yet, the question of how to enable and foster this collectivistic orientation remains unanswered. Inspired by stewardship theory, this research conceptualizes a collectivistic-oriented decisionmaker by the means of motivation. A literature review is conducted for identifying and exploring pertinent motivation mechanisms that foster the adoption of a collectivistic orientation among decision-makers, enriched with focus group data. To this end, five groups of situational and psychological mechanisms are reported. These findings set out a guidance for prospective EAM research in approaching architectural coordination through a collectivistic orientation in decision-making.

Abstract: Current Adaptive Case Management (ACM) solutions are strong in flexibility, but business users must still meet compliance rules stemming from sources such as laws (e.g., Sarbanes-Oxley Act), standards (e.g., ISO 45001) and best practices (e.g., ITIL). This paper presents a framework on how to enable support for compliance in the context of ACM by constraints. Since ACM applications undergo constant change, there must be ways to introduce compliance rules on the fly. Currently, constraints (and similar alternative solutions) are predominately maintained by technical users, which results in long maintenance cycles. Our framework aims at enabling faster adoption of changing compliance requirements, both explicitly by enabling non-technical users (knowledge workers) to define and adapt constraints, and implicitly by learning from the decisions taken by other knowledge workers during case enactments. The former is achieved by supporting domain knowledge, maintained in an ontology. The latter is supported by a recommendation approach that enables an automated knowledge transfer between knowledge workers by propagating tacit knowledge, best practices, and the handling of constraints and their violations.

Abstract: This paper studies the effects of applying process improvement principles to knowledge-intensive processes, which are typical for being supported by Adaptive Case Management systems. The study is being completed by investigating a process of conducting a small-scale research project that results in BS or MS thesis being defended and graded. The research is a mixture of the case study at the department with which both authors are affiliated and reflections on own experience as active participants of the process. The preliminary results show that the improvement principles applied in the project have helped to smoothen the process and improve its outcome in terms of producing defendable theses that satisfy the requirements set by the relevant authority. However, it seems they might not positively affect other outcomes, like the size and quality of research contribution.

Abstract: We analyze the enterprise architecture management (EAM) 'journey' of the Swiss Federal Railways over the last twenty years. Fundamental organizational changes were matched by shifts of EAM's focus from advocating an enterprise-wide perspective over developing the enterprise architecture toolbox to establishing business transformation support. Beyond maturity considerations, insights from this longitudinal case study can be gained from an institutional perspective, i.e., by describing the EAM journey not only as a process of establishing the EAM function, but also as a process that extends EAM effects beyond the boundaries of IT. We identify four principles that guided this process: (1) Consistency of norms and values (2) Focus on reinventing rather than maturing (3) Picking the right EAM 'battles', and (4) Playing on EAM's holistic perspective.

Abstract: We propose a data-centric three leveled modeling architecture in an effort towards a Model Driven approach of services for the Internet of Things (IoT): a resources level, an artifacts level and a semantic level. In this architecture, the resources level abstracts all important pieces of information describing real objects as resources. The artifacts level allows to collect all objects and contexts information necessary for the execution of a given service. The semantic level introduces semantic notions to the architecture. So, data and actions are named in a standardized naming and the rules facilitate the interaction of the system with the non-expert users. We llustrate our architecture on a small example in which we present all three levels.

Abstract: The importance of Modelling Method Engineering is equally rising with the importance of Domain Specific Languages and individual modelling approaches. In order to capture the most relevant semantic primitives that address domain specific needs, it is necessary to involve both the method engineers as well as domain experts. Based on practical experience in business, more than twenty EU-projects and other research initiatives, this paper presents improved and extended version of a model-driven approach to support the design of a modelling method. The approach is evaluated by two projects in the context of e-Learning, and Business and IT-Cloud Alignment. The paper discusses the evaluation results and derived outlooks.

Abstract: Innovations are essential if companies are to maintain and increase their competitiveness. Thus, companies face the question how rather than whether to generate innovations. One possible parameter to design an innovation-friendly environment is providing appropriate software such as enterprise social software (ESS). This motivated us to explore how the use of ESS may impact the innovation process. We present results deduced from an exploratory case study. Based on 26 interviews and theoretical considerations about the innovation process, our findings reveal that ESS has the potential to support the different stages of the innovation process. Furthermore, the results indicate that ESS usage creates impacts concerning (1) the information processing itself, (2) daily work outputs, and (3) measurable outcomes at the organizational level. Here, particularly the generic processes in the knowledge and implementation stages stand to benefit.

Abstract: The Internet of Things, Enterprise Social Networks, Adaptive Case Management, Mobility S ystems, Analytics for Big Data, and Cloudenvironments are emerging to support smart connected i.e. digital products and services and the digital transformation. Biological metaphors of living and adaptable ecosystems provide the logical foundation for self- optimizing and resilient run-time environments for intelligent business services and related distributed information systems with service-oriented digitization architectures. We are investigatingmechanismsforflexibleadaptationandevolution of information systems with digital architecture in the context of the ongoing digital transformation. Our aim is to support flexibility and agile transformation for both business and related information systems through adaptation and dynamical evolution of their digital architectures. The present research paper investigates mechanisms of decision analytics for digitization architectures, putting a spotlight to Internet of Things architectures, by extending original enterprise architecture reference models with digitization architectures andtheirmulti- perspective architectural decision management.