In the realm of artificial intelligence (AI) and machine learning (ML), the scarcity of robust and diverse datasets often poses a significant challenge, prompting the need for effective data generation methods. This paper presents an evaluation of tabular data generation techniques on the DaFne platform, centered around a predictive maintenance case study for bridges. The DaFne platform offers a variety of tabular data generation functionalities, including rule-based creation, data fusion (with weather data), and data reproduction. We investigate the utility of these functionalities across different machine learning models for the prediction of bridge conditions. Our analysis includes a descriptive statistical comparison of real and synthetic data. Additionally, we explore the utility of original, weather, and synthetic datasets. We do this through the lens of ML models like MLR, XGBoost, CNN, and GRU, performing a predictive maintenance algorithm on these datasets. Our results indicate that while the inclusion of weather data did not significantly enhance predictive performance, the synthetic dataset shows satisfactory quality. However, the synthetic data's performance is lower than the original data in predictive maintenance tasks, with differences observed in models heavily reliant on sequential data. This research underscores the potential of the DaFne platform in generating high-quality synthetic data. It also highlights areas for future improvement and offers valuable insights for advancing data generation and analysis techniques in predictive maintenance and other AI applications.

In the planning of smart cities, machine learning models can support decision-making with intelligent insights. But what data sets should training processes be based on if there is not yet a city from which to collect data, or if data is not usable due to privacy issues? Synthetic data can provide a realistic representation of conditions in the city not only for machine learning experts, but also for smart city experts. For data-affine users, there are machine learning-based methods for generating synthetic data, but these have limited accessibility to data amateurs. The Platform Data Fusion Generator (DaFne) project aims to improve the usability of data generation methods for various professions. The platform with its generic functionalities should appeal to users from all domains. This paper refers to the research on how smart-city use cases can be addressed and how complex machine learning based methods can be made accessible through the platform to urban professions. Based on results from user interviews and experiments of a smart city case study, the need for a non-generic platform feature emerges. The Use Case Explorer feature provides users with a simple interface to query pre-trained machine learning models to generate data for a specific use case.

We identified two use cases for machine learning (ML) in the process of handling insurance claims related to water damage. The first use case is a classification task and concerns the decision, whether or not to send an expert to review the water damage. The second use case is a regression task and involves estimating the initial reserve to declare for the claim. We compared the performance of different ML models for both use cases. Concerning both the classification and regression task, a neural network (NN) with four layers and about 9,000 parameters performed the best. On the classification task, the NN has a precision of 0.8093. On the regression task, the NN has a mean absolute error (MAE) of 1,799 EUR and outperforms human estimations, which have an MAE of 2,258 EUR.

In the context of turnaround management, key performance indicators (KPIs) are needed to illustrate the effects of measures taken during the turnaround process. These KPIs are usually scattered over different data sources in the company. This paper describes a pattern construct conceived to relate KPIs to both their data source and the measure effects they are supposed to document. The pattern may serve as a basis for generally relating corporate KPIs to their data sources.

Analyzing and understanding large-scale, distributed business applications and enterprise architectures is particularly important to align an enterprise's overall business with the supporting IT infrastructure. It is necessary to model and simulate the dynamic complexity properly as well as capture possible emergent effects in real-world enterprise architectures and process landscapes. So technologies and methods that extend static analyses, like delivered by typical enterprise architecture tools, are needed. This work proposes an approach for such dynamic analyses in which agent based modeling and simulation is utilized through the MARS System, a cloud-based massive multi-agent system. The system continuously simulates the operation and transformation of the application landscape in order to support advancing business scenarios. Agents in this approach may represent any entity. They may be customers and suppliers as well as users and stakeholders of the business application, but may also represent any internal component of the enterprise system like servers, services or certain applications. MARS is an MSaaS (Modeling & Simulation as a Service) system, which enables users to setup and control complex, distributed massive multi-agent simulations from within a web application. Models can make use of GIS data and integrate it with arbitrary information from other sources, thus creating a multi-layer environment which depicts the whole business application landscape. This paper presents the current state and possibilities of the MARS system and discusses an example model to showcase the agent-based simulation approach and its possible advantages for dynamic and continuous enterprise architecture analysis.

Neue Modelle fur digitale Unternehmensarchitekturen mit Big Data, Services & Cloud Computing, mobilen Systemen, Internet of Things sowie Industrie 4.0 Okosystemen machen eine enge Kooperation verschiedener Partner aus Wissenschaft, Anwendungsunternehmen, offentlichen Organisationen, Softwarehersteller und IT- Dienstleister notwendig. Ziel dieser Zusammenarbeit ist die Zusammenfuhrung neuer Konzepte und Moglichkeiten der Informationstechnologie zur bestmoglichen Unterstutzung sich verandernder Unternehmensziele und -strategien. Software- und Unternehmensarchitekturen spielen hierbei eine zentrale Rolle. So werden Anforderungen bezuglich Flexibilitat und Agilitat in digitalen Unternehmen wesentlich durch serviceorientierte Ansatze unterstutzt. Der Ordnungsgrad und die kosteneffiziente Gestaltung komplexer IT-Landschaften soll durch Digital Enterprise Architecture Management deutlich verbessert werden – passend zu neuen Moglichkeiten von Services & Cloud Computing, Big Data, sowie kollaborativen Geschaftsprozessen.

In present Enterprise Architecture Management there is a conceptual gap between very complex methodologies on the one hand and usually methodology-agnostic query-based tool support on the other hand. As a result, Enterprise Architecture Management is often unable to tap its full potential. Issues for possible improvement are described in this paper. We postulate the hypotheses that the indicated deficiencies can be corrected by using EAM tools which consider and model EAM methodologies and their underlying activities as processes and are able to actively manage, steer and support these processes. Initial architectural considerations for such a tool as well as a corresponding research roadmap are presented.

Within the energy domain, the manifold term service gains more and more momentum. The term is used in different contexts and on various abstraction layers, so that many perspectives on services exist. This chapter introduces multiperspective service management (MPSM) and its application in virtual power plants (VPPs). A VPP combines numerous decentralized generating units and consumers by a smart ICT-infrastructure, and thus creates a need for service-oriented architecture management. Service management has to address several views from different stakeholders from certain viewpoints. MPSM allows both the annotation of metadata to single services to support context-free analyses and modeling the entire enterprise for holistic and context-aware service management. This approach is based on Enterprise Architecture Management knowledge, and it shows how to manage complex systems like VPPs by considering different perspectives on services and domain-specific requirements. To demonstrate the benefits of the approach, exemplary analyses are provided as use cases.

1 Motivation The discipline of Enterprise Architecture Management (EAM), while becoming firmly established with global enterprises, often remains unnoticed by medium-sized enterprises. In global enterprises the role of enterprise architects has been established for years, and they are equipped by EAM tools with underlying EA frameworks such as the Zachman Framework (Zachman 1987, p. 276-292) or TOGAF (The Open Group 2009, p. 7). In smaller companies, however, the role of an enterprise architect may not even be defined or is poorly supported in terms of resources, budget, and influence. Peyret (2007) reports that, in the year 2006, only 26% of the small and medium-sized companies made use EAM tools. Yet, the need for a proper alignment of business and IT is more than ever a key to success-especially for medium-sized enterprises competing on a global market. EAM is a means to achieve this alignment, providing a holistic view of an enterprise's different architecture domains such as business, application, data and infrastructure (The Open Group 2009, P. 10). With respect to the complexity of these architecture domains and their relations, management tools play an important role in order to keep track of the architecture development and the business IT Alignment (Technische Universität München 2008, P. 23-37). Tool vendors are most often focusing on large enterprises, providing almost universal support for the entire architecture. This leads to complex and expensive tools requiring huge effort in customizing and professional training courses for the designated staff – a barrier hard to overcome by medium-sized enterprises.

Migration of legacy assets to SOA embodies a key software engineering challenge. Existing methodologies mostly focus on development of new services while they provide little to no guidance for transforming services from pre-existing enterprise assets. SAPIENSA, a joint research project of the VU University Amsterdam and Tilburg University, aims to fill this gap. It proposes a migration methodology creating a well-constructed SOA out of pre-existing enterprise assets. The methodology exploits the relevant architectural knowledge to drive migration. To this end, the necessary knowledge concerning the migration should be identified, captured, analyzed and generalized. It should be noted that, the methodology is the result of an extensive analysis of the literature in the fields of reengineering and service engineering. This paper provides an overview of the SAPIENSA methodology along with discussions on the regarding research areas.

Rigorous modelling techniques and specialised analysis methods support enterprise architects when embarking on enterprise architecture management (EAM). Yet, while customised modelling solutions provide scalability, adaptability and flexibility they are often in conflict with generic or reusable visualisations. We present an approach to augment customised modelling with the techniques of model transformations and higher-order transformations to provide flexible and adaptable visualisations with a minimum of requirements for the underlying enterprise models. We detail our approach with a proof-of-concept implementation and show how a decoupling can ease EAM approaches and provide appropriate tooling in practice.

For many enterprises, introduction and maintenance of service orientation is still a daunting task and there is often no distinct idea of how to approach respective projects. Especially the migration of legacy systems functionality into a new service-oriented environment - the SOA enabling - is key to the success of SOA projects. Another challenge arises for enterprises having SOA already introduced. Here, the focus lies on maintaining and evolving existing SOA environments - SOA maintenance. The SOAME workshop (soame2010.eu) brought together researchers and practitioners in these areas to present and discuss state-of-the-art techniques as well as real-world experiences.

The transfer from the current power grid to the power grid of the future implicates major changes for all stakeholders participating in the smart grid. Hence, utilities have to face several novel problems in terms of service provision. In this contribution, we examine two complementary views in the overall context of smart grids. We argue for a combination of those two views, based on ontology mappings. We explain a generic top-down and EA-related view, focused on intra-enterprise communication as well as a very domain-specific, technical bottom-up view, focused on inter-enterprise communication. The top-down view supports architects in reorganizing and developing enterprise SOA, whereas the bottom-up view takes into account the CIM (IEC 61970/61968), OPC UA (IEC 62541) and semantic web services to cope with technical interoperability in an utility domain-specific SOA. The combination of those two views results in the capability for smart grid stakeholders to realize seamless information exchange among field and IT.

The paradigm of service orientation is heavily used to design complex IT systems able to satisfy the need for an agile business support. Many enterprises have established service-oriented architectures (SOAs) of different size and complexity. However, such SOAs need efficient management and the discipline of enterprise architecture management has only just begun to reflect the shift from application orientation to service orientation. This contribution discusses how enterprise architecture management can react on service orientation by providing a viewpoint-driven approach. We present a methodology able to develop SOA viewpoints by systematically addressing stakeholder concerns, designing an adequate metamodel, identifying data sources and providing visualizations for stakeholders' analyses. To evaluate the approach, 45 SOA-related concerns were collected, a comprehensive metamodel was designed, and a prototype was implemented able to support typical SOA analyses in an enterprise context.

For many enterprises the introduction of service orientation is still a daunting task and there is often no distinct idea of how to approach respective projects. Only recently, SOA research addresses this open and essential question and systematic methodologies for SOA introduction and evolution have been conceived. IBM's SOMA and sd&m's Quasar Enterprise are prominent examples. In practice, these methodologies have to rely on a variety of enterprise-specific information and integrate a number of different architectural instruments. This contribution introduces one typical constituent of evolution towards service orientation making extensive use of enterprise-specific information. The presented approach and prototypical implementation for the gap analysis of current and ideal application landscapes can also be regarded as a building block for more general architecture development methodologies like for example proposed by the TOGAF Architecture Development Method. The gap analysis measures the distance between two states of the application landscape by applying and aggregating a set of metrics specifically aimed at the context of architecture development. It results in a list of concrete actions which can be considered for landscape migration planning and hence can be a helpful instrument for enterprise architects.

Ansätze und Strategien zur IT-Governance sind für viele Unternehmen heutzutage nahezu unverzichtbar geworden. Dazu trägt die Notwendigkeit bei, die IT des Unternehmens an den aktuellen Trends der Gestaltung und Entwicklung komplexer ITSysteme auszurichten. Hier liegt das Gewicht hauptsächlich auf verteilten Systemund Softwarearchitekturen, die beispielsweise dem Paradigma der Service-orientierten Architekturen (SOA) folgen.

The complexity of today’s enterprise application landscapes makes the selection of new applications which still fit into the existing landscape a complex and daunting task. In this paper, we present an approach to support this task by a two-phase landscape-dependent evaluation process. In the initial phase single characteristics of the landscape’s different applications and the application to be introduced are assessed. In the second phase these characteristics are combined into an overall value which can be regarded as an indication of how adequate the new application will be in the context of the existing landscape. We rely on vague evaluation models for the calculation of this indication and present fuzzy logic as one example for such a model. Finally, we substantiate our approach by a practical example.

In verschiedenen Fachgruppen der GI werden Themen bearbeitet, die sich berühren oder gar überlappen. Im Zuge der zunehmenden Spezialisierung der fachlichen Struktur der GI erschien es uns folgerichtig, die verschiedenen Communities in einer gemeinsamen Veranstaltung zusammenzuführen und Fachgruppenübergreifende Arbeiten anzustoßen. Deshalb waren die Themen zu diesem Workshop bewusst breit angelegt, und es wurden zugunsten von fokussierten Gruppendiskussionen nur relativ wenige Vorträge gehalten. Der Schwerpunkt lag auf gemeinsamen Themen der beteiligten Fachgruppen mit Bevorzugung aktueller Fragestellungen:

The integration of business information systems is an important task for enterprises, with an increasing demand on the level of integration. While most integration projects in the past concentrated on the integration of data used by different applications, today the focus is on the integration of the applications themselves. Furthermore there already is a tendency towards the integration of systems on the process and presentation level. In this paper we present the approach taken in the MINT project where a method for model-driven integration on the level of business processes that bases on the MDA approach of the OMG is proposed.