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Transaction processing is of growing importance for mobile computing. Booking tickets, flight reservation, banking, ePayment, and booking holiday arrangements are just a few examples for mobile transactions. Due to temporarily disconnected situations the synchronisation and consistent transaction processing are key issues. Serializability is a too strong criteria for correctness when the semantics of a transaction is known. We introduce a transaction model that allows higher concurrency for a certain class of transactions defined by its semantic. The transaction results are ”escrow serializable” and the synchronisation mechanism is non-blocking. Experimental implementation showed higher concurrency, transaction throughput, and less resources used than common locking or optimistic protocols.
As production workspaces become more mobile and dynamic it becomes increasingly important to reliably monitor the overall state of the environment. Therein manipulators or other robotic systems likely have to be able to act autonomously together with humans and other systems within a joint workspace. Such interactions require that all components in non-stationary environments are able to perceive the state relative to each other. As vision-sensors provide a rich source of information to accomplish this, we present RoPose, a convolutional neural network (CNN) based approach, to estimate the two dimensional joint configuration of a simulated industrial manipulator from a camera image. This pose information can further be used by a novel targetless calibration setup to estimate the pose of the camera relative to the manipulator’s space. We present a pipeline to automatically generate synthetic training data and conclude with a discussion of the potential usage of the same pipeline to acquire real image datasets of physically existent robots.
The digital transformation of our life changes the way we work, learn, communicate, and collaborate. Enterprises are presently transforming their strategy, culture, processes, and their information systems to become digital. The digital transformation deeply disrupts existing enterprises and economies. Digitization fosters the development of IT systems with many rather small and distributed structures, like Internet of Things, Microservices and mobile services. Since years a lot of new business opportunities appear using the potential of services computing, Internet of Things, mobile systems, big data with analytics, cloud computing, collaboration networks, and decision support. Biological metaphors of living and adaptable ecosystems provide the logical foundation for self optimizing and resilient run-time environments for intelligent business services and adaptable distributed information systems with service oriented enterprise architectures. This has a strong impact for architecting digital services and products following both a value-oriented and a service perspective. The change from a closed world modeling world to a more flexible open-world composition and evolution of enterprise architectures defines the moving context for adaptable and high distributed systems, which are essential to enable the digital transformation. The present research paper investigates the evolution of Enterprise Architecture considering new defined value-oriented mappings between digital strategies, digital business models and an improved digital enterprise architecture.
Free-floating e-scooter sharing is an upcoming trend in mobility, which has been spreading since 2015 in various German cities. Unlike the more scientifically explorend car sharing, the usage patterns and behaviors of e-scooter sharing customers are yet to be analyzed. This presumably discovers better ways to attract customers as well as adaptions of the business model in order to increase scooter utilization and therefore the profit of the e-scooter providers. As most of the customer's journey, from registration to scooter reservation and the ride itself, is digitally traceable, large datasets are available allowing for understanding of customers' needs and motivations. Based on these datasets of an e-scooter provider operating in a big German city we propose a customer clustering that identifies four different customer segments, which enables multiple conclusions to be drawn for business development and improving the problem-solution fit of the e-scooter sharing model.
Recognizing actions of humans, reliably inferring their meaning and being able to potentially exchange mutual social information are core challenges for autonomous systems when they directly share the same space with humans. Today’s technical perception solutions have been developed and tested mostly on standard vision benchmark datasets where manual labeling of sensory ground truth is a tedious but necessary task. Furthermore, rarely occurring human activities are underrepresented in such data leading to algorithms not recognizing such activities. For this purpose, we introduce a modular simulation framework which offers to train and validate algorithms on various environmental conditions. For this paper we created a dataset, containing rare human activities in urban areas, on which a current state of the art algorithm for pose estimation fails and demonstrate how to train such rare poses with simulated data only.
This research addresses the question of why employees use enterprise social networks (ESN). Against the background of technology acceptance research, we propose an extended unified theory of acceptance and use of technology (UTAUT) model, adapt it to an ESN context, and test our model against data from ESN users of large and medium-sized enterprises. We use partial least squares structural equation modeling to gain insights into the determinants of ESN use. This paper contributes to ESN acceptance research by evaluating a model containing determinants of ESN use. It also examines the effects of determinants on five different usage dimensions of ESN. The results reveal that facilitating conditions are the main driver of ESN use while the impact of intention to use is comparably small. Implications for theory and practice are discussed.
Combining agile development and software product lines in automotive: challenges and recommendations
(2018)
Software product lines (SPLs) are used throughout the automotive industry. SPLs help to manage the large number of variants and to improve quality by reuse. In order to develop high quality software faster, agile software development (ASD) practices are introduced. From both the research and the management point of view it is still not clear how these two approaches can be combined. We derive recommendations to combine ASD and SPLs based on challenges identified for an automotive specific model. This study combines the outcome of a literature review and a qualitative interview study with 16 practitioners from the automotive domain. We evaluate the results and analyze the relationship between ASD and SPLs in the automotive domain. Furthermore, we derive recommendations to combine ASD and SPLs based on challenges identified in the automotive domain. This study identifies 86 individual challenges. Important challenges address supplier collaboration and faster software release cycles without loss of quality. The identified challenges and the derived recommendations show that the combination of ASD and SPL in the automotive industry is promising but not trivial. There is a need for an automotive-specific approach that combines ASD and SPL.
An assessment model to foster the adoption of agile software product lines in the automotive domain
(2018)
A software product line is commonly used for the software development in large automotive organizations. A strategic reuse of software is needed to handle the increasing complexity of the development and to maintain the quality of numerous software variants. However, the development process needs to be continuously adapted at a fast pace to satisfy the changing market demands. Introducing agile software development methods promise the flexibility to react on customers’ change requests and market demands to deliver high quality software. Despite this need, it is still challenging to combine agile software development and product lines. The maturity of an agile adoption is often hard to determine. Assessing the current situation regarding the combination is a first step towards a successful inclusion of agile methods into automotive software product lines. Based on an interview study with 16 participants and a literature review, we build the so-called ASPLA Model allowing self-assessments within the team to determine the current state of agile software development in combination with software product lines. The model comprises seven areas of improvement and recommends a possibility to improve the current status.
Software engineering courses have to deliver theoretical and technical knowledge and skills while establishing links to practice. However, due to course goals or resource limitations, it is not always possible or even meaningful to set up complete projects and let students work on a real piece of software. For instance, if students shall understand the impact of group dynamics on productivity, a particular software to be developed is of less interest than an environment in which students can learn about team-related phenomena. To address this issue, we use experimentation as a teaching tool in software engineering courses. Experiments help to precisely characterize and study a problem in a systematic way, to observe phenomena, and to develop and evaluate solutions. Furthermore, experiments help establishing short feedback and learning cycles, and they also allow for experiencing risk and failure scenarios in a controlled environment. In this paper, we report on three courses in which we implemented different experiments and we share our experiences and lessons learned. Using these courses, we demonstrate how to use classroom experiments, and we provide a discussion on the feasibility based on formal and informal course evaluations. This experience report thus aims to help teachers integrating small- and medium sized experiments in their courses.
Engineers of the research project “Digital Product Life-Cycle” are using a graph-based design language to model all aspects of the product they are working on. This abstract model is the base for all further investigations, developments and implementations. In particular at early stages of development, collaborative decision making is very important. We propose a semantic augmented knowledge space by means of mixed reality technology, to support engineering teams. Therefore we present an interaction prototype consisting of a pico projector and a camera. In our usage scenario engineers are augmenting different artefacts in a virtual working environment. The concept of our prototype contains both an interaction and a technical concept. To realise implicit and natural interactions, we conducted two prototype tests: (1) A test with a low-fidelity prototype and (2) a test by using the method Wizard of Oz. As a result, we present a prototype with interaction selection using augmentation spotlighting and an interaction zoom as a semantic zoom.
In any autonomous driving system, the map for localization plays a vital part that is often underestimated. The map describes the world around the vehicle outside of the sensor view and is a main input into the decision making process in highly complicated scenarios. Thus there are strict requirements towards the accuracy and timeliness of the map. We present a robust and reliable approach towards crowd based mapping using a GraphSLAM framework based on radar sensors. We show on a parking lot that even in dynamically changing environments, the localization results are very accurate and reliable even in unexplored terrain without any map data. This can be achieved by collaborative map updates from multiple vehicles. To show these claims experimentally, the Joint Graph Optimization is compared to the ground truth on an industrial parking space. Mapping performance is evaluated using a dense map from a total station as reference and localization results are compared with a deeply coupled DGPS/INS system.
Incubators in multinational corporations : development of a corporate incubator operator model
(2017)
This paper analyzes the components of a corporate incubator operator model in multinational companies. Thereby, three relevant phases were identified: pre incubation, incubation, and exit. Each phase contains different criteria that represent critical success factors for a corporate incubator, which are based on theoretical findings and lessons learned from practice. During the pre-incubation phase companies should define their need for a corporate incubator, the origin of ideas and the selection criteria for incubator tenants. The actual phase of incubation refers to the incubator program, which should be flexible with respect to each tenant. Furthermore, resource allocation plays an important role during the incubator program. Exit options after a successful incubation differ according to internal ideas and external start-ups, as well as the objective of the incubator. The research is based on a comprehensive screening of existing incubator literature and a qualitative content analysis of statements from eight experts of international corporate incubators.
In the present paper we demonstrate the novel technique to apply the recently proposed approach of In-Place Appends – overwrites on Flash without a prior erase operation. IPA can be applied selectively: only to DB-objects that have frequent and relatively small updates. To do so we couple IPA to the concept of NoFTL regions, allowing the DBA to place update-intensive DB-objects into special IPA-enabled regions. The decision about region configuration can be (semi-)automated by an advisor analyzing DB-log files in the background.
We showcase a Shore-MT based prototype of the above approach, operating on real Flash hardware. During the demonstration we allow the users to interact with the system and gain hands-on experience under different demonstration scenarios.
Digitization fosters the development of IT environments with many rather small structures, like Internet of Things (IoT), microservices, or mobility systems. They are needed to support flexible and agile digitized products and services. The goal is to create service-oriented enterprise architectures (EA) that are self optimizing and resilient. The present research paper investigates methods for decision-making concerning digitization architectures for Internet of Things and microservices. They are based on evolving enterprise architecture reference models and state of the art elements for architectural engineering for microgranular systems. Decision analytics in this field becomes increasingly complex and decision support, particularly for the development and evolution of sustainable enterprise architectures, is sorely needed. The challenging of the decision processes can be supported with in a more flexible and intuitive way by an architecture management cockpit.
Nowadays there is a rich diversity of sleep monitoring systems available on the market. They promise to offer information about sleep quality of the user by recording a limited number of vital signals, mainly heart rate and body movement. Typically, fitness trackers, smart watches, smart shirts, smartphone applications or patches do not provide access to the raw sensor data. Moreover, the sleep classification algorithm and the agreement ratio with the gold standard, polysomnography (PSG) are not disclosed. Some commercial systems record and store the data on the wearable device, but the user needs to transfer and import it into specialised software applications or return it to the doctor, for clinical evaluation of the data set. Thus an immediate feedback mechanism or the possibility of remote control and supervision are lacking. Furthermore, many such systems only distinguish between sleep and wake states, or between wake, light sleep and deep sleep. It is not always clear how these stages are mapped to the four known sleep stages: REM, NREM1, NREM2, NREM3-4. [1] The goal of this research is to find a reduced complexity method to process a minimum number of bio vital signals, while providing accurate sleep classification results. The model we propose offers remote control and real time supervision capabilities, by using Internet of Things (IoT) technology. This paper focuses on the data processing method and the sleep classification logic. The body sensor network representing our data acquisition system will be described in a separate publication. Our solution showed promising results and a good potential to overcome the limitations of existing products. Further improvements will be made and subjects with different age and health conditions will be tested.
Software development consists to a large extent of human-based processes with continuously increasing demands regarding interdisciplinary team work. Understanding the dynamics of software teams can be seen as highly important to successful project execution. Hence, for future project managers, knowledge about non-technical processes in teams is significant. In this paper, we present a course unit that provides an environment in which students can learn and experience the role of different communication patterns in distributed agile software development. In particular, students gain awareness about the importance of communication by experiencing the impact of limitations of communication channels and the effects on collaboration and team performance. The course unit presented uses the controlled experiment instrument to provide the basic organization of a small software project carried out in virtual teams. We provide a detailed design of the course unit to allow for implementation in further courses. Furthermore, we provide experiences obtained from implementing this course unit with 16 graduate students. We observed students struggling with technical aspects and team coordination in general, while not realizing the importance of communication channels (or their absence). Furthermore, we could show the students that lacking communication protocols impact team coordination and performance regardless of the communication channels used.
For decades, Software Process Improvement (SPI) programs have been implemented, inter alia, to improve quality and speed of software development. To set up, guide, and carry out SPI projects, and to measure SPI state, impact, and success, a multitude of different SPI approaches and considerable experience are available. SPI addresses many aspects ranging from individual developer skills to entire organizations. It comprises for instance the optimization of specific activities in the software lifecycle as well as the creation of organization awareness and project culture. In the course of conducting a systematic mapping study on the state-of-the-art in SPI from a general perspective, we observed Global Software Engineering (GSE) becoming a topic of interest in recent years. Therefore, in this paper, we provide a detailed investigation of those papers from the overall systematic mapping study that were classified as addressing SPI in the context of GSE. From the main study’s result set, a set of 30 papers dealing with GSE was selected for an in-depth analysis using the systematic review instrument to study the contributions and to develop an initial picture of how GSE is considered from the perspective of SPI. Our findings show the analyzed papers delivering a substantial discussion of cultural models and how such models can be used to better address and align SPI programs with multi-national environments. Furthermore, experience is shared discussing how agile approaches can be implemented in companies working at the global scale. Finally, success factors and barriers are studied to help companies implementing SPI in a GSE context.
The internet of things, enterprise social networks, adaptive case management, mobility systems, analytics for big data, and cloud environments are emerging to support smart connected i.e. digital products and services and the digital transformation. Biological metaphors for living and adaptable ecosystems are currently providing the logical foundation for resilient run-time environments with serviceoriented digitization architectures and for self-optimizing intelligent business services and related distributed information systems. We are investigating mechanisms for flexible adaptation and evolution of information systems with digital architecture in the context of the ongoing digital transformation. The goal is to support flexible and agile transformations 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 micro-granular architectures, by extending original enterprise architecture reference models with digitization architectures and their multi-perspective architectural decision management.
IT environments that consist of a very large number of rather small structures like microservices, Internet of Things (IoT) components, or mobility systems are emerging to support flexible and agile products and services in the age of digital transformation. Biological metaphors of living and adaptable ecosystems with service-oriented enterprise architectures provide the foundation for self-optimizing, resilient run-time environments and distributed information systems. We are extending Enterprise Architecture (EA) methodologies and models that cover a high degree of heterogeneity and distribution to support the digital transformation and related information systems with micro-granular architectures. Our aim is to support flexibility and agile transformation for both IT and business capabilities within adaptable digital enterprise architectures. The present research paper investigates mechanisms for integrating Microservice Architectures (MSA) by extending original enterprise architecture reference models with elements for more flexible architectural metamodels and EA-mini-descriptions.
Reliable and accurate car driver head pose estimation is an important function for the next generation of advanced driver assistance systems that need to consider the driver state in their analysis. For optimal performance, head pose estimation needs to be non-invasive, calibration-free and accurate for varying driving and illumination conditions. In this pilot study we investigate a 3D head pose estimation system that automatically fits a statistical 3D face model to measurements of a driver’s face, acquired with a low-cost depth sensor on challenging real-world data. We evaluate the results of our sensor-independent, driver-adaptive approach to those of a state-of-the-art camera-based 2D face tracking system as well as a non-adaptive 3D model relative to own ground-truth data, and compare to other 3D benchmarks. We find large accuracy benefits of the adaptive 3D approach.
On the way to achieving higher degrees of autonomy for vehicles in complicated, ever changing scenarios, the localization problem poses a very important role. Especially the Simultaneous Localization and Mapping (SLAM) problem has been studied greatly in the past. For an autonomous system in the real world, we present a very cost-efficient, robust and very precise localization approach based on GraphSLAM and graph optimization using radar sensors. We are able to prove on a dynamically changing parking lot layout that both mapping and localization accuracy are very high. To evaluate the performance of the mapping algorithm, a highly accurate ground truth map generated from a total station was used. Localization results are compared to a high precision DGPS/INS system. Utilizing these methods, we can show the strong performance of our algorithm.
Organizations identified the opportunities of big data analytics to support the business with problem-specific insights through the exploitation of generated data. Sociotechnical solutions are developed in big data projects to reach competitive advantage. Although these projects are aligned to specific business needs, common architectural challenges are not addressed in a comprehensive manner. Enterprise architecture management is a holistic approach to tackle complex business and IT architectures. The transformation of an organization’s EA is influenced by big data transformation processes and their data-driven approach on all layers. In this paper, we review big data literature to analyze which requirements for the EA management discipline are proposed. Based on a systematic literature identification, conceptual categories of requirements for EA management are elicited utilizing an inductive category formation. These conceptual categories of requirements constitute a category system that facilitates a new perspective on EA management and fosters the innovation-driven evolution of the EA management.
discipline.
Significant advances have been achieved in mobile robot localization and mapping in dynamic environments, however these are mostly incapable of dealing with the physical properties of automotive radar sensors. In this paper we present an accurate and robust solution to this problem, by introducing a memory efficient cluster map representation. Our approach is validated by experiments that took place on a public parking space with pedestrians, moving cars, as well as different parking configurations to provide a challenging dynamic environment. The results prove its ability to reproducibly localize our vehicle within an error margin of below 1% with respect to ground truth using only point based radar targets. A decay process enables our map representation to support local updates.
In the present tutorial we perform a cross-cut analysis of database systems from the perspective of modern storage technology, namely Flash memory. We argue that neither the design of modern DBMS, nor the architecture of flash storage technologies are aligned with each other. The result is needlessly suboptimal DBMS performance and inefficient flash utilisation as well as low flash storage endurance and reliability. We showcase new DBMS approaches with improved algorithms and leaner architectures, designed to leverage the properties of modern storage technologies. We cover the area of transaction management and multi-versioning, putting a special emphasis on: (i) version organisation models and invalidation mechanisms in multi-versioning DBMS; (ii) Flash storage management especially on append-based storage in tuple granularity; (iii) Flash-friendly buffer management; as well as (iv) improvements in the searching and indexing models. Furthermore, we present our NoFTL approach to native Flash access that integrates parts of the flash-management functionality into the DBMS yielding significant performance increase and simplification of the I/O stack. In addition, we cover the basics of building large Flash storage for DBMS and revisit some of the RAID techniques and principles.
Rapid value delivery requires a company to utilize empirical evaluation of new features and products in order to avoid unnecessary product risks. This helps to make data-driven decisions and to ensure that the development is focused on features that provide real value for customers. Short feedback loops are a prerequisite as they allow for fast learning and reduced reaction times. Continuous experimentation is a development practice where the entire R&D process is guided by constantly conducting experiments and collecting feedback. Although principles of continuous experimentation have been successfully applied in domains such as game software or SAAS, it is not obvious how to transfer continuous experimentation to the business to-business domain. In this article, a case study from a medium-sized software company in the B2B domain is presented. The study objective is to analyze the challenges, benefits and organizational aspects of continuous experimentation in the B2B domain. The results suggest that technical challenges are only one part of the challenges a company encounters in this transition. The company also has to address challenges related to the customer and organizational culture. Unique properties in each customers business play a major role and need to be considered when designing experiments. Additionally, the speed by which experiments can be conducted is relative to the speed by which production deployments can be made. Finally, the article shows how the study results can be used to modify the development in the case company in a way that more feedback and data is used instead of opinions.
To evaluate the quality of a person´s sleep it is essential to identify the sleep stages and their durations. Currently, the gold standard in terms of sleep analysis is overnight polysomnography (PSG), during which several techniques like EEG (eletroencephalogram), EOG (electrooculogram), EMG (electromyogram), ECG (electrocardiogram), SpO2 (blood oxygen saturation) and for example respiratory airflow and respiratory effort are recorded. These expensive and complex procedures, applied in sleep laboratories, are invasive and unfamiliar for the subjects and it is a reason why it might have an impact on the recorded data. These are the main reasons why low-cost home diagnostic systems are likely to be advantageous. Their aim is to reach a larger population by reducing the number of parameters recorded. Nowadays, many wearable devices promise to measure sleep quality using only the ECG and body-movement signals. This work presents an android application developed in order to proof the accuracy of an algorithm published in the sleep literature. The algorithm uses ECG and body movement recordings to estimate sleep stages. The pre-recorded signals fed into the algorithm have been taken from physionet1 online database. The obtained results have been compared with those of the standard method used in PSG. The mean agreement ratios between the sleep stages REM, Wake, NREM-1, NREM-2 and NREM-3 were 38.1%, 14%, 16%, 75% and 54.3%.
Information systems, which support the workflow in the clinical area, are currently limited to organizational processes. This work shows a first approach of an information system supporting all actors in the perioperative area. The first prototype and proof of concept was a task manager, giving all actors information about their task and the task of all other actors during an intervention. Based on this initial task manager, we implemented an information system based on a workflow engine controlling all processes and all information necessary for the intervention. A second part was the development of a perioperative process visualization which was developed based on a user centered approach jointly with clinicians and OR members.
Excellence in IT is both a driver and a key enabler of the digital transformation. The digital transformation changes the way we live, work, learn, communicate, and collaborate. The Internet of Things (IoT) fundamentally influences today’s digital strategies with disruptive business operating models and fast changing markets. New business information systems are integrating emerging Internet of Things infrastructures and components. With the huge diversity of Internet of Things technologies and products organizations have to leverage and extend previous Enterprise Architecture efforts to enable business value by integrating Internet of Things architectures. Both architecture engineering and management of current information systems and business models are complex and currently integrating beside the Internet of Things synergistic subjects, like Enterprise Architecture in context with services & cloud computing, semantic-based decision support through ontologies and knowledge-based systems, big data management, as well as mobility and collaboration networks. To provide adequate decision support for complex business/IT environments, we have to make transparent the impact of business and IT changes over the integral landscape of affected architectural capabilities, like directly and transitively impacted IoT-objects, business categories, processes, applications, services, platforms and infrastructures. The paper describes a new metamodel-based approach for integrating Internet of Things architectural objects, which are semi-automatically federated into a holistic Digital Enterprise Architecture environment.
SmartLife ecosystems are emerging as intelligent user-centered systems that will shape future trends in technology and communication. Biological metaphors of living adaptable ecosystems provide the logical foundation for self-optimizing and self-healing run-time environments for intelligent adaptable business services and related information systems with service-oriented enterprise architectures. The present research in progress work investigates mechanisms for adaptable enterprise architectures for the development of service-oriented ecosystems with integrated technologies like Semantic Technologies, Web Services, Cloud Computing and Big Data Management. With a large and diverse set of ecosystem services with different owners, our scenario of service-based SmartLife ecosystems can pose challenges in their development, and more importantly, for maintenance and software evolution. Our research explores the use of knowledge modeling using ontologies and flexible metamodels for adaptable enterprise architectures to support program comprehension for software engineers during maintenance and evolution tasks of service-based applications. Our previous reference enterprise architecture model ESARC -- Enterprise Services Architecture Reference Cube -- and the Open Group SOA Ontology was extended to support agile semantic analysis, program comprehension and software evolution for a SmartLife applications scenario. The Semantic Browser is a semantic search tool that was developed to provide knowledge-enhanced investigation capabilities for service-oriented applications and their architectures.
Current approaches for enterprise architecture lack analytical instruments for cyclic evaluations of business and system architectures in real business enterprise system environments. This impedes the broad use of enterprise architecture methodologies. Furthermore, the permanent evolution of systems desynchronizes quickly model representation and reality. Therefore we are introducing an approach for complementing the existing top-down approach for the creation of enterprise architecture with a bottom approach. Enterprise Architecture Analytics uses the architectural information contained in many infrastructures to provide architectural information. By applying Big Data technologies it is possible to exploit this information and to create architectural information. That means, Enterprise Architectures may be discovered, analyzed and optimized using analytics. The increased availability of architectural data also improves the possibilities to verify the compliance of Enterprise Architectures. Architectural decisions are linked to clustered architecture artifacts and categories according to a holistic EAM Reference Architecture with specific architecture metamodels. A special suited EAM Maturity Framework provides the base for systematic and analytics supported assessments of architecture capabilities.
Analysis and planning of Enterprise Architectures (EA) is a complex task for stakeholders. The change of one architecture element has impact on multiple other elements because of manifold relationships and interactions between them. The interactive cockpit approach presented in this paper supports stakeholders planning and analyzing EAs and to tackle the intrinsic complexity. This approach supplies a cockpit with multiple viewpoints to put relevant information side-by-side without losing the context combined with interaction functionality. In this paper, we develop such cockpit starting with relevant use cases, describing a potential design based on well-established foundations in EA modeling, and outline an exemplary usage scenario.
Model-guided Therapy and Surgical Workflow Systems are two interrelated research fields, which have been developed separately in the last years. To make full use of both technologies, it is necessary to integrate them and connect them to Hospital Information Systems. We propose a framework for integration of Model-guided Therapy in Hospital Information Systems based on the Electronic Medical Record, and a taskbased Workflow Management System, which is suitable for clinical end users. Two prototypes - one based on Business Process Modeling Language, one based on the serum-board - are presented. From the experience with these prototypes, we developed a novel personalized visualization system for Surgical Workflows and Model-guided Therapy. Key challenges for further development are automated situation detection and a common communication infrastructure.