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Background and purpose: Transapical aortic valve replacement (TAVR) is a recent minimally invasive surgical treatment technique for elderly and high-risk patients with severe aortic stenosis. In this paper,a simple and accurate image-based method is introduced to aid the intra-operative guidance of TAVR procedure under 2-D X-ray fluoroscopy.
Methods: The proposed method fuses a 3-D aortic mesh model and anatomical valve landmarks with live 2-D fluoroscopic images. The 3-D aortic mesh model and landmarks are reconstructed from interventional X-ray C-arm CT system, and a target area for valve implantation is automatically estimated using these aortic mesh models.Based on template-based tracking approach, the overlay of visualized 3-D aortic mesh model, land-marks and target area of implantation is updated onto fluoroscopic images by approximating the aortic root motion from a pigtail catheter motion without contrast agent. Also, a rigid intensity-based registration algorithm is used to track continuously the aortic root motion in the presence of contrast agent.Furthermore, a sensorless tracking of the aortic valve prosthesis is provided to guide the physician to perform the appropriate placement of prosthesis into the estimated target area of implantation.
Results: Retrospective experiments were carried out on fifteen patient datasets from the clinical routine of the TAVR. The maximum displacement errors were less than 2.0 mm for both the dynamic overlay of aortic mesh models and image-based tracking of the prosthesis, and within the clinically accepted ranges. Moreover, high success rates of the proposed method were obtained above 91.0% for all tested patient datasets.
Conclusion: The results showed that the proposed method for computer-aided TAVR is potentially a helpful tool for physicians by automatically defining the accurate placement position of the prosthesis during the surgical procedure.
Detecting the adherence of driving rules in an energy-efficient, safe and adaptive driving system
(2016)
An adaptive and rule-based driving system is being developed that tries to improve the driving behavior in terms of the energy-efficiency and safety by giving recommendations. Therefore, the driving system has to monitor the adherence of driving rules by matching the rules to the driving behavior. However, existing rule matching algorithms are not sufficient, as the data within a driving system is changing frequently. In this paper a rule matching algorithm is introduced that is able to handle frequently changing data within the context of the driving system. 15 journeys were used to evaluate the performance of the rule matching algorithms. The results showed that the introduced algorithm outperforms existing algorithms in the context of the driving system. Thus, the introduced algorithm is suited for matching frequently changing data against rules with a higher performance, why it will be used in the driving system for the detection of broken energy-efficiency of safety-relevant driving rules.
In the last decades, several driving systems were developed to improve the driving behaviour in energy efficiency or safety. However, these driving systems cover either the area of energy-efficiency or safety. Furthermore, they do not consider the stress level of the driver when showing a recommendation, although stress can lead to an unsafe or inefficient driving behaviour. In this paper, an approach is presented to consider the driver stress level in a driving system for safe and energy-efficient driving behaviour. The driving system tries to suppress a recommendation when the driver is in stress in order not to stress the driver additionally with recommendations in a stressful driving situation. This can lead to an increase in the road safety and in the user acceptance of the driving system, as the driver is not getting bothered or stressed by the driving system.
The evaluation of the approach showed, that the driving system
is able to show recommendations to the driver, while also reacting
to a high stress level by suppressing recommendations in
order not to stress the driver additionally.
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.
Context: An experiment-driven approach to software product and service development is gaining increasing attention as a way to channel limited resources to the efficient creation of customer value. In this approach, software capabilities are developed incrementally and validated in continuous experiments with stakeholders such as customers and users. The experiments provide factual feedback for guiding subsequent development.
Objective: This paper explores the state of the practice of experimentation in the software industry. It also identifies the key challenges and success factors that practitioners associate with the approach.
Method: A qualitative survey based on semi-structured interviews and thematic coding analysis was conducted. Ten Finnish software development companies, represented by thirteen interviewees, participated in the study.
Results: The study found that although the principles of continuous experimentation resonated with industry practitioners, the state of the practice is not yet mature. In particular, experimentation is rarely systematic and continuous. Key challenges relate to changing the organizational culture, accelerating the development cycle speed, and finding the right measures for customer value and product success. Success factors include a supportive organizational culture, deep customer and domain knowledge, and the availability of the relevant skills and tools to conduct experiments.
Conclusions: It is concluded that the major issues in moving towards continuous experimentation are on an organizational level; most significant technical challenges have been solved. An evolutionary approach is proposed as a way to transition towards experiment-driven development.
Software process improvement (SPI) has been around for decades: frameworks are proposed, success factors are studied, and experiences have been reported. However, the sheer mass of concepts, approaches, and standards published over the years overwhelms practitioners as well as researchers. What is out there? Are there new trends and emerging approaches? What are open issues? Still, we struggle to answer these questions about the current state of SPI and related research. In this article, we present results from an updated systematic mapping study to shed light on the field of SPI, to develop a big picture of the state of the art, and to draw conclusions for future research directions. An analysis of 769 publications draws a big picture of SPI-related research of the past quarter-century. Our study shows a high number of solution proposals, experience reports, and secondary studies, but only few theories and models on SPI in general. In particular, standard SPI models like CMMI and ISO/IEC 15,504 are analyzed, enhanced, and evaluated for applicability in practice, but these standards are also critically discussed, e.g., from the perspective of SPI in small to-medium-sized companies, which leads to new specialized frameworks. New and specialized frameworks account for the majority of the contributions found (approx. 38%). Furthermore, we find a growing interest in success factors (approx. 16%) to aid companies in conducting SPI and in adapting agile principles and practices for SPI (approx. 10%). Beyond these specific topics, the study results also show an increasing interest into secondary studies with the purpose of aggregating and structuring SPI-related knowledge. Finally, the present study helps directing future research by identifying under-researched topics awaiting further investigation.
Software development consists to a large extend of humanbased 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 impact of group dynamics on project performance and quality. The course unit uses the Tuckman model as theoretical framework, and borrows from controlled experiments to organize and implement its practical parts in which students then experience the effects of, e.g., time pressure, resource bottlenecks, staff turnover, loss of key personnel, and other stress factors. We provide a detailed design of the course unit to allow for implementation in further software project management courses. Furthermore, we provide experiences obtained from two instances of this unit conducted in Munich and Karlskrona with 36 graduate students. We observed students building awareness of stress factors and developing counter measures to reduce impact of those factors. Moreover, students experienced what problems occur when teams work under stress and how to form a performing team despite exceptional situations.
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.
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.
Sleep is an important aspect in life of every human being. The average sleep duration for an adult is approximately 7 h per day. Sleep is necessary to regenerate physical and psychological state of a human. A bad sleep quality has a major impact on the health status and can lead to different diseases. In this paper an approach will be presented, which uses a long-term monitoring of vital data gathered by a body sensor during the day and the night supported by mobile application connected to an analyzing system, to estimate sleep quality of its user as well as give recommendations to improve it in real-time. Actimetry and historical data will be used to improve the individual recommendations, based on common techniques used in the area of machine learning and big data analysis.
The troubles began when Tom, the business analyst, asked the customer what he wants. The customer came up with good ideas for software features. Tom created a brilliant roadmap and defined the requirements for a new software product. Mary, the development team leader, was already eager to start developing and happy when she got the requirements. She and her team went ahead and created the software right away. Afterwards, Paul tested the software against the requirements. As soon as the software fulfilled the requirements, Linda, the product manager, deployed it to the customer. The customer did not like the software and ignored it. Ringo, the head of software development, was fired. How come? Nowadays, we have tremendous capabilities for creating nearly all kinds of software to fulfill the needs of customers. We can apply agile practices for reacting flexibly to changing requirements, we can use distributed development, open source, or other means for creating software at low cost, we can use cloud technologies for deploying software rapidly, and we can get enormous amounts of data showing us how customers actually use software products. However, the sad reality is that around 90% of products fail, and more than 60% of the features of a typical software product are rarely or never used. But there is a silver lining – an insight regarding successful features: Around 60% of the successes stem from a significant change of an initial idea. This gives us a hint on how to build the right software for users and customers.
Software Process Improvement (SPI) programs have been implemented, inter alia, to improve quality and speed of software development. 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 organizational 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 Software Quality Management (SQM) being of certain relevance in SPI programs. 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 SQM (including testing). From the main study’s result set, 92 papers were selected for an in-depth systematic review to study the contributions and to develop an initial picture of how these topics are addressed in SPI. Our findings show a fairly pragmatic contribution set in which different solutions are proposed, discussed, and evaluated. Among others, our findings indicate a certain reluctance towards standard quality or (test) maturity models and a strong focus on custom review, testing, and documentation techniques, whereas a set of five selected improvement measures is almost equally addressed.
Context: Companies need capabilities to evaluate the customer value of software intensive products and services. One way of systematically acquiring data on customer value is running continuous experiments as part of the overall development process. Objective: This paper investigates the first steps of transitioning towards continuous experimentation in a large company, including the challenges faced. Method: We conduct a single-case study using participant observation, interviews, and qualitative analysis of the collected data. Results: Results show that continuous experimentation was well received by the practitioners and practising experimentation helped them to enhance understanding of their product value and user needs. Although the complexities of a large multi-stakeholder business to-business (B2B) environment presented several challenges such as inaccessible users, it was possible to address impediments and integrate an experiment in an ongoing development project. Conclusion: Developing the capability for continuous experimentation in large organisations is a learning process which can be supported by a systematic introduction approach with the guidance of experts. We gained experience by introducing the approach on a small scale in a large organisation, and one of the major steps for future work is to understand how this can be scaled up to the whole development organisation.
Context: The current transformation of automotive development towards innovation, permanent learning and adapting to changes are directing various foci on the integration of agile methods. Although, there have been efforts to apply agile methods in the automotive domain for many years, a wide-spread adoption has not yet taken place.
Goal: This study aims to gain a better understanding of the forces that prevent the adoption of agile methods.
Method: Survey based on 16 semi-structured interviews from the automotive domain. The results are analyzed by means of thematic coding.
Results: Forces that prevent agile adoption are mainly of organizational, technical and social nature and address inertia, anxiety and context factors. Key challenges in agile adoption are related to transforming organizational structures and culture, achieving faster software release cycles without loss of quality, the importance of software reuse in combination with agile practices, appropriate quality assurance measures, and the collaboration with suppliers and other disciplines such as mechanics.
Conclusion: Significant challenges are imposed by specific characteristics of the automotive domain such as high quality requirements and many interfaces to surrounding rigid and inflexible processes. Several means are identified that promise to overcome these challenges.
Managing software process evolution : traditional, agile and beyond - how to handle process change
(2016)
This book focuses on the design, development, management, governance and application of evolving software processes that are aligned with changing business objectives, such as expansion to new domains or shifting to global production. In the context of an evolving business world, it examines the complete software process lifecycle, from the initial definition of a product to its systematic improvement. In doing so, it addresses difficult problems, such as how to implement processes in highly regulated domains or where to find a suitable notation system for documenting processes, and provides essential insights and tips to help readers manage process evolutions. And last but not least, it provides a wealth of examples and cases on how to deal with software evolution in practice.
Reflecting these topics, the book is divided into three parts. Part 1 focuses on software business transformation and addresses the questions of which process(es) to use and adapt, and how to organize process improvement programs. Subsequently, Part 2 mainly addresses process modeling. Lastly, Part 3 collects concrete approaches, experiences, and recommendations that can help to improve software processes, with a particular focus on specific lifecycle phases.
This book is aimed at anyone interested in understanding and optimizing software development tasks at their organization. While the experiences and ideas presented will be useful for both those readers who are unfamiliar with software process improvement and want to get an overview of the different aspects of the topic, and for those who are experts with many years of experience, it particularly targets the needs of researchers and Ph.D. students in the area of software and systems engineering or information systems who study advanced topics concerning the organization and management of (software development) projects and process improvements projects.
Information Systems in Distributed Environment (ISDE) is becoming a prominent standard in this globalization era due to advancement in information and communication technologies. The advent of the internet has supported Distributed Software Development (DSD) by introducing new concepts and opportunities, resulting in benefits such as scalability, flexibility, interdependence, reduced cost, resource pools, and usage tracking. The distributed development of information systems as well as their deployment and operation in distributed environments impose new challenges for software organizations and can lead to business advantages. In distributed environments, business units collaborate across time zones, organizational boundaries, work cultures and geographical distances, something that ultimately has led to an increasing diversification and growing complexity of cooperation among units. The real-world practice of developing, deployment and operation of information systems in globally distributed projects has been viewed from various perspectives, though technical and engineering in conjunction with managerial and organizational viewpoints have dominated the researcher’s attention so far. Successful participation in distributed environments, however, is ultimately a matter of the participants understanding and exploiting the particularities of their respective local contexts at specific points in time and exploring practical solutions through the local resources available.
This special issue of the Computer standards & interfaces journal therefore includes papers received from the public call for papers and extended and improved versions of those papers that were selected from the best of the International Workshop on Information Systems in Distributed Environment (ISDE 2014). It aims to serve as a forum to bring together academics, researchers, practitioners and students in the field of distributed information system, by presenting novel developments and lesson learned from real world cases, and to promote the exchange of ideas, discussion and advancement in these areas.
Die Kombination von Softwareproduktlinien und agiler Softwareentwicklung in der Automobilbranche ist vielversprechend. Das Ziel ist hierbei, sowohl die Vorteile agiler Methoden wie kurze Entwicklungszyklen als auch die Vorteile systematischer Wiederverwendung wie beispielsweise das effektive Management von Varianten zu erzielen. Allerdings ist die Kombination auch mit Herausforderungen verbunden und erfordert eine geeignete Einführungs- oder Transformationsstrategie. Basierend auf Erkenntnissen einer Interviewstudie und existierenden Produktlinienentwicklungen werden Herausforderungen und Lösungsideen aufgezeigt.
Analysis of multicellular patterns is required to understand tissue organizational processes. By using a multi-scale object oriented image processing method, the spatial information of cells can be extracted automatically. Instead of manual segmentation or indirect measurements, such as general distribution of contrast or flow, the orientation and distribution of individual cells is extracted for quantitative analysis. Relevant objects are identified by feature queries and no low-level knowledge of image processing is required.
Data collected from internet applications are mainly stored in the form of transactions. All transactions of one user form a sequence, which shows the user´s behaviour on the site. Nowadays, it is important to be able to classify the behaviour in real time for various reasons: e.g. to increase conversion rate of customers while they are in the store or to prevent fraudulent transactions before they are placed. However, this is difficult due to the complex structure of the data sequences (i.e. a mix of categorical and continuous data types, constant data updates) and the large amounts of data that are stored. Therefore, this thesis studies the classification of complex data sequences. It surveys the fields of time series analysis (temporal data mining), sequence data mining or standard classification algorithms. It turns out that these algorithms are either difficult to be applied on data sequences or do not deliver a classification: Time series need a predefined model and are not able to handle complex data types; sequence classification algorithms such as the apriori algorithm family are not able to utilize the time aspect of the data. The strengths and weaknesses of the candidate algorithms are identified and used to build a new approach to solve the problem of classification of complex data sequences. The problem is thereby solved by a two-step process. First, feature construction is used to create and discover suitable features in a training phase. Then, the blueprints of the discovered features are used in a formula during the classification phase to perform the real time classification. The features are constructed by combining and aggregating the original data over the span of the sequence including the elapsed time by using a calculated time axis. Additionally, a combination of features and feature selection are used to simplify complex data types. This allows catching behavioural patterns that occur in the course of time. This new proposed approach combines techniques from several research fields. Part of the algorithm originates from the field of feature construction and is used to reveal behaviour over time and express this behaviour in the form of features. A combination of the features is used to highlight relations between them. The blueprints of these features can then be used to achieve classification in real time on an incoming data stream. An automated framework is presented that allows the features to adapt iteratively to a change in underlying patterns in the data stream. This core feature of the presented work is achieved by separating the feature application step from the computational costly feature construction step and by iteratively restarting the feature construction step on the new incoming data. The algorithm and the corresponding models are described in detail as well as applied to three case studies (customer churn prediction, bot detection in computer games, credit card fraud detection). The case studies show that the proposed algorithm is able to find distinctive information in data sequences and use it effectively for classification tasks. The promising results indicate that the suggested approach can be applied to a wide range of other application areas that incorporate data sequences.
This book showcases new and innovative approaches to biometric data capture and analysis, focusing especially on those that are characterized by non-intrusiveness, reliable prediction algorithms, and high user acceptance. It comprises the peer-reviewed papers from the international workshop on the subject that was held in Ancona, Italy, in October 2014 and featured sessions on ICT for health care, biometric data in automotive and home applications, embedded systems for biometric data analysis, biometric data analysis: EMG and ECG, and ICT for gait analysis. The background to the book is the challenge posed by the prevention and treatment of common, widespread chronic diseases in modern, aging societies. Capture of biometric data is a cornerstone for any analysis and treatment strategy. The latest advances in sensor technology allow accurate data measurement in a non-intrusive way, and in many cases it is necessary to provide online monitoring and real-time data capturing to support a patient’s prevention plans or to allow medical professionals to access the patient’s current status. This book will be of value to all with an interest in this expanding field.
Stress is becoming an important topic in modern life. The influence of stress results in a higher rate of health disorders such as burnout, heart problems, obesity, asthma, diabetes, depressions and many others. Furthermore individual’s behavior and capabilities could be directly affected leading to altered cognition, inappropriate decision making and problem solving skills. In a dynamic and unpredictable environment, such as automotive, this can result in a higher risk for accidents. Different papers faced the estimation as well as prediction of drivers’ stress level during driving. Another important question is not only the stress level of the driver himself, but also the influence on and of a group of other drivers in the near area. This paper proposes a system, which determines a group of drivers in a near area as clusters and it derives the individual stress level. This information will be analyzed to generate a stress map, which represents a graphical view about road section with a higher stress influence. Aggregated data can be used to generate navigation routes with a lower stress influence to decrease stress influenced driving as well as improve road safety.
Stress is recognized as a predominant disease with raising costs for rehabilitation and treatment. Currently there are several different approaches that can be used for determining and calculating the stress levels. Usually the methods for determining stress are divided in two categories. The first category do not require any special equipment for measuring the stress. This category useless the variation in the behaviour patterns that occur while stress. The core disadvantage for the category is their limitation to specific use case. The second category uses laboratories instruments and biological sensors. This category allow to measure stress precisely and proficiently but on the same time they are not mobile and transportable and do not support real-time feedback. This work presents a mobile system that provides the calculation of stress. For achieving this, the of a mobile ECG sensor is analysed, processed and visualised over a mobile system like a smartphone. This work also explains the used stress measurement algorithm. The result of this work is a portable system that can be used with a mobile system like a smartphone as visual interface for reporting the current stress level.
A lot of people need help in their daily life to wash, select and manage their clothing. The goal of this work is to design an assistant system (eKlarA) to support the user by giving recommendations to choose the clothing combinations, to find the clothing and to wash the clothing. The idea behind eKlarA is to generate a system that uses sensors to identify the clothing and their state in the clothing cycle. The clothing cycle consists of the stations: closets, laundry basket and washing machine in one or several places. The system uses the information about the clothing, weather and calendar to support the user in the different steps of the clothing cycle. The first prototype of this system has been developed and tested. The test results are presented in this work.
Besides the optimisation of the car, energy-efficiency and safety can also be increased by optimising the driving behaviour. Based on this fact, a driving system is in development whose goal is to educate the driver in energy efficient and safe driving. It monitors the driver, the car and the environment and gives energy-efficiency and safety relevant recommendations. However, the driving system tries not to distract or bother the driver by giving recommendations for example during stressful driving situations or when the driver is not interested in that recommendation. Therefore, the driving system monitors the stress level of the driver as well as the reaction of the driver to a given recommendation and decideswhether to give a recommendation or not. This allows to suppress recommendations when needed and, thus, to increase the road safety and the user acceptance of
the driving system.
The digitization of our society changes the way we live, work, learn, communicate, and collaborate. The Internet of Things, enterprise social networks, adaptive case management, mobility systems, analytics for big data, and cloud services environments are emerging to support smart connected 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 service-oriented enterprise architectures. Our aim is to support flexibility and agile transformations for both business domains and related information technology. The present research paper investigates mechanisms for decision analytics in the context of multi-perspective explorations of enterprise services and their digital enterprise architectures by extending original architecture reference models with state of art elements for agile architectural engineering for the digitization and collaborative architectural decision support. The paper’s context focuses on digital transformations of business and IT and integrates fundamental mappings between adaptable digital enterprise architectures and service-oriented information systems. We are putting a spotlight on the example domain – Internet of Things.
The amount of image data has been rising exponentially over the last decades due to numerous trends like social networks, smartphones, automotive, biology, medicine and robotics. Traditionally, file systems are used as storage. Although they are easy to use and can handle large data volumes, they are suboptimal for efficient sequential image processing due to the limitation of data organisation on single images. Database systems and especially column-stores support more stuctured storage and access methods on the raw data level for entiere series.
In this paper we propose definitions of various layouts for an efficient storage of raw image data and metadata in a column store. These schemes are designed to improve the runtime behaviour of image processing operations. We present a tool called column-store Image Processing Toolbox (cIPT) allowing to easily combine the data layouts and operations for different image processing scenarios.
The experimental evaluation of a classification task on a real world image dataset indicates a performance increase of up to 15x on a column store compared to a traditional row-store (PostgreSQL) while the space consumption is reduced 7x. With these results cIPT provides the basis for a future mature database feature.
In this paper we present our work in progress on revisiting traditional DBMS mechanisms to manage space on native Flash and how it is administered by the DBA. Our observations and initial results show that: the standard logical database structures can be used for physical organization of data on native Flash; at the same time higher DBMS performance is achieved without incurring extra DBA overhead. Initial experimental evaluation indicates a 20% increase in transactional throughput under TPC-C, by performing intelligent data placement on Flash, less erase operations and thus better Flash longevity.
Purpose – This paper aims to complement the current understanding about user engagement in electronic word-of-mouth (eWoM) communications across online services and product communities. It examines the effect of the senders’ prior experience with products and services, and their extent of acquaintance with other community members, on user engagement with the eWoM.
Design/methodology/approach – The study used a sample of 576 unique user postings from the corporate fan page of two German firms: a service community of a telecom provider and a product community of a car manufacturer. Multiple regression analysis is used to test the conceptual model.
Findings – Senders’ prior experience and acquaintance positively affect user engagement with eWoM, and these effects differ across communities for products and services and across their influence on “likes” and “comments”. The results also suggest that communities for products are orientated toward information sharing, while those discussing services engage in information building.
Research limitations/implications – This research explains mechanisms of user engagement with eWoM and opens directions for future research around motives, content and social media tools within the structures of online communities. The insights on information-handling dimensions of online tools and antecedents to their use contribute to the research on two prioritized topics by the Marketing Science Institute – "Measuring and
Communicating the Value of Online Marketing Activities and Investments" and "Leveraging Digital/Social/Mobile Technology".
Practical implications – This research offers insights for firms to leverage user engagement and facilitate eWoM generation through members who have a higher number of acquaintances or who have more experience with the product or service. Executives should concentrate their community engagement strategies on the identification and utilization of power users. The conceptualization and empirical test about the role of likes and comments will help social media managers to create and better capture value from their social media metrics.
Originality/value – The insights about the underlying factors that influence engagement with eWoM advance our understanding about the usage of online content.
Industrie 4.0 - Ausblick
(2016)
Für Unternehmen ist es wichtig, frühzeitig die strategischen Weichen für ihre Industrie 4.0-Stoßrichtung zu stellen und Erfahrung im Umgang mit Industrie 4.0-Technologien aufzubauen. Allerdings werden einige der Industrie 4.0-relevanten Technologien voraussichtlich erst in 5 bis 10 Jahren ihr Effizienzpotential voll ausschöpfen können. Die Einführung von Industrie 4.0 betrifft nahezu alle Bereiche eines Unternehmens und ist deshalb nicht nur als digitale Transformation, sondern auch als Kulturwandel in der Organisation zu verstehen, zu planen und aktiv zu managen. Themen wie Datenschutz und IT-Sicherheit sind nicht nur wichtige Voraussetzungen für eine erfolgreiche Industrie 4.0-Einführung, sondern müssen als wesentliche Akzeptanz- und Erfolgsfaktoren konsequent und durchgängig in den digitalen Systemen verankert werden.
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.
Wie sieht eine erfolgreiche Einführung von Industrie 4.0 aus? Dieses Buch stellt das Konzept, die Paradigmen und relevanten Technologien von Industrie 4.0 sowie deren Gesamtzusammenhänge systematisch vor. Entgegen der gängigen, rein technologischen und anwendungsbezogenen Betrachtungsweise, führt das Buch zusätzlich strategische, taktische und operative Betrachtungsebenen zu einem integrativen Strang zusammen. Zentrales Herzstück dabei ist ein Vorgehensmodell, das den Handlungsbedarf auf strategischer und operativer Ebene beschreibt. Ein Praxisfall, unterschiedliche Industrie 4.0-Use Cases und namhafte Experten aus Forschung und Praxis machen diese Lektüre interessant für Neueinsteiger, aber auch für Umsetzungsinteressierte des mittleren und oberen Managements, die eine neue Sichtweise auf die Komplexität des Themas gewinnen möchten. Das Glossar macht das Buch zum wertvollen Nachschlagewerk für das Thema Industrie 4.0.
KMUs sehen sich häufig aus finanziellen Gründen nicht in der Lage, in grundlegende Technologien der Industrie 4.0 zu investieren. So wird als Hauptvorbehalt eine vermeintlich schlechte Kosten-Nutzen-Relation bzw. langfristige Pay-Back-Zyklen angegeben. Die aktuellen Herausforderungen liegen derzeit eher bei der immer weiter voranschreitenden Internationalisierung sowie dem ansteigenden Innovationsdruck durch den Wettbewerb. Natürlich ist bekannt, dass die zunehmende Vernetzung der Produktionsanlagen in der Industrie 4.0 zudem Risiken in der IT- und Datensicherheit mit sich bringt. Auch Datenqualitäts-, Stabilitäts-, Schnittstellenprobleme oder rechtliche Probleme sind ausschlaggebend für die Verunsicherung der Unternehmen. Durch die zukünftig immer weiter ansteigende Vernetzung zwischen Unternehmen und Stakeholdern, müssen sich insbesondere Zulieferunternehmen in der Pflicht sehen, das Thema Industrie 4.0 aufzugreifen und sich damit auseinander zu setzen. Gerade diese Unternehmen müssen sich vor Augen führen, dass sie nur durch den zukünftigen Einsatz geeigneter Informations- und Kommunikationstechnologien noch in der Lage sein werden, Teil der Wertschöpfungskette zwischen ihren Kunden und Lieferanten zu sein.
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.
Wie digital ist ein Unternehmen aufgestellt? Wie weit ist es im Vergleich mit anderen Unternehmen der Branche? Um dies zu eruieren, eignen sich digitale Reifegradmodelle. Sie bieten eine Beschreibung der Ist-Situation, regen zur Reflexion über die wichtigen Fragen der Digitalisierung an und zeigen, welche Faktoren sich beeinflussen. Kontinuierlich eingesetzt lassen sie sich als Monitoring des digitalen Transformationsprozesses nutzen.
Based on well-established robotic concepts of autonomous localization and navigation we present a system prototype to assist camera-based indoor navigation for human utilization implemented in the Robot Operating System (ROS). Our prototype takes advantage of state-of-the-art computer vision and robotic methods. Our system is designed for assistive indoor guidance. We employ a vibro tactile belt to serve as a guiding device to render derived motion suggestions to the user via vibration patterns. We evaluated the effectiveness of a variety of vibro-tactile feedback patterns for guidance of blindfolded users. Our prototype demonstrates that a vision-based system can support human navigation, and may also assist the visually impaired in a human-centered way.
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.
Analysis is an important part of the enterprise architecture management process. Prior to decisions regarding transformation of the enterprise architecture, the current situation and the outcomes of alternative action plans have to be analysed. Many analysis approaches have been proposed by researchers and current enterprise architecture management tools implement analysis functionalities. However, few work has been done structuring and classifying enterprise architecture analysis approaches. This paper collects and extends existing classification schemes, presenting a framework for enterprise architecture analysis classification. For evaluation, a collection of enterprise architecture analysis approaches has been classified based on this framework. As a result, the description of these approaches has been assessed, a common set of important categories for enterprise architecture analysis classification has been derived and suggestions for further development are drawn.
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.
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.
This book presents emerging trends in the evolution of service-oriented and enterprise architectures. New architectures and methods of both business and IT are integrating services to support mobility systems, internet of things, ubiquitous computing, collaborative and adaptive business processes, big data, and cloud ecosystems. They inspire current and future digital strategies and create new opportunities for the digital transformation of next digital products and services. Services Oriented Architectures (SOA) and Enterprise Architectures (EA) have emerged as a useful framework for developing interoperable, large-scale systems, typically implementing various standards, like web services, REST, and microservices. Managing the adaptation and evolution of such systems presents a great challenge. Service-Oriented Architecture enables flexibility through loose coupling, both between the services themselves and between the IT organizations that manage them. Enterprises evolve continuously by transforming and extending their services, processes and information systems. Enterprise Architectures provide a holistic blueprint to help define the structure and operation of an organization with the goal of determining how an organization can most effectively achieve its objectives. The book proposes several approaches to address the challenges of the service-oriented evolution of digital enterprise and software architectures.
The evolution of Services Oriented Architectures (SOA) presents many challenges due to their complex, dynamic and heterogeneous nature. We describe how SOA design principles can facilitate SOA evolvability and examine several approaches to support SOA evolution. SOA evolution approaches can be classified based on the level of granularity they address, namely, service code level, service interaction level and model level. We also discuss emerging trends, such as microservices and knowledge-based support, which can enhance the evolution of future SOA systems.
The digital transformation of our society changes the way we live, work, learn, communicate, and collaborate. The digitization of software-intensive products and services is enabled basically by four megatrends: Cloud computing, big data mobile systems, and social technologies. This disruptive change interacts with all information processes and systems that are important business enablers for the current digital transformation. The internet of things, social collaboration systems for adaptive case management, mobility systems and services for big data in cloud services environments are emerging to support intelligent user-centered and social community systems. Modern enterprises see themselves confronted with an ever growing design space to engineer business models of the future as well as their IT support, respectively. The decision analytics in this field becomes increasingly complex and decision support, particularly for the development and evolution of sustainable enterprise architectures (EA), is duly needed. With the advent of intelligent user-centered and social community systems, the challenging decision processes can be supported in more flexible and intuitive ways. Tapping into these systems and techniques, the engineers and managers of the enterprise architecture become part of a viable enterprise, i.e. a resilient and continuously evolving system that develops innovative business models.
The Internet of Things (IoT), enterprise social networks, adaptive case management, mobility systems, analytics for big data, and cloud services environments are emerging to support smart connected products and services and the digital transformation. Biological metaphors of living and adaptable ecosystems with service oriented enterprise architectures provide the foundation for self-optimizing and resilient run-time environments for intelligent business services and related distributed information systems. We are investigating mechanisms for flexible adaptation and evolution for the next digital enterprise architecture systems in the context of the digital transformation. Our aim is to support flexibility and agile transformation for both business and related enterprise systems through adaptation and dynamical evolution of digital enterprise architectures. The present research paper investigates mechanisms for decision case management in the context of multi-perspective explorations of enterprise services and Internet of Things architectures by extending original enterprise architecture reference models with state of art elements for architectural engineering for the digitization and architectural decision support.
An enormous amount of data in the context of business processes is stored as images. They contain valuable information for business process management. Up to now this data had to be integrated manually into the business process. By advances of capturing it is possible to extract information from an increasing number of images. Therefore, we systematically investigate the potentials of Image Mining for business process management by a literature research and an in-depth analysis of the business process lifecycle. As a first step to evaluate our research, we developed a prototype for recovering process model information from drawings using Rapidminer.
Preface of IDEA 2015
(2016)
Digitization is more than using digital technologies to transfer data and perform computations and tasks. Digitization embraces disruptive effects of digital technologies on economy and society. To capture these effects, two perspectives are introduced, the product and the value-creation perspective. In the product perspective, digitization enables the transition from material, static products to interactive and configurable services. In the value-creation perspective, digitization facilitates the transition from centralized, isolated models of value creation, to bidirectional, co-creation oriented approaches of value creation.
The Internet of Things, enterprise social networks, adaptive case management, mobility systems, analytics for big data, and cloud services environments are emerging to support smart connected 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 enterprise architectures. We are investigating mechanisms for flexible adaptation and evolution for the next digital enterprise architecture systems in the context of the digital transformation. Our aim is to support flexibility and agile transformation for both business and related enterprise systems through adaptation and dynamical evolution of digital enterprise architectures. The present research paper investigates digital transformations of business and IT and integrates fundamental mappings between adaptable digital enterprise architectures and service-oriented information systems. We are putting a spotlight with the example domain – Internet of Things.
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.
In this note we look at anisotropic approximation of smooth functions on bounded domains with tensor product splines. The main idea is to extend such functions and then use known approximation techniques on Rd. We prove an error estimate for domains for which bounded extension operators exist. This obvious approach has some limitations. It is not applicable without restrictions on the chosen coordinate degree even if the domain is as simple as the unit disk. Further for approximation on Rd there are error estimates in which the grid widths and directional derivatives are paired in an interesting way. It seems impossible to maintain this property using extension operators.