Data management and Data Pipelines: An empirical investigation in the embedded systems domain

Context: Companies are increasingly collecting data from all possible sources to extract insights that help in data-driven decision-making. Increased data volume, variety, and velocity and the impact of poor quality data on the development of data products are leading companies to look for an improved data management approach that can accelerate the development of high-quality data products. Further, AI is being applied in a growing number of fields, and thus it is evolving as a horizontal technology. Consequently, AI components are increasingly been integrated into embedded systems along with electronics and software. We refer to these systems as AI-enhanced embedded systems. Given the strong dependence of AI on data, this expansion also creates a new space for applying data management techniques. Objective: The overall goal of this thesis is to empirically identify the data management challenges encountered during the development and maintenance of AI-enhanced embedded systems, propose an improved data management approach and empirically validate the proposed approach.Method: To achieve the goal, we conducted this research in close collaboration with Software Center companies using a combination of different empirical research methods: case studies, literature reviews, and action research.Results and conclusions: This research provides five main results. First, it identifies key data management challenges specific to Deep Learning models developed at embedded system companies. Second, it examines the practices such as DataOps and data pipelines that help to address data management challenges. We observed that DataOps is the best data management practice that improves the data quality and reduces the time tdevelop data products. The data pipeline is the critical component of DataOps that manages the data life cycle activities. The study also provides the potential faults at each step of the data pipeline and the corresponding mitigation strategies. Finally, the data pipeline model is realized in a small piece of data pipeline and calculated the percentage of saved data dumps through the implementation.Future work: As future work, we plan to realize the conceptual data pipeline model so that companies can build customized robust data pipelines. We also plan to analyze the impact and value of data pipelines in cross-domain AI systems and data applications. We also plan to develop AI-based fault detection and mitigation system suitable for data pipelines

Cell Nuclear Morphology Analysis Using 3D Shape Modeling, Machine Learning and Visual Analytics

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with cell differentiation, development, proliferation, and disease. Changes in the nuclear form are associated with reorganization of chromatin architecture related to altered functional properties such as gene regulation and expression. Understanding these processes through quantitative analysis of morphological changes is important not only for investigating nuclear organization, but also has clinical implications, for example, in detection and treatment of pathological conditions such as cancer. While efforts have been made to characterize nuclear shapes in two or pseudo-three dimensions, several studies have demonstrated that three dimensional (3D) representations provide better nuclear shape description, in part due to the high variability of nuclear morphologies. 3D shape descriptors that permit robust morphological analysis and facilitate human interpretation are still under active investigation. A few methods have been proposed to classify nuclear morphologies in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. There is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analyses. In this work, we address a number of these existing limitations. First, we present a largest publicly available, to-date, 3D microscopy imaging dataset for cell nuclear morphology analysis and classification. We provide a detailed description of the image analysis protocol, from segmentation to baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. We proposed a specific cross-validation scheme that accounts for possible batch effects in data. Second, we propose a new technique that combines mathematical modeling, machine learning, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. Employing robust and smooth surface reconstruction methods to accurately approximate 3D object boundary enables the establishment of homologies between different biological shapes. Then, we compute geometric morphological measures characterizing the form of cell nuclei and nucleoli. We combine these methods into a highly parallel computational pipeline workflow for automated morphological analysis of thousands of nuclei and nucleoli in 3D. We also describe the use of visual analytics and deep learning techniques for the analysis of nuclear morphology data. Third, we evaluate proposed methods for 3D surface morphometric analysis of our data. We improved the performance of morphological classification between epithelial vs mesenchymal human prostate cancer cells compared to the previously reported results due to the more accurate shape representation and the use of combined nuclear and nucleolar morphometry. We confirmed previously reported relevant morphological characteristics, and also reported new features that can provide insight in the underlying biological mechanisms of pathology of prostate cancer. We also assessed nuclear morphology changes associated with chromatin remodeling in drug-induced cellular reprogramming. We computed temporal trajectories reflecting morphological differences in astroglial cell sub-populations administered with 2 different treatments vs controls. We described specific changes in nuclear morphology that are characteristic of chromatin re-organization under each treatment, which previously has been only tentatively hypothesized in literature. Our approach demonstrated high classification performance on each of 3 different cell lines and reported the most salient morphometric characteristics. We conclude with the discussion of the potential impact of method development in nuclear morphology analysis on clinical decision-making and fundamental investigation of 3D nuclear architecture. We consider some open problems and future trends in this field.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147598/1/akalinin_1.pd

Aligning the Qualitative Comparative Analysis (QCA) counterfactual approach with the practice of retroduction: some preliminary insights

This study offers fresh ontological insights by examining generative causality through the Qualitative Comparative Analysis (QCA) counterfactual lens, in conjunction with Critical Realism and the practice of retroduction. Specifically, it claims that Information Systems (IS) researchers could retroduce generative mechanisms by leveraging the QCA counterfactual approach to causation because retroduction is about conjecturing hypothetical mechanisms that would generate the outcome of interest in a counterfactual fashion. Drawing on an example of typological theorising, this study calls for a renewed effort in the use of retroduction in the study of IS phenomena. In addition, this study sheds new light on the overarching approach for conducting Critical Realist (case study) research. A number of theoretical, methodological, and practical implications are discussed

Creating intelligible metrics road traffic analysis

Dissertação de mestrado em Computer ScienceThe increasing pervasiveness and lower cost of electronic devices equipped with sensors is leading to a greater and cheaper availability of localized information. The advent of the internet has brought phenomena such as crowd-sourced maps and related data. The combination of the availability of mobile information, community built maps, with the added convenience of retrieving information over the internet creates the opportunity to contextualize data in new ways. This work takes that opportunity and attempts to generalize the detection of driving events which are deemed problematic as a function of contextual factors, such as neighbouring buildings, areas, amenities, the weather, and the time of day, week or month. In order to research the problem at hand, the issue is first contextualized properly, providing an overview of important factors, namely Smart Cities, Data Fusion, and Machine Learning. That is followed by a chapter concerning the state of the art, that showcases related projects and how the various facets of road traffic expression are being approached. The focus is then turned to creating a solution. At first this consists in aggregating data so as to create a richer context than would be present otherwise, this includes the retrieval from different services, as well as the composition of a unique view of the same driving situation with new dimensions added to it. And then Models were created using different Machine Learning methods, and a comparison of results according to selected and justified evaluation metrics was made. The compared Methods are Decision Tree, Naive Bayes, and Support Vector Machine. The different types of information were evaluated on their own as potential classifiers and then were evaluated together, leading to the conclusion that the various types combined allow for the creation of better models capable of finding problems with more confidence in such results. According to the tests performed the chosen approach can improve the performance over a baseline approach and point out problematic situations with a precision of over 90%. As expected by not using factors concerning the driver state or acceleration the scope of problems which are detected is limited in domain.A expansão e menor custo de dispositivos eletrónicos equipados com sensores está a levar a uma maior e mais barata disponibilidade de informação localizada. O advento da internet criou fenómenos como a criação de mapas e dados relacionados gerados por comunidades. A combinação da disponibilidade de informação móvel e mapas construídos pela comunidade, em conjunto com uma obtenção de informação através da internet mais conveniente, criou a oportunidade de contextualizar os dados de novas maneiras. Este trabalho faz uso dessa oportunidade e tenta generalizar eventos de condução que são considerados problemáticos em função de factores contextuais, tais como a presença de edifícios, áreas, e comodidades na vizinhança, o clima, e a hora do dia, a semana, ou o mês. De modo a investigar esta questão, o problema é contextualizado como emergente no tópico de Cidades Inteligentes, e explorado com recurso a Fusão de Dados e a Aprendizagem Máquina. O estado da arte é exposto, através de projectos relacionados à expressão do tráfego rodoviário, dando relevo às várias facetas até então investigadas por outros autores de modo a enquadrar o trabalho presente. Dado o enquadramento e concretização do problema, é proposta uma solução. Esta solução passa por inicialmente agregar dados de modo a enriquecer o contexto, incluindo a recolha destes de vários serviços, e uma composição dos dados recolhidos numa perspectiva única referente a uma situação de condução. Após este enriquecimento dos dados, são criados modelos com base em diferentes técnicas de Aprendizagem Máquina. Os métodos utilizados são Decision Tree, Naive Bayes, e Support Vector Machine. Os resultados conseguidos com estes modelos são depois comparados de acordo com as métricas de avaliação seleccionadas. Uma comparação foi feita também com diferentes tipos de informação separadamente e também em conjunto, levando à conclusão de que os vários tipos combinados permitem a criação de melhores modelos capazes de encontrar problemas com mais confiança nos resultados produzidos. De acordo com os testes executados a abordagem escolhida consegue melhorar resultados de um modelo base e descobrir situações problemáticas de condução com uma precisão acima dos 90%. No entanto, como seria de esperar, o âmbito dos problemas detectados tem um domínio limitado aos aspectos seleccionados

Smart Service Innovation: Organization, Design, and Assessment

Background: The emergence of technologies such as the Internet of Things, big data, cloud computing, and wireless communication drives the digital transformation of the entire society. Organizations can exploit these potentials by offering new data-driven services with innovative value propositions, such as carsharing, remote equipment maintenance, and energy management services. These services result from value co-creation enabled by smart service systems, which are configurations of people, processes, and digital technologies. However, developing such systems was found to be challenging in practice. This is mainly due to the difficulties of managing complexity and uncertainty in the innovation process, as contributions of various actors from multiple disciplines must be coordinated. Previous research in service innovation and service systems engineering (SSE) has not shed sufficient light on the specifics of smart services, while research on smart service systems lacks empirical grounding. Purpose: This thesis aims to advance the understanding of the systematic development of smart services in multi-actor settings by investigating how smart service innovation (SSI) is conducted in practice, particularly regarding the participating actors, roles they assume, and methods they apply for designing smart service systems. Furthermore, the existing set of methods is extended by new methods for the design-integrated assessment of smart services and service business models. Approach: Empirical and design science methods were combined to address the research questions. To explore how SSI is conducted in practice, 25 interviews with experts from 13 organizations were conducted in two rounds. Building on service-dominant logic (SDL) as a theoretical foundation and a multi-level framework for SSI, the involvement of actors, their activities, employed means, and experienced challenges were collected. Additionally, a case study was used to evaluate the suitability of the Lifecycle Modelling Language to describe smart service systems. Design science methods were applied to determine a useful combination of service design methods and to build meta-models and tools for assessing smart services. They were evaluated using experiments and the talk aloud method. Results: On the macro-level, service ecosystems consist of various actors that conduct service innovation through the reconfiguration of resources. Collaboration of these actors is facilitated on the meso-level within a project. The structure and dynamics of project configurations can be described through a set of roles, innovation patterns, and ecosystem states. Four main activities have been identified, which actors perform to reduce uncertainty in the project. To guide their work, actors apply a variety of means from different disciplines to develop and document work products. The approach of design-integrated business model assessment is enabled through a meta-model that links qualitative aspects of service architectures and business models with quantitative assessment information. The evaluation of two tool prototypes showed the feasibility and benefit of this approach. Originality / Value: The results reported in this thesis advance the understanding of smart service innovation. They contribute to evidence-based knowledge on service systems engineering and its embedding in service ecosystems. Specifically, the consideration of actors, roles, activities, and methods can enhance existing reference process models. Furthermore, the support of activities in such processes through suitable methods can stimulate discussions on how methods from different disciplines can be applied and combined for developing the various aspects of smart service systems. The underlying results help practitioners to better organize and conduct SSI projects. As potential roles in a service ecosystem depend on organizational capabilities, the presented results can support the analysis of ex¬ternal dependencies and develop strategies for building up internal competencies.:Abstract iii Content Overview iv List of Abbreviations viii List of Tables x List of Figures xii PART A - SYNOPSIS 1 1 Introduction 2 1.1 Motivation 2 1.2 Research Objectives and Research Questions 4 1.3 Thesis Structure 6 2 Research Background 7 2.1 Smart Service Systems 7 2.2 Service-Dominant Logic 8 2.3 Service Innovation in Ecosystems 11 2.4 Systematic Development of Smart Service Systems 13 3 Research Approach 21 3.1 Research Strategy 21 3.2 Applied Research Methods 22 4 Summary of Findings 26 4.1 Overview of Research Results 26 4.2 Organizational Setup of Multi-Actor Smart Service Innovation 27 4.3 Conducting Smart Service Innovation Projects 32 4.4 Approaches for the Design-integrated Assessment of Smart Services 39 5 Discussion 44 5.1 Contributions 44 5.2 Limitations 46 5.3 Managerial Implications 47 5.4 Directions for Future Research 48 6 Conclusion 54 References 55 PART B - PUBLICATIONS 68 7 It Takes More than Two to Tango: Identifying Roles and Patterns in Multi-Actor Smart Service Innovation 69 7.1 Introduction 69 7.2 Research Background 72 7.3 Methodology 76 7.4 Results 79 7.5 Discussion 90 7.6 Conclusions and Outlook 96 7.7 References 97 8 Iterative Uncertainty Reduction in Multi-Actor Smart Service Innovation 100 8.1 Introduction 100 8.2 Research Background 103 8.3 Research Approach 109 8.4 Findings 113 8.5 Discussion 127 8.6 Conclusions and Outlook 131 8.7 References 133 9 How to Tame the Tiger – Exploring the Means, Ends, and Challenges in Smart Service Systems Engineering 139 9.1 Introduction 139 9.2 Research Background 140 9.3 Methodology 143 9.4 Results 145 9.5 Discussion and Conclusions 151 9.6 References 153 10 Combining Methods for the Design of Digital Services in Practice: Experiences from a Predictive Costing Service 156 10.1 Introduction 156 10.2 Conceptual Foundation 157 10.3 Preparing the Action Design Research Project 158 10.4 Application and Evaluation of Methods 160 10.5 Discussion and Formalization of Learning 167 10.6 Conclusion 169 10.7 References 170 11 Modelling of a Smart Service for Consumables Replenishment: A Life Cycle Perspective 171 11.1 Introduction 171 11.2 Life Cycles of Smart Services 173 11.3 Case Study 178 11.4 Discussion of the Modelling Approach 185 11.5 Conclusion and Outlook 187 11.6 References 188 12 Design-integrated Financial Assessment of Smart Services 192 12.1 Introduction 192 12.2 Problem Analysis 195 12.3 Meta-Model Design 200 12.4 Application of the Meta-Model in a Tool Prototype 204 12.5 Evaluation 206 12.6 Discussion 208 12.7 Conclusions 209 12.8 References 211 13 Towards a Cost-Benefit-Analysis of Data-Driven Business Models 215 13.1 Introduction 215 13.2 Conceptual Foundation 216 13.3 Methodology 218 13.4 Case Analysis 220 13.5 A Cost-Benefit-Analysis Model for DDBM 222 13.6 Conclusion and Outlook 225 13.7 References 226 14 Enabling Design-integrated Assessment of Service Business Models Through Factor Refinement 228 14.1 Introduction 228 14.2 Related Work 229 14.3 Research Goal and Method 230 14.4 Solution Design 231 14.5 Demonstration 234 14.6 Discussion 235 14.7 Conclusion 236 14.8 References 23

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants

Intelligent Systems for Sustainable Person-Centered Healthcare

This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life