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Over the last decades, a tremendous change toward using information technology in almost every daily routine of our lives can be perceived in our society, entailing an incredible growth of data collected day-by-day on Web, IoT, and AI applications.
At the same time, magneto-mechanical HDDs are being replaced by semiconductor storage such as SSDs, equipped with modern Non-Volatile Memories, like Flash, which yield significantly faster access latencies and higher levels of parallelism. Likewise, the execution speed of processing units increased considerably as nowadays server architectures comprise up to multiple hundreds of independently working CPU cores along with a variety of specialized computing co-processors such as GPUs or FPGAs.
However, the burden of moving the continuously growing data to the best fitting processing unit is inherently linked to today’s computer architecture that is based on the data-to-code paradigm. In the light of Amdahl's Law, this leads to the conclusion that even with today's powerful processing units, the speedup of systems is limited since the fraction of parallel work is largely I/O-bound.
Therefore, throughout this cumulative dissertation, we investigate the paradigm shift toward code-to-data, formally known as Near-Data Processing (NDP), which relieves the contention on the I/O bus by offloading processing to intelligent computational storage devices, where the data is originally located.
Firstly, we identified Native Storage Management as the essential foundation for NDP due to its direct control of physical storage management within the database. Upon this, the interface is extended to propagate address mapping information and to invoke NDP functionality on the storage device. As the former can become very large, we introduce Physical Page Pointers as one novel NDP abstraction for self-contained immutable database objects.
Secondly, the on-device navigation and interpretation of data are elaborated. Therefore, we introduce cross-layer Parsers and Accessors as another NDP abstraction that can be executed on the heterogeneous processing capabilities of modern computational storage devices. Thereby, the compute placement and resource configuration per NDP request is identified as a major performance criteria. Our experimental evaluation shows an improvement in the execution durations of 1.4x to 2.7x compared to traditional systems. Moreover, we propose a framework for the automatic generation of Parsers and Accessors on FPGAs to ease their application in NDP.
Thirdly, we investigate the interplay of NDP and modern workload characteristics like HTAP. Therefore, we present different offloading models and focus on an intervention-free execution. By propagating the Shared State with the latest modifications of the database to the computational storage device, it is able to process data with transactional guarantees. Thus, we achieve to extend the design space of HTAP with NDP by providing a solution that optimizes for performance isolation, data freshness, and the reduction of data transfers. In contrast to traditional systems, we experience no significant drop in performance when an OLAP query is invoked but a steady and 30% faster throughput.
Lastly, in-situ result-set management and consumption as well as NDP pipelines are proposed to achieve flexibility in processing data on heterogeneous hardware. As those produce final and intermediary results, we continue investigating their management and identified that an on-device materialization comes at a low cost but enables novel consumption modes and reuse semantics. Thereby, we achieve significant performance improvements of up to 400x by reusing once materialized results multiple times.
Intracranial brain tumors are one of the ten most common malignant cancers and account for substantial morbidity and mortality. The largest histological category of primary brain tumors is the gliomas which occur with an ultimate heterogeneous appearance and can be challenging to discern radiologically from other brain lesions. Neurosurgery is mostly the standard of care for newly diagnosed glioma patients and may be followed by radiation therapy and adjuvant temozolomide chemotherapy.
However, brain tumor surgery faces fundamental challenges in achieving maximal tumor removal while avoiding postoperative neurologic deficits. Two of these neurosurgical challenges are presented as follows. First, manual glioma delineation, including its sub-regions, is considered difficult due to its infiltrative nature and the presence of heterogeneous contrast enhancement. Second, the brain deforms its shape, called “brain shift,” in response to surgical manipulation, swelling due to osmotic drugs, and anesthesia, which limits the utility of pre-operative imaging data for guiding the surgery.
Image-guided systems provide physicians with invaluable insight into anatomical or pathological targets based on modern imaging modalities such as magnetic resonance imaging (MRI) and Ultrasound (US). The image-guided toolkits are mainly computer-based systems, employing computer vision methods to facilitate the performance of peri-operative surgical procedures. However, surgeons still need to mentally fuse the surgical plan from pre-operative images with real-time information while manipulating the surgical instruments inside the body and monitoring target delivery. Hence, the need for image guidance during neurosurgical procedures has always been a significant concern for physicians.
This research aims to develop a novel peri-operative image-guided neurosurgery (IGN) system, namely DeepIGN, that can achieve the expected outcomes of brain tumor surgery, thus maximizing the overall survival rate and minimizing post-operative neurologic morbidity. In the scope of this thesis, novel methods are first proposed for the core parts of the DeepIGN system of brain tumor segmentation in MRI and multimodal pre-operative MRI to the intra-operative US (iUS) image registration using the recent developments in deep learning. Then, the output prediction of the employed deep learning networks is further interpreted and examined by providing human-understandable explainable maps. Finally, open-source packages have been developed and integrated into widely endorsed software, which is responsible for integrating information from tracking systems, image visualization, image fusion, and displaying real-time updates of the instruments relative to the patient domain.
The components of DeepIGN have been validated in the laboratory and evaluated in the simulated operating room. For the segmentation module, DeepSeg, a generic decoupled deep learning framework for automatic glioma delineation in brain MRI, achieved an accuracy of 0.84 in terms of the dice coefficient for the gross tumor volume. Performance improvements were observed when employing advancements in deep learning approaches such as 3D convolutions over all slices, region-based training, on-the-fly data augmentation techniques, and ensemble methods.
To compensate for brain shift, an automated, fast, and accurate deformable approach, iRegNet, is proposed for registering pre-operative MRI to iUS volumes as part of the multimodal registration module. Extensive experiments have been conducted on two multi-location databases: the BITE and the RESECT. Two expert neurosurgeons conducted additional qualitative validation of this study through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that the proposed iRegNet is fast and achieves state-of-the-art accuracies. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images, as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance.
For the explainability module, the NeuroXAI framework is proposed to increase the trust of medical experts in applying AI techniques and deep neural networks. The NeuroXAI includes seven explanation methods providing visualization maps to help make deep learning models transparent. Experimental findings showed that the proposed XAI framework achieves good performance in extracting both local and global contexts in addition to generating explainable saliency maps to help understand the prediction of the deep network. Further, visualization maps are obtained to realize the flow of information in the internal layers of the encoder-decoder network and understand the contribution of MRI modalities in the final prediction. The explainability process could provide medical professionals with additional information about tumor segmentation results and therefore aid in understanding how the deep learning model is capable of processing MRI data successfully.
Furthermore, an interactive neurosurgical display has been developed for interventional guidance, which supports the available commercial hardware such as iUS navigation devices and instrument tracking systems. The clinical environment and technical requirements of the integrated multi-modality DeepIGN system were established with the ability to incorporate: (1) pre-operative MRI data and associated 3D volume reconstructions, (2) real-time iUS data, and (3) positional instrument tracking. This system's accuracy was tested using a custom agar phantom model, and its use in a pre-clinical operating room is simulated. The results of the clinical simulation confirmed that system assembly was straightforward, achievable in a clinically acceptable time of 15 min, and performed with a clinically acceptable level of accuracy.
In this thesis, a multimodality IGN system has been developed using the recent advances in deep learning to accurately guide neurosurgeons, incorporating pre- and intra-operative patient image data and interventional devices into the surgical procedure. DeepIGN is developed as open-source research software to accelerate research in the field, enable ease of sharing between multiple research groups, and continuous developments by the community. The experimental results hold great promise for applying deep learning models to assist interventional procedures - a crucial step towards improving the surgical treatment of brain tumors and the corresponding long-term post-operative outcomes.
Supply chains have evolved into dynamic, interconnected supply networks, which increases the complexity of achieving end-to-end traceability of object flows and their experienced events. With its capability to ensure a secure, transparent, and immutable environment without relying on a trusted third party, the emerging blockchain technology shows strong potential to enable end-to-end traceability in such complex multitiered supply networks. However, as the dissertation’s systematic literature review reveals, the currently available blockchain-based traceability solutions lack the ability to map object-related supply chain events holistically, which involves mapping objects’ creation and deletion, aggregation and disaggregation, transformation, and transaction. Therefore, this dissertation proposes a novel blockchain-based traceability architecture that integrates governance and token concepts to overcome the limitations of existing architectures. While the governance concept manages the supply chain structure on an application level, the token concept includes all functions to conduct object-related supply chain events. For this to be possible, this dissertation’s token concept introduces token ‘blueprints’, which allow clients to group tokens into different types, where tokens of the same type are non-fungible. Furthermore, blueprints can include minting conditions, which are, for example, necessary when mapping assembly or delivery processes. In addition, the token concept contains logic for reflecting all conducted object-related events in an integrated token history. This ultimately leads to end-to-end traceability of tokens and their physical or abstract representatives on the blockchain. For validation purposes, this dissertation implements the architecture’s components and their update and request relationships in code and proves its applicability based on the Ethereum blockchain. Finally, this dissertation provides a scenario-based evaluation based on two industrial case studies from a manufacturing and logistics perspective to validate the architecture’s capabilities when applied in real-world industrial settings. The proposed blockchain-based traceability architecture thus covers all object-related supply chain events derived from the two industrial case studies and therefore proves its general-purpose end-to-end traceability capabilities of object flows.
Development of an indoor positioning system to create a digital shadow of production plant layouts
(2023)
The objective of this dissertation is to develop an indoor positioning system that allows the creation of a digital shadow of the plant layout in order to continuously represent the actual state of the physical layout in the virtual space. In order to define the requirements for such a system, potential stakeholders who could benefit from a digital shadow in the context of the plant layout were analysed. In order to generate added value for their work, the requirements were derived from their perspective. As the core of an indoor positioning system is the sensory aspect to capture the physical layout parameters, different potential technologies were compared and evaluated in terms of their suitability for this particular application. Derived from this analysis, the selected concept is based on the use of a pan-tilt-zoom (PTZ) camera in combination with fiducial markers. In order to determine specific camera parameters, a series of experiments were conducted which were necessary to develop the measurement method as well as the mathematical calculation method and coordinate transformation for the determination of poses (positions and angular orientations) of the respective facilities in the plant. In addition, an experimental validation was performed to ensure that the limit values for individual parameters determined in the requirements analysis can be met.
Increasing concerns regarding the world´s natural resources and sustainability continue to be a major issue for global development. As a result several political initiatives and strategies for green or resource-efficient growth both on national and international levels have been proposed. A core element of these initiatives is the promotion of an increase of resource or material productivity. This dissertation examines material productivity developments in the OECD and BRICS countries between 1980 and 2008. By applying the concept of convergence stemming from economic growth theory to material productivity the analysis provides insights into both aspects: material productivity developments in general as well potentials for accelerated improvements in material productivity which consequently may allow a reduction of material use globally. The results of the convergence analysis underline the importance of policy-making with regard to technology and innovation policy enabling the production of resource-efficient products and services as well as technology transfer and diffusion.
Die Globalisierung hat die Emergenz neuer Formen gesellschaftlicher Steuerung vorangetrieben. Es entstehen sowohl neue Formen von globalen Regeln als auch neue Akteurskonstellationen zur Setzung und Durchsetzung dieser Regeln. Politische, wirtschaftliche und gesellschaftliche Akteure – internationale Organisationen, transnationale Unternehmen und Nichtregierungsorganisationen – gewinnen an Einfluss.
In diesem Kontext werden zunehmend Multistakeholder-Dialoge initiiert, in denen sich relevante Akteure aus Politik, Wirtschaft und Gesellschaft organisieren, um Lösungsansätze für globale Probleme u.a. durch die Erarbeitung von Richtlinien und Standards zu entwickeln. Diese Formen gesellschaftlicher Regulierung zeichnen sich dadurch aus, dass neue Organisationsstrukturen und Verfahrensregeln implementiert, neue Rollen gelernt und neue Akteure integriert werden müssen.
In diesem Buch werden die Governancestrukturen von Multistakeholder-Dialogen zur Führung, Steuerung und Kontrolle solcher Kooperationsprojekte analysiert. Ein aktuelles Beispiel für ein transnationales und transkulturelles Kooperationsprojekt dieser Art ist der von der ‚International Organization for Standardization‘ (ISO) initiierte Prozess zur Erarbeitung einer Norm zur gesellschaftlichen Verantwortung von Organisationen (‚Social Responsibility‘). Die im November 2010 veröffentlichte ISO 26000-Norm richtet sich an alle Arten von Organisationen im öffentlichen und gemeinnützigen Sektor und in der Privatwirtschaft – weltweit und unabhängig von ihrer Größe. Dieser Multistakeholder-Dialog wird theoretisch rekonstruiert und empirisch analysiert. Die theoretische Perspektive ist bestimmt durch eine kulturalistisch informierte Governanceökonomik und -ethik, die auf der Basis eines verallgemeinerten Stakeholderbegriffs operiert. Die empirische Analyse konzentriert sich auf die Mikrogovernance zur Steuerung deliberativer Multistakeholder-Dialoge.
Die interdisziplinär angelegte Studie, ihr argumentativer Gang und ihre Ergebnisse sind sowohl für die strategische Führung von Unternehmen als auch für die Gestaltung politischer Prozesse von großem Interesse. Sie leistet einen Beitrag zur aktuellen gesellschaftlichen Diskussion um die Verantwortlichkeit und Nachhaltigkeit von Unternehmen in der globalisierten Gesellschaft.
Customer orientation should be the core engine of every organisation. Information technology can be considered as the enabler to generate competitive advantages through customer processes in marketing, sales and service. The impact of information technologies is the biggest risk and at the same time a huge opportunity for any organisation. Research shows that Customer Relationship Management (CRM) enables organisations to perform better and focus more on their customers (e.g. market capitalisation of Amazon). While global enterprises are shaping the future of customer centricity and information technology, the question arises how German B2B organisations can shift their value contribution from product-centric to customer-centric. Therefore, these organisations are attempting to implement CRM software and putting their customers more into focus. However, the question remains, how organisations are approaching the implementation of CRM and if these attempts are paying off in terms of business performance.
Contributing to this highly topical discussion, this thesis contributes to the body of knowledge about the implementation of CRM in the German B2B sector and how it impacts their business performance. First, theoretical frameworks have been developed based on an extensive literature review. Hereby different aspects of CRM are worked-out and mapped against three dimensions of business performance, namely process efficiency, customer satisfaction and financial performance. Based on the theory, a conceptual framework was developed to test the relationships between CRM and Business Performance (BP). Therefore, a survey with 500 participants has been conducted. Based on this a measurement model was developed to test five main hypotheses.
The findings of these hypotheses suggest, that the implementation of CRM positively impacts business performance. In specific, the usage of analytical CRM and the establishment of a dedicated CRM success measurement correlate with the performance of German B2B organisations. In addition to these main findings, various key statements could be derived from the research and a measurement model was developed, which can be used for different organisational characteristics assessing BP. As a result, CRM implementations can be enhanced, and business performance can be improved.
Unternehmen stehen aktuell aufgrund der Digitalisierung, des stetigen technologischen Fortschritts und immer kürzer werdenden Produktlebenszyklen vor großen Herausforderungen. Um am Markt bestehen zu können, müssen Geschäftsmodelle öfter und schneller an sich verändernde Marktverhältnisse angepasst werden als dies früher der Fall war. Eine schnelle Anpassungsfähigkeit, auch Agilität genannt, ist in der heutigen Zeit ein entscheidender Wettbewerbsfaktor. Aufgrund des stetig wachsenden IT Anteils in Produkten sowie der Tatsache, dass diese IT-gestützt hergestellt werden, haben Änderungen des Geschäftsmodells große Auswirkungen auf die Unternehmensarchitektur eines Unternehmens.
Eine Unternehmensarchitektur umspannt das Unternehmen, indem diese die fachlichen und technischen Strukturen, insbesondere die gesamte IT, des Unternehmens beinhaltet und integriert. Das Management der Unternehmensarchitektur ist die Disziplin zur Beherrschung und Abstimmung dieser Strukturen. An der Gestaltung der Unternehmensarchitektur wirken viele Stakeholder mit individuellen und teils gegensätzlichen Interessen aus den unterschiedlichsten Bereichen des Unternehmens mit. Dies macht die Entscheidungsfindung zu einer komplexen Aufgabe.
Die in dieser Arbeit entworfene integrative Methode für die Entscheidungsfindung hat das Ziel, die Betroffenen und Beteiligten, im Folgenden Stakeholder, bei ihren Entscheidungen zu unterstützen. Die Grundidee hierbei ist die systematische Einbeziehung der Interessen der Stakeholder und davon abgeleiteter Visualisierungen. Dies verleiht der Methode ihren integrativen Charakter und hilft Abhängigkeiten zwischen Stakeholdern zu erkennen. Dadurch wird die Zusammenarbeit zwischen den an Entscheidungen beteiligten Stakeholdern gefördert. Neben der systematischen Einbeziehung von Visualisierungen wird im Rahmen dieser Arbeit das Konzept der Technik eingeführt. Techniken werden ebenfalls von den Interessen der Stakeholder abgeleitet und dienen der Unterstützung bei der Durchführung von Aktivitäten der Entscheidungsfindung, indem Vorgehensweisen bei bestimmten Aufgaben vorgegeben oder Teilprozesse der Entscheidungsfindung sogar automatisiert durchgeführt werden. Das Konzept der Technik, die systematische Ableitung von den Interessen der Stakeholder sowie das Zusammenspiel mit Visualisierungen wird in dieser Arbeit in Form einer erweiterten Konzeptualisierung der Architekturbeschreibung definiert.
Da die Werkzeugunterstützung in der Praxis häufig eine Herausforderung darstellt, rundet diese Arbeit ein eigens konzipiertes und prototypisch validiertes Architekturcockpit ab. Das Cockpit ist eine auf einem elektronischen Sitzungsraum basierende Werkzeugunterstützung der eingeführten integrativen Methode.
Im Fokus der Arbeit steht die Unterstützung der Stentgraftauswahl bei endovaskulärer Versorgung eines infrarenalen Aortenaneurysmas. Im Rahmen der Arbeit wurde eine Methode zur Auswertung von Ergebnissen einer Finite Elemente-Analyse zum Stentgraftverhalten konzipiert, implementiert und im Rahmen einer deutschlandweiten Benutzerstudie mit 16 Chirurgen diskutiert. Die entwickelte Mensch-Maschine-Schnittstelle ermöglicht dem Gefäßmediziner eine interaktive Analyse berechneter Fixierungskräfte und Kontaktzustände mehrerer Stentgrafts im Kontext mit dem zu behandelnden Aortenabschnitt. Die entwickelte Methode ermöglicht eine tiefergehende Auseinandersetzung der Mediziner mit numerischen Simulationen und Stentgraftbewertungsgrößen. Hierdurch konnte im Rahmen der Benutzerstudie das Einsatzpotenzial numerischer Simulationen zur Unterstützung der Stentgraftauswahl ermittelt und eine Anforderungsspezifikation an ein System zur simulationsbasierten Stentgraftplanung definiert werden. Im Ergebnis wurde als wesentliches Einsatzpotenzial die Festlegung eines Mindestmaßes an Überdimensionierung, die Optimierung der Schenkellänge von bifurkativen Stentgrafts sowie der Vergleich unterschiedlicher Stentgraftdesigns ermittelt. Zu den wesentlichen Funktionen eines Systems zur simulationsbasierten Stentgraftauswahl gehören eine Übersichtskarte zu farbkodiertem Migrationsrisiko pro Stentgraft und Landungszone, die Visualisierung des Abdichtungszustandes der Stentkomponenten sowie die Darstellung von Stentgraft- und Gefäßdeformationen im 3D-Modell.
High Performance Computing (HPC) enables significant progress in both science and industry. Whereas traditionally parallel applications have been developed to address the grand challenges in science, as of today, they are also heavily used to speed up the time-to-result in the context of product design, production planning, financial risk management, medical diagnosis, as well as research and development efforts. However, purchasing and operating HPC clusters to run these applications requires huge capital expenditures as well as operational knowledge and thus is reserved to large organizations that benefit from economies of scale. More recently, the cloud evolved into an alternative execution environment for parallel applications, which comes with novel characteristics such as on-demand access to compute resources, pay-per-use, and elasticity. Whereas the cloud has been mainly used to operate interactive multi-tier applications, HPC users are also interested in the benefits offered. These include full control of the resource configuration based on virtualization, fast setup times by using on-demand accessible compute resources, and eliminated upfront capital expenditures due to the pay-per-use billing model. Additionally, elasticity allows compute resources to be provisioned and decommissioned at runtime, which allows fine-grained control of an application's performance in terms of its execution time and efficiency as well as the related monetary costs of the computation. Whereas HPC-optimized cloud environments have been introduced by cloud providers such as Amazon Web Services (AWS) and Microsoft Azure, existing parallel architectures are not designed to make use of elasticity. This thesis addresses several challenges in the emergent field of High Performance Cloud Computing. In particular, the presented contributions focus on the novel opportunities and challenges related to elasticity. First, the principles of elastic parallel systems as well as related design considerations are discussed in detail. On this basis, two exemplary elastic parallel system architectures are presented, each of which includes (1) an elasticity controller that controls the number of processing units based on user-defined goals, (2) a cloud-aware parallel execution model that handles coordination and synchronization requirements in an automated manner, and (3) a programming abstraction to ease the implementation of elastic parallel applications. To automate application delivery and deployment, novel approaches are presented that generate the required deployment artifacts from developer-provided source code in an automated manner while considering application-specific non-functional requirements. Throughout this thesis, a broad spectrum of design decisions related to the construction of elastic parallel system architectures is discussed, including proactive and reactive elasticity control mechanisms as well as cloud-based parallel processing with virtual machines (Infrastructure as a Service) and functions (Function as a Service). To evaluate these contributions, extensive experimental evaluations are presented.