TY - THES A1 - Niklaus, Christina T1 - From Complex Sentences to a Formal Semantic Representation using Syntactic Text Simplification and Open Information Extraction N2 - Sentences that present a complex linguistic structure act as a major stumbling block for Natural Language Processing (NLP) applications whose predictive quality deteriorates with sentence length and complexity. The task of Text Simplification (TS) may remedy this situation. It aims to modify sentences in order to make them easier to process, using a set of rewriting operations, such as reordering, deletion or splitting. These transformations are executed with the objective of converting the input into a simplified output, while preserving its main idea and keeping it grammatically sound. State-of-the-art syntactic TS approaches suffer from two major drawbacks: first, they follow a very conservative approach in that they tend to retain the input rather than transforming it, and second, they ignore the cohesive nature of texts, where context spread across clauses or sentences is needed to infer the true meaning of a statement. To address these problems, we present a discourse-aware TS framework that is able to split and rephrase complex English sentences within the semantic context in which they occur. By generating a fine-grained output with a simple canonical structure that is easy to analyze by downstream applications, we tackle the first issue. For this purpose, we decompose a source sentence into smaller units by using a linguistically grounded transformation stage. The result is a set of selfcontained propositions, with each of them presenting a minimal semantic unit. To address the second concern, we suggest not only to split the input into isolated sentences, but to also incorporate the semantic context in the form of hierarchical structures and semantic relationships between the split propositions. In that way, we generate a semantic hierarchy of minimal propositions that benefits downstream Open Information Extraction (IE) tasks. To function well, the TS approach that we propose requires syntactically well-formed input sentences. It targets generalpurpose texts in English, such as newswire or Wikipedia articles, which commonly contain a high proportion of complex assertions. In a second step, we present a method that allows state-of-the-art Open IE systems to leverage the semantic hierarchy of simplified sentences created by our discourseaware TS approach in constructing a lightweight semantic representation of complex assertions in the form of semantically typed predicate-argument structures. In that way, important contextual information of the extracted relations is preserved that allows for a proper interpretation of the output. Thus, we address the problem of extracting incomplete, uninformative or incoherent relational tuples that is commonly to be observed in existing Open IE approaches. Moreover, assuming that shorter sentences with a more regular structure are easier to process, the extraction of relational tuples is facilitated, leading to a higher coverage and accuracy of the extracted relations when operating on the simplified sentences. Aside from taking advantage of the semantic hierarchy of minimal propositions in existing Open IE Abstract approaches, we also develop an Open IE reference system, Graphene. It implements a relation extraction pattern upon the simplified sentences. The framework we propose is evaluated within our reference TS implementation DisSim. In a comparative analysis, we demonstrate that our approach outperforms the state of the art in structural TS both in an automatic and a manual analysis. It obtains the highest score on three simplification datasets from two different domains with regard to SAMSA (0.67, 0.57, 0.54), a recently proposed metric targeted at automatically measuring the syntactic complexity of sentences which highly correlates with human judgments on structural simplicity and grammaticality. These findings are supported by the ratings from the human evaluation, which indicate that our baseline implementation DisSim returns fine-grained simplified sentences that achieve a high level of syntactic correctness and largely preserve the meaning of the input. Furthermore, a comparative analysis with the annotations contained in the RST Discourse Treebank (RST-DT) reveals that we are able to capture the contextual hierarchy between the split sentences with a precision of approximately 90% and reach an average precision of almost 70% for the classification of the rhetorical relations that hold between them. Finally, an extrinsic evaluation shows that when applying our TS framework as a pre-processing step, the performance of state-ofthe-art Open IE systems can be improved by up to 32% in precision and 30% in recall of the extracted relational tuples. Accordingly, we can conclude that our proposed discourse-aware TS approach succeeds in transforming sentences that present a complex linguistic structure into a sequence of simplified sentences that are to a large extent grammatically correct, represent atomic semantic units and preserve the meaning of the input. Moreover, the evaluation provides sufficient evidence that our framework is able to establish a semantic hierarchy between the split sentences, generating a fine-grained representation of complex assertions in the form of hierarchically ordered and semantically interconnected propositions. Finally, we demonstrate that state-of-the-art Open IE systems benefit from using our TS approach as a pre-processing step by increasing both the accuracy and coverage of the extracted relational tuples for the majority of the Open IE approaches under consideration. In addition, we outline that the semantic hierarchy of simplified sentences can be leveraged to enrich the output of existing Open IE systems with additional meta information, thus transforming the shallow semantic representation of state-of-the-art approaches into a canonical context-preserving representation of relational tuples. KW - Text Simplification KW - Syntactic Simplification KW - Open Information Extraction KW - Semantic Representation KW - Complex Sentences Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-10540 ER - TY - THES A1 - Mandarawi, Waseem T1 - Multi-objective Network Virtualization and its Applicability to Industrial Networks N2 - Network virtualization provides high flexibility for deploying communication services in dense and heterogeneous environments. Two main approaches (dimensions) that are usually combined exist: Network Function Virtualization (NFV) technologies for functionality virtualization and Virtual Network Embedding (VNE) algorithms for resource virtualization. These approaches can be applied to different network levels, such as factory and enterprise levels of industrial networks. Several objectives and constraints, that might be conflicting, shall be considered when network virtualization is applied, mainly in complex topologies. This thesis proposes a network virtualization model that considers both virtualization dimensions, two network levels, and different objectives and constraints. The network levels considered are two primary levels in industrial networks. However, this consideration does not restrict the model to a particular environment or certain levels. The considered objectivities/constraints are topology, reliability, security, performance, and resource usage. Based on this model, we first build an overall combined solution for autonomic and composite virtual networking. This solution considers both virtualization dimensions, two network levels, and target objectives. Furthermore, this solution combines three novel virtualization sub-approaches that consider performance, reliability, and performance. However, the sub-approaches apply to different combinations of levels and dimensions, and the reliability approach additionally considers the resource usage objective. After presenting all solutions, we map them to the defined model. Regarding applicability to industrial networks, the combined approach is applied to an enterprise-level Industrial Internet of Things (IIoT) use case inspired by the smart factory concept in Industry 4.0. However, the sub-approaches are applied to more specific use cases. The performance and reliability solutions are integrated with relevant components of the Time Sensitive Networks (TSN) standard as a modern technology for industrial networks. The goal is to enrich the reliability and performance capabilities of TSN with the flexibility of network virtualization. In the combined approach, we compose and embed an environment-aware Extended Virtual Network (EVN) that represents the physical devices, virtual application functions, and required Service Function Chains (SFCs). We use the graph transformation method to transform abstract application requirements (represented by an Application Request (AR)) into an EVN. Both EVN composition and embedding methods consider the Substrate Network (SN) topology and different security, reliability, performance, and resource usage policies. These policies are applied with a certain priority and depend on the properties of communicating entities such as location and type. The EVN is embedded using property-based node mapping, reliability-aware branching, and a greedy chain embedding heuristic. The chain embedding heuristic is evaluated using a random topology that represents the use case. The performance sub-approach is NFV-based and is applied to a specific use case with Time-critical Traffic (TCT) flows. We develop and evaluate a complete framework for virtualizing Time-aware Shaper (TAS) using high-performance NFV. The reliability sub-approach is VNE-based and is applied to a specific factory level use case. We develop minimal and maximal branching heuristics based on a reliability-aware k-shortest path algorithm and compare them using a typical factory topology. We then integrate these algorithms with a Frame Replication and Elimination for Reliability (FRER) simulator to realize reliability policies by the autonomic and efficient configuration of a supporting technology. The security sub-approaches are related to both virtualization dimensions and are applied to generic enterprise-level use cases. However, the applicability of the security aspect to industrial networks is only shown in the combined (EVN) approach and its use case. We research the autonomic security management in Network Function Virtualization Infrastructure (NFVI) with the main goal of early reaction to threats through SFC reconfiguration through Virtual Network Function (VNF) live migration. This goal is approached by supporting the security measurements with a decision making architecture that considers, on the one hand, the threats and events in the environment and, on the other hand, the Service Level Agreement (SLA) between the NFVI provider and user. For this purpose, we classify the VNF-specific attacks and define possible early detectable behavior patterns. Finally, we develop a security-aware VNE heuristic that considers the security requirements of the Virtual Network (VN) and the security capabilities of the SN. This approach is modified in the combined approach to consider deploying virtualized security VNFs. KW - Network Virtualization KW - Industrial Networks KW - Virtual Network Embedding KW - Network Function Virtualization KW - Time Sensitive Networking Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-10606 ER - TY - THES A1 - Alyousef, Ammar T1 - E-Mobility Management: Towards a Grid-friendly Smart Charging Solution N2 - Replacing fossil-fueled vehicles with Electric Vehicles (EVs) poses new challenges for power distribution networks. Specifically speaking, the electrification of the mobility sector relies on the ability to process and analyze information on when, where, for how long, or how fast charging processes will take place. Nevertheless, such kind of information is typically difficult to acquire or insufficiently predictable due to the dynamic nature of the system. Also, the increasing adoption rate of the renewable energy sources, specifically the domestic Photovoltaic (PV) systems, and the potentially associated grid defection scenarios will significantly impact the cost and efforts required to operate the grid in terms of power quality and demand-supply aspects. However, such emerging requirements have arguably not been taken into account when the distribution grid was built originally. Besides, expanding the distribution and transmission capacity is a very costly and lengthy process. Therefore, any proposed solution should be cost-effective as well as environment-, grid- and user-friendly. To this end, the advancements in Information and Communications Technology (ICT) are increasingly adopted and applied. This thesis addresses the rapidly growing EV sector and deals with the problems to overcome potential power quality degradation caused by the challenges mentioned above. Since time switch and radio ripple control as existing solutions in Germany are costly and neither very effective nor scalable as it requires hardware retrofitting of existing public Charging Stations (CSs), the primary focus of this work is the development of an appropriate, standards-based, scalable, and smart charging solution of EVs. Such a solution can, in turn, boost the usage of renewable energy by ensuring that the existing grid infrastructure can operate within its permissible limits while maintaining acceptable levels of power quality. This work introduces a new definition of the concept, “grid-friendly EV charging”, where the power demand of a CS is adjusted depending on the real-time status of a power grid. In this regard, the conflicting concerns of stakeholders in an EV ecosystem are considered. For example, a Distribution System Operator (DSO) does not want to reveal a lot of technical details about the power grid or its status. Similarly, a Charging Service Provider (CSP) wants to keep its clients happy without sharing the details of its business model with others, namely, DSOs. For that sake, a distributed smart charging architecture is proposed in this thesis. It is event-driven and responds in nearly real-time to unforeseen and critical grid situations such as high/low voltage, congestion, phase unbalance, and harmonics. In that regard, the publish/subscribe messaging pattern, used as a part of the architecture, enables an efficient and well-performing communication scheme among the different components. Moreover, an indication mechanism about the different issues in a power grid is developed; it adopts the traffic light model. It works as a black box to separate smart controllers for each CS and configured only by the CSP. Smart chargers enable a smooth adjustment of the charging power to avoid drastic changes in the grid state. To that end, two types of intelligent controllers are developed and tested. While the first controller is inspired by the fuzzy logic, the second one is inspired by the slow-start mechanism used in TCP to control congestion in computer networks. A simulative approach is applied to evaluate the solution, thereby, a topology of a real low voltage grid with realistic load and generation profiles is used. Furthermore, a set of metrics is defined regarding the main concerns of stakeholders: voltage, overloading, fairness, the satisfaction of EV users and grid operator, as well as the grid-friendly behavior of a CS/ EV user. The evaluation shows that the solution is able to guarantee a safe operation of the grid. The proposed system can ensure a grid-friendly charging by sacrificing of a small portion of user satisfaction, that sacrifice of a user is awarded via a points-based reward system. Last but not least, the proposed distributed controllers are compared to two other controllers: (1) a decentralized controller based only on sensing the local voltage and (2) a very strict centralized controller focusing on grid-friendliness. The latter ensures proportional fairness among users regarding the objective function of the optimization problem solved in each simulation step. The distributed controllers are superior to the decentralized controller in terms of grid friendly and fairness and converge in general to the centralized one. KW - E-Mobility KW - Smart Charging KW - Grid-Friendliness KW - Elektromobilität KW - Lademanagement KW - Netzstabilität Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-9302 ER - TY - THES A1 - Niedermeier, Florian T1 - Power-Adaptive Computing in Future Energy Networks N2 - The current electricity grid is undergoing major changes. There is increasing pressure to move away from power generation from fossil fuels, both due to ecological concerns and fear of dependencies on scarce natural resources. Increasing the share of decentralized generation from renewable sources is a widely accepted way to a more sustainable power infrastructure. However, this comes at the price of new challenges: generation from solar or wind power is not controllable and only forecastable with limited accuracy. To compensate for the increasing volatility in power generation, exerting control on the demand side is a promising approach. By providing flexibility on demand side, imbalances between power generation and demand may be mitigated. This work is concerned with developing methods to provide grid support on demand side while limiting the associated costs. This is done in four major steps: first, the target power curve to follow is derived taking both goals of a grid authority and costs of the respective load into account. In the following, the special case of data centers as an instance of significant loads inside a power grid are focused on more closely. Data center services are adapted in a way such as to achieve the previously derived power curve. By means of hardware power demand models, the required adaptation of hardware utilization can be derived. The possibilities of adapting software services are investigated for the special use case of live video encoding. A method to minimize quality of experience loss while reducing power demand is presented. Finally, the possibility of applying probabilistic model checking to a continuous demand-response scenario is demonstrated. KW - Power-adaptive software KW - Energy systems KW - Energieversorgung KW - Software Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-9993 ER - TY - THES A1 - Ansah, Frimpong T1 - Performance and optimization technologies for software defined industrial networks N2 - The concept of programmable networks is radically changing the way communication infrastructures are designed, integrated, and operated. Currently, the topic is spearheaded by concepts such as software-defined networking, forwarding and control element separation, and network function virtualization. Notably, software-defined networking has attracted significant attention in telecommunication and data centers and thus already in some production-grade networks. Despite the prevalence of software-defined networking in these domains, industrial networks are yet to see its benefits to encourage adoption. However, the misconceptions around the concept itself, the role of virtualization, and algorithms pose a significant obstacle. Furthermore, the desire to accommodate new services in the automation industry results in a pattern of constantly increasing complexity of industrial networks, which is compounded by the requirement to provide stringent deterministic service guarantees considering characteristically different applications and thus posing a significant challenge for management, configuration, and maintenance as existing solutions are architecturally inflexible. Therefore, the first contribution of this thesis addresses the misconceptions around software-defined networking by providing a comparative analysis of programmable network concepts, detailing where software-defined networks compare with other concepts and how its principles can be leveraged to evolve industrial networks. Armed with the fundamental principles of programmable networks, the second contribution identifies virtualization technologies and proposes novel algorithms to provide varied quality of service guarantees on converged time-sensitive Ethernet networks using software-defined networking concepts. Finally, a performance analysis of a software-defined hybrid deployment solution for control and management of time-sensitive Ethernet networks that integrates proposed novel algorithms is presented as an industrial use-case that enables industrial operators to harness the full potential of time-sensitive networks. KW - Performance KW - Software Defined Industrial Networks KW - Virtual Network Embedding KW - Schedulability Analysis KW - Worst-case Delay Analysis KW - Software Defined Industrial Networks KW - Deterministic Petri-net and Queuing networks KW - Virtual Network Embedding and Worst-case Delay Analysis KW - Schedulability Analysis KW - Performance Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-9002 PB - Universität Passau CY - Passau ER - TY - THES A1 - Lang, Thomas T1 - AI-Supported Interactive Segmentation of 3D Volumes N2 - The segmentation of volumetric datasets, i.e., the partitioning of the data into disjoint sub-volumes with the goal to extract information about these regions,is a difficult problem and has been discussed in medical imaging for decades. Due to the ever-increasing imaging capabilities, in particular in X-ray computed tomography (CT) or magnetic resonance imaging, segmentation in industrial applications also gains interest. Especially in industrial applications the generated datasets increase in size. Hence, most applications apply well-known techniques in a 2+1-dimensional manner,i.e., they apply image segmentation procedures on each slice separately and track the progress along the axis of the volume in which the slices are stacked on. This discards the information on preceding or subsequent slices, which is often assumed to be nearly identical. However, in the industrial context this might prove wrong since industrial parts might change their appearance significantly over the course of even a few slices. Moreover, artifacts can further distort the content of the slices. Therefore, three-dimensional processing of voxel volumes has to be preferred, which induces constraints upon the segmentation procedures. For example, they must not consider global information as it is usually not feasible in big scans to compute them efficiently. Yet another frequent problem is that applications focus on individual parts only and algorithms are tailored to that case. Most prominent medical segmentation procedures do so by applying methods to specifically find the liver and only the liver of a patient, for example. The implication is that the same method then cannot be applied to find other parts of the scan and such methods have to be designed individually for any object to be segmented. Flexible segmentation methods are needed too specifically when partitioning unique scans. We define a unique scan to be a voxel dataset for which no comparable volume exists. Classical examples include the use case of cultural heritage where not only the objects themselves are unique but also scan parameters are optimized to obtain the best image quality possible for that specific scan. This thesis aims at introducing novel methods for voxelwise classifications based on local geometric features. The latter are computed from local environments around each voxel and extract information in similar ways as humans do, namely by observing their similarity to geometric or textural primitives. These features serve as the foundation to learning the proposed voxelwise classifiers and to discriminate between segmented and unsegmented voxels. On the one hand, they perform fully automated clustering of volumes for which a representative random sample is extracted first. On the other hand, a set of segmenting classifiers can be trained from few seed voxels, i.e., volume elements for which a domain expert marked if they belong to the components that shall be segmented. The interactive selection offers the advantage that no completely labeled voxel volumes are necessary and hence that unique scans of objects can be segmented for which no comparable scans exist. Overall, it will be shown that all proposed segmentation methods are effectively of linear runtime with respect to the number of voxels in the volume. Thus, voxel volumes without size restrictions can be segmented in an efficient linear pass through the volume. Finally, the segmentation performance is evaluated on selected datasets which shows that the introduced methods can achieve good results on scans from a broad variety of domains for both small and big voxel volumes. N2 - Die Segmentierung von Volumendaten, also die Partitionierung der Daten in disjunkte Teilvolumen zur weiteren Informationsextraktion, ist ein Problem, welches in der medizinischen Bildverarbeitung seit Jahrzehnten behandelt wird. Bedingt durch die sich ständig verbessernden Bilderfassungsmethoden, speziell im Bereich der Röntgen-Computertomographie (CT) oder der Magnetresonanztomographie, gewinnt die Segmentierung von industriellen Volumendaten auch an Wichtigkeit. Insbesondere im industriellen Kontext steigt die Größe der zu segmentierenden Daten jedoch rasant an, so dass sich die meisten Segmentierungsapplikationen auf den 2+1-dimensionalen Fall beschränken, also Bilder verarbeiten und die Ergebnisse über mehrere Bilder hinweg verfolgen. Jedoch werden somit beispielsweise geometrische Informationen über benachbarte Schichten ignoriert. Diese können sich aber gerade im industriellen Bereich signifikant ändern. Aus diesem Grund ist hier die dreidimensionale Bildverarbeitung vorzuziehen. Dadurch ergeben sich neue Einschränkungen, beispielsweise können keine globalen Informationen zur Segmentierung herangezogen werden, da diese typischerweise nicht effizient berechenbar sind. Ferner fokussieren sich dreidimensionale Methoden aus medizinischen Bereichen zumeist auf bestimmte Bestandteile der Daten, wie einzelne Organe. Dies schränkt die Generalität dieser Methoden signifikant ein und somit sind separate Verfahren für jedes zu segmentierende Objekt notwendig. Flexible Methoden sind darüber hinaus bei Anwendung auf einzigartige Scans erforderlich. Ein einzigartiger Scan ist ein Voxelvolumen, für welches kein vergleichbares Datum existiert. Klassische Beispiele sind Kulturgutdigitalisate, da dort nicht nur die Objekte einzigartig sind, sondern auch die Aufnahmeparameter spezifisch für diesen einen Scan optimiert wurden. Die vorliegende Dissertation führt neuartige Methoden zur voxelweisen dreidimensionalen Segmentierung von Volumendaten auf Basis lokaler geometrischer Informationen ein. Die Bewertung dieser Informationen imitiert die menschliche Objektwahrnehmung, indem lokale Regionen mit geometrischen oder strukturellen Primitiven verglichen werden. Mit Hilfe dieser Bewertungen werden voxelweise anzuwendende Klassifikatoren trainiert, welche zwischen erwünschten und unerwünschten Voxeln unterscheiden sollen. Ein Teil dieser Klassifikatoren führt eine vollautomatische Clustering-Analyse durch, nachdem eine repräsentative und zufällig ausgewählte Teilmenge fester Größe an Voxeln selektiert wurde. Die verbliebenen Segmentierungsalgorithmen erhalten Trainingsdaten in Form von Seed-Voxeln, also wenige Volumenelemente, die von einem Domänenexperten markiert wurden. Diese interaktive Herangehensweise ermöglicht das Einbringen von Expertenwissen ohne die Notwendigkeit vollständig annotierter Trainingsvolumen, wodurch auch einzigartige Scans segmentiert werden können. Für alle Verfahren wird dargelegt, dass die eingeführten Algorithmen von asymptotisch linearer Laufzeit in der Anzahl der Voxel im Volumen sind. Somit können Voxeldaten ohne Größenbeschränkungen in einem effizienten linearen Durchgang verarbeitet werden. Abschließend wird die Performanz der vorgestellten Verfahren auf ausgewählten Daten evaluiert und aufgezeigt, dass mit denselben wenigen Verfahren gute Ergebnisse auf vielen unterschiedlichen Domänen und gleichfalls auf kleinen und großen Volumen erzielt werden können. KW - Segmentation KW - Computed Tomography KW - Artificial Intelligence KW - Active Learning KW - Interactive KW - Machine learning KW - Image processing Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-9221 ER - TY - THES A1 - Salehi Rizi, Fatemeh T1 - Graph Representation Learning for Social Networks N2 - Online social networks provide a rich source of information about millions of users worldwide. However, due to sparsity and complex structure, analyzing these networks is quite challenging and expensive. Recently, graph embedding emerged to map networked data into low-dimensional representations, i.e. vector embeddings. These representations are fed into off-the-shelf machine learning algorithms to simplify and speed up graph analytic tasks. Given the immense importance of social network analysis, in this thesis, we aim to study graph embedding for social networks in three directions. Firstly, we focus on social networks at microscopic level to primarily encode the structural characteristic of users' personal networks so-called ego networks. These representations are utilized in evaluation tasks whose performance depends on relational information from direct neighbors. For example, social circle prediction and event attendance inference both need structural information from neighbors in social networks. Secondly, we explore assessing the content of vector embeddings in terms of topological properties. This could be explained via two proposed approaches: 1) a learning to rank algorithm in which the model weights reveal the importance of properties at subgraph level (ego networks), 2) a regression model for direct approximation of network statistical properties at vertex level. Thirdly, we propose extensions of graph embedding to capture sign or additional content of social networks. Users in social media often express their feelings and attitudes towards others which forms sentiment links besides social links. We design a joint objective function whose terms capture semantics of both social and sentiment links simultaneously. We also propose a multi-task learning framework for networks with attributes and labels by stacking autoencoders. The weights of the learning tasks are automatically assigned via an adaptive loss weighting layer. Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-9211 ER - TY - JOUR A1 - Basmadjian, Robert T1 - Flexibility-Based Energy and Demand Management in Data Centers BT - a Case Study for Cloud Computing JF - Energies N2 - The power demand (kW) and energy consumption (kWh) of data centers were augmenteddrastically due to the increased communication and computation needs of IT services. Leveragingdemand and energy management within data centers is a necessity. Thanks to the automated ICTinfrastructure empowered by the IoT technology, such types of management are becoming more feasiblethan ever. In this paper, we look at management from two different perspectives: (1) minimization of theoverall energy consumption and (2) reduction of peak power demand during demand-response periods.Both perspectives have a positive impact on total cost of ownership for data centers. We exhaustivelyreviewed the potential mechanisms in data centers that provided flexibilities together with flexiblecontracts such as green service level and supply-demand agreements. We extended state-of-the-artby introducing the methodological building blocks and foundations of management systems for theabove mentioned two perspectives. We validated our results by conducting experiments on a lab-gradescale cloud computing data center at the premises of HPE in Milano. The obtained results support thetheoretical model, by highlighting the excellent potential of flexible service level agreements in Green IT:33% of overall energy savings and 50% of power demand reduction during demand-response periods inthe case of data center federation. Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-9251 VL - 2019 IS - 12 SP - 1 EP - 22 PB - MDPI CY - Basel ER - TY - THES A1 - Schmid, Matthias T1 - Towards Storing 3D Model Graphs in Relational Databases N2 - The increasing relevance of massive graph data reinforces the need for adequate graph data management. While several graph database engines have been developed, the storage of graph data in a relational database management system, and therefore the seamless integration into existing information systems remains an open challenge. Motivated by the use case to integrate Building Information Modeling (BIM) data into the MonArch system, we propose a solution that transforms the BIM data into a property graph and stores this graph in the database system. We present a novel approach to efficiently store property graph data in a relational database management system using JSON functionality and redundant storage of edges in adjacency lists and show how to import huge data sets into this schema. Applying this approach, we import data sets of up to nearly 1 TB of disk space within the relational database, while only having 96 GB of main memory available. We also present a new approach of how to retrieve data from this database schema, translating queries written in the popular property graph query language Cypher into SQL. Hence, we provide an intuitive way to write semantically complex queries. We also demonstrate the efficiency of our approach using the standardized Linked Data Benchmark Council – Social Network Benchmark (LDBC - SNB) framework. Our approach increases the throughput for this benchmark by up to 85 times, compared to existing approaches for RDBMS. In addition, we propose a new method to transform BIM data into the property graph model and how to apply the aforementioned property graph storage to this data. We can import IFC models of up to 300 MB within five minutes. We show the suitability of our approach using our own use case specific benchmark, which we integrated into the previously mentioned Social Network Benchmark. For our interactive use case-specific queries, we achieve response times faster than 5 ms in 99% of all executions. Finally, we present how the aforementioned approach to store BIM data in a relational database management system is integrated into the existing MonArch system by splitting the different functionalities of our approach into a microservice architecture. N2 - Die steigende Relevanz von riesigen Graphdatenmengen verstärkt die Notwendigkeit von adäquatem Graphdaten Management. Während bereits mehrere Graphdatenbanken entwickelt wurden, bleibt die Speicherung von Graphdaten in relationalen Datenbanken und die damit verbundene nahtlose Integration in bereits existierende Informationssysteme eine ungelöste Herausforderung. Motiviert durch unseren eigenen Anwendungsfall Building Information Modeling (BIM)Daten in das MonArch Informationssystem zu integrieren, schlagen wir einen Ansatz vor, BIM Daten in eine Property Graph Form umzuwandeln und diesen in der Datenbank zu speichern. Um dies zu erreichen, stellen wir einen neuartigen Ansatz vor, um Property Graphen in einem relationalen Datenbanksystem zu speichern, indem wir Funktionalitäten wie JSON und die redundante Speicherung von Kanten in Adjazenzlisten kombinieren und zeigen wie große Mengen dieser Daten in das Schema importiert werden können. Durch die Anwendung unseres Ansatzes können wir Datensätze von bis zu 1 TB in das Datenbanksystem importieren, während wir nur 96 GB Hauptspeicher zur Verfügung haben. Wir stellen außerdem einen neuen Ansatz vor, um Daten aus dem zuvor genannten Schema abzufragen, indem wir die beliebte Graphanfragesprache Cypher in die Sprache SQL übersetzen. Dadurch erreichen wir eine intuitive Art semantisch komplexe Anfragen zu schreiben. Zusätzlich zeigen wir die Effizienz unseres Ansatzes, indem wir das standardisierte Evaluationsframework Social Network Benchmark des Linked Data Benchmar Council (LDBC – SNB) verwenden. Unser Ansatz erhöht den Durchsatz dieses Benchmarks, im Vergleich zu existierenden Ansätzen für relationale Datenbanksysteme, auf einen bis zu 85-fachen Durchsatz. Zusätzlich schlagen wir eine neue Methode vor, um BIM Daten in das Property Graph Modell zu übertragen und wie das zuvor vorgestellte Speichermodel verwendet werden kann, um diese Daten zu speichern. Damit können wir IFC Modelle mit bis zu 300 MB in unter 5 Minuten in unser System importieren. Schließlich zeigen wir die Eignung unseres Ansatzes, indem wir einen eigenen Benchmark spezifisch für unseren Anwendungsfall verwenden, welchen wir in den zuvor erwähnte Social Network Benchmark integriert haben. Für unsere anwendungsfallspezifischen Anfragen erreichen wir Antwortzeiten von unter 5 ms in 99% der Ausführungen. KW - Graph-based database models KW - Relational database model KW - Property graph KW - Industry Foundation Classes KW - IFC Store Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-10353 ER - TY - THES A1 - Bermeitinger, Bernhard T1 - Investigating a Second-Order Optimization Strategy for Neural Networks N2 - In summary, this cumulative dissertation investigates the application of the conjugate gradient method CG for the optimization of artificial neural networks (NNs) and compares this method with common first-order optimization methods, especially the stochastic gradient descent (SGD). The presented research results show that CG can effectively optimize both small and very large networks. However, the default machine precision of 32 bits can lead to problems. The best results are only achieved in 64-bits computations. The research also emphasizes the importance of the initialization of the NNs’ trainable parameters and shows that an initialization using singular value decomposition (SVD) leads to drastically lower error values. Surprisingly, shallow but wide NNs, both in Transformer and CNN architectures, often perform better than their deeper counterparts. Overall, the research results recommend a re-evaluation of the previous preference for extremely deep NNs and emphasize the potential of CG as an optimization method. N2 - Zusammenfassend untersucht die vorliegende kumulative Dissertation die Anwendung des konjugierten Gradienten (CG) zur Optimierung künstlicher neuronaler Netzwerke (NNs) und vergleicht diese Methode mit verbreiteten Optimierungsverfahren erster Ordnung, insbesondere dem Stochastischem Gradientenabstieg (SGD). Die in den Arbeiten präsentierten Forschungsergebnisse zeigen, dass CG in der Lage ist, sowohl kleinere als auch sehr große Netzwerke effektiv zu optimieren. Allerdings kann die Maschinen- genauigkeit bei 32-Bit-Berechnungen zu Problemen führen, beste Ergebnisse werden erst in 64-Bit-Fließkommazahlen erreicht. Die Forschung betont auch die Bedeutung der Initialisierung der NN-Parameter und zeigt, dass eine Initialisierung mittels Singulärwertzerlegung zu deutlich geringeren Fehlerwerten führt. Überraschenderweise erzielen flachere NNs bessere Ergebnisse als tiefe NNs mit einer vergleichbaren Anzahl an trainierbaren Parametern, unabhängig vom jeweiligen NN, das die künstlichen Daten erzeugt. Es zeigt sich auch, dass flache, breite NNs, sowohl in Transformer-, als auch in CNN-Architekturen oft besser abschneiden als ihre tieferen Gegenstücke. Insgesamt empfehlen die Forschungsergebnisse eine Neubewertung der bisherigen Präferenz für extrem tiefe NNs und betonen das Potential von CG als Optimierungsmethode. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-14087 ER - TY - JOUR A1 - Frank, Florian A1 - Böttger, Simon A1 - Mexis, Nico A1 - Anagnostopoulos, Nikolaos Athanasios A1 - Mohamed, Ali A1 - Hartmann, Martin A1 - Kuhn, Harald A1 - Helke, Christian A1 - Arul, Tolga A1 - Katzenbeisser, Stefan A1 - Hermann, Sascha T1 - CNT-PUFs: highly robust and heat-tolerant carbon-nanotube-based physical unclonable functions N2 - In this work, we explored a highly robust and unique Physical Unclonable Function (PUF) based on the stochastic assembly of single-walled Carbon NanoTubes (CNTs) integrated within a wafer-level technology. Our work demonstrated that the proposed CNT-based PUFs are exceptionally robust with an average fractional intra-device Hamming distance well below 0.01 both at room temperature and under varying temperatures in the range from 23 °C to 120 °C. We attributed the excellent heat tolerance to comparatively low activation energies of less than 40 meV extracted from an Arrhenius plot. As the number of unstable bits in the examined implementation is extremely low, our devices allow for a lightweight and simple error correction, just by selecting stable cells, thereby diminishing the need for complex error correction. Through a significant number of tests, we demonstrated the capability of novel nanomaterial devices to serve as highly efficient hardware security primitives. KW - Carbon NanoTube (CNT) KW - Physical Unclonable Function (PUF) KW - Nanomaterials (NMs) KW - hardware security KW - security KW - privacy KW - Internet of Things (IoT) Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-14011 VL - 2023 IS - 13(22) PB - MDPI CY - Basel ER - TY - THES A1 - Fink, Simon Dominik T1 - Constrained Planarity Algorithms in Theory and Practice N2 - In the constrained planarity setting, we ask whether a graph admits a crossing-free drawing that additionally satisfies a given set of constraints. These constraints are often derived from very natural problems; prominent examples are Level Planarity, where vertices have to lie on given horizontal lines indicating a hierarchy, Partially Embedded Planarity, where we extend a given drawing without modifying already-drawn parts, and Clustered Planarity, where we additionally draw the boundaries of clusters which recursively group the vertices in a crossing-free manner. In the last years, the family of constrained planarity problems received a lot of attention in the field of graph drawing. Efficient algorithms were discovered for many of them, while a few others turned out to be NP-complete. In contrast to the extensive theoretical considerations and the direct motivation by applications, only very few of the found algorithms have been implemented and evaluated in practice. The goal of this thesis is to advance the research on both theoretical as well as practical aspects of constrained planarity. On the theoretical side, we consider two types of constrained planarity problems. The first type are problems that individually constrain the rotations of vertices, that is they restrict the counter-clockwise cyclic orders of the edges incident to vertices. We give a simple linear-time algorithm for the problem Partially Embedded Planarity, which also generalizes to further constrained planarity variants of this type. The second type of constrained planarity problem concerns more involved planarity variants that come down to the question whether there are embeddings of one or multiple graphs such that the rotations of certain vertices are in sync in a certain way. Clustered Planarity and a variant of the Simultaneous Embedding with Fixed Edges Problem (Connected SEFE-2) are well-known problems of this type. Both are generalized by our Synchronized Planarity problem, for which we give a quadratic algorithm. Through reductions from various other problems, we provide a unified modelling framework for almost all known efficiently solvable constrained planarity variants that also directly provides a quadratic-time solution to all of them. For both our algorithms, a key ingredient for reaching an efficient solution is the usage of the right data structure for the problem at hand. In this case, these data structures are the SPQR-tree and the PC-tree, which describe planar embedding possibilities from a global and a local perspective, respectively. More specifically, PC-trees can be used to locally describe the possible cyclic orders of edges around vertices in all planar embeddings of a graph. This makes it a key component for our algorithms, as it allows us to test planarity while also respecting further constraints, and to communicate constraints arising from the surrounding graph structure between vertices with synchronized rotation. Bridging over to the practical side, we present the first correct implementation of PC-trees. We also describe further improvements, which allow us to outperform all implementations of alternative data structures (out of which we only found very few to be fully correct) by at least a factor of 4. We show that this yields a simple and competitive planarity test that can also yield an embedding to certify planarity. We also use our PC-tree implementation to implement our quadratic algorithm for solving Synchronized Planarity. Here, we show that our algorithm greatly outperforms previous attempts at solving related problems like Clustered Planarity in practice. We also engineer its running time and show how degrees of freedom in the theoretical algorithm can be leveraged to yield an up to tenfold speed-up in practice. KW - Constrained Planarity KW - Clustered Planarity KW - Synchronized Planarity KW - Algorithm Engineering Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-13817 ER -