@phdthesis{Woelfl2018, author = {W{\"o}lfl, Andreas}, title = {Data Management in Certified Avionics Systems}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-5758}, school = {Universit{\"a}t Passau}, pages = {xi, 177 Seiten}, year = {2018}, abstract = {Data management is a cornerstone for any kind of information system - including the aerospace and aviation sector. In contrast to conventional domains, software development in the avionics domain must adhere to a legally binding certification process, called qualification. The success of the process depends on compliance with international standards, such as DO-178: Software Considerations in Airborne Systems and Equipment Certification. From a software developer's perspective, challenges arise in terms of methods and tools. Techniques that have a potential impact on the deterministic and predictable execution of avionics software are prohibited. The objective of this thesis' research is to develop a scalable method to realize data-management for multi-variant avionics software under the restrictions and constraints of the domain. Since avionics software faces very long-term life-cycles (up to 75 years), a particular focus is being placed on maintenance and evolution. Based on the insights gained in a semi-structured interview at Airbus Helicopters, industrial established approaches to implement qualified avionics software are assessed at first and compared with respect to strengths and weaknesses for data-management afterwards. As a result, a novel development approach is proposed, combining model-based techniques and product-line technology to derive the source code of highly specific data-management variants, as well as the majority of assets required for the qualification process, from a declarative system specification. In order to demonstrate the practicability of the approach in industry, a framework is presented that is deployed and applied at Airbus Helicopters to generate qualifiable data-management components for the variants of the NH90 helicopter. The maintainability is shown by means of a domain-specific optimization, in which the model-based and generative approach is used to establish safe memory overlays at compile-time. Key findings reveal a substantially reduced memory footprint (29,1\% in case of a real-world scenario), as well as an significantly facilitated implementation process, which would not be accomplishable using conventional methods for software development in the avionics domain.}, subject = {Avionik}, language = {en} } @phdthesis{Reislhuber2017, author = {Reislhuber, Josef}, title = {Optical Graph Recognition}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-5159}, school = {Universit{\"a}t Passau}, pages = {270 Seiten}, year = {2017}, abstract = {Graphs are an important model for the representation of structural information between objects. One identifies objects and nodes as well as a binary relation between objects and edges. Graphs have many uses, e. g., in social sciences, life sciences and engineering. There are two primary representations: abstract and visual. The abstract representation is well suited for processing graphs by computers and is given by an adjacency list, an adjacency matrix or any abstract data structure. A visual representation is used by human users who prefer a picture. Common terms are diagram, scheme, plan, or network. The objective of Graph Drawing is to transform a graph into a visual representation called the drawing of a graph. The goal is a "nice" drawing. In this thesis we introduce Optical Graph Recognition. Optical Graph Recognition (OGR) reverses Graph Drawing and transforms a digital image of a graph into an abstract representation. Our approach consists of four phases: Preprocessing where we determine which pixels of an image are part of the graph, Segmentation where we recognize the nodes, Topology Recognition where we detect the edges and Postprocessing where we enrich the recognized graph with additional information. We apply established digital image processing methods and make use of the special property that the image contains nodes that are connected by edges. We have focused on developing algorithms that need as little parameters as possible or to automatically calibrate the parameters. Most false recognition results are caused by crossing edges as this makes tracing the edges difficult and can lead to other recognition errors. We have evaluated hand-drawn and computer-drawn graphs. Our algorithms have a very high recognition rate for computer-drawn graphs, e. g., from a set of 100000 computer-drawn graphs over 90\% were correctly recognized. Most false recognition results where observed for hand-drawn graphs as they can include drawing errors and inaccuracies. For universal usability we have implemented a prototype called OGRup for mobile devices like smartphones or tablet computers. With our software it is possible to directly take a picture of a graph via a built in camera, recognize the graph, and then use the result for further processing. Furthermore, in order to gain more insight into the way a person draws a graph by hand, we have conducted a field study.}, subject = {Bildverarbeitung}, language = {en} } @phdthesis{Petit2017, author = {Petit, Albin}, title = {Introducing Privacy in Current Web Search Engines}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-4652}, school = {Universit{\"a}t Passau}, pages = {XVI, 153 S.}, year = {2017}, abstract = {During the last few years, the technological progress in collecting, storing and processing a large quantity of data for a reasonable cost has raised serious privacy issues. Privacy concerns many areas, but is especially important in frequently used services like search engines (e.g., Google, Bing, Yahoo!). These services allow users to retrieve relevant content on the Internet by exploiting their personal data. In this context, developing solutions to enable users to use these services in a privacy-preserving way is becoming increasingly important. In this thesis, we introduce SimAttack an attack against existing protection mechanism to query search engines in a privacy-preserving way. This attack aims at retrieving the original user query. We show with this attack that three representative state-of-the-art solutions do not protect the user privacy in a satisfactory manner. We therefore develop PEAS a new protection mechanism that better protects the user privacy. This solution leverages two types of protection: hiding the user identity (with a succession of two nodes) and masking users' queries (by combining them with several fake queries). To generate realistic fake queries, PEAS exploits previous queries sent by the users in the system. Finally, we present mechanisms to identify sensitive queries. Our goal is to adapt existing protection mechanisms to protect sensitive queries only, and thus save user resources (e.g., CPU, RAM). We design two modules to identify sensitive queries. By deploying these modules on real protection mechanisms, we establish empirically that they dramatically improve the performance of the protection mechanisms.}, subject = {Suchmaschine}, language = {en} } @phdthesis{Loewe2017, author = {L{\"o}we, Stefan}, title = {Effective Approaches to Abstraction Refinement for Automatic Software Verification}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-4815}, school = {Universit{\"a}t Passau}, pages = {XXI, 155 S.}, year = {2017}, abstract = {This thesis presents various techniques that aim at enabling more effective and more efficient approaches for automatic software verification. After a brief motivation why automatic software verification is getting ever more relevant, we continue with detailing the formalism used in this thesis and on the concepts it is built on. We then describe the design and implementation of the value analysis, an analysis for automatic software verification that tracks state information concretely. From a thorough evaluation based on well over 4 000 verification tasks from the latest edition of the International Competition on Software Verification (SV-COMP), we learn that this plain value analysis leads to an efficient verification process for many verification tasks, but at the same time, fails to solve other verification tasks due to state-space explosion. From this insight we infer that some form of abstraction technique must be added to the value analysis in order to also allow the successful verification of large and complex verification tasks. As a solution, we propose to incorporate counterexample-guided abstraction refinement (CEGAR) and interpolation into the value domain. To this end, we design a novel interpolation procedure, that extracts from infeasible counterexamples interpolants for the value domain, allowing to form a precision strong enough to exclude these infeasible counterexamples, and to make progress in the CEGAR loop. We then describe several optimizations and extensions to these concepts, such that the value analysis with CEGAR becomes competitive for automatic software verification. As the next step, we combine the value analysis with CEGAR with a predicate analysis, to obtain a more precise and efficient composite analysis based on CEGAR. This composite analysis is indeed on a par with the world's leading software verification tools, as witnessed by the results of SV-COMP'13 where this approach achieved the 2 nd place in the overall ranking. After having available competitive CEGAR-based analyses for the value domain, the predicate domain, and the combination thereof, we then turn our attention to techniques that have the goal to make all these CEGAR-based approaches more successful. Our first novel idea in this regard is based on the concept of infeasible sliced prefixes, which allow the computation of different precisions from a single infeasible counterexample. This adds choice to the CEGAR loop, while without this enhancement, no choice for a specific precision, i. e., a specific refinement, is possible. In our evaluation we show, for both the value analysis and the predicate analysis, that choosing different infeasible sliced prefixes during the refinement step leads to major differences in verification effectiveness and verification efficiency. Extending on the concept of infeasible sliced prefixes, we define several heuristics in order to precisely select a single refinement from a set of possible refinements. We make this new concept, which we refer to as guided refinement selection, available to both the value and predicate analysis, and in a large-scale evaluation we try to answer the question which selection technique leads to well suited abstractions and thus, to a more effective verification process. Additionally, we present the idea of inter-analysis refinement selection, where the refinement component of a composite analysis may decide which of its component analyses is best to be refined, and in yet another evaluation we highlight the positive effects of this technique. Finally, we present the results of SV-COMP'16, where the verifier we contributed and which is based on the concepts and ideas presented in this thesis achieved the 1 st place in the category DeviceDriversLinux64.}, subject = {Programmverifikation}, language = {en} } @phdthesis{Alshawish2021, author = {Alshawish, Ali}, title = {Risk-based Security Management in Critical Infrastructure Organizations}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-10026}, school = {Universit{\"a}t Passau}, pages = {xii, 181 Seiten}, year = {2021}, abstract = {Critical infrastructure and contemporary business organizations are experiencing an ongoing paradigm shift of business towards more collaboration and agility. On the one hand, this shift seeks to enhance business efficiency, coordinate large-scale distribution operations, and manage complex supply chains. But, on the other hand, it makes traditional security practices such as firewalls and other perimeter defenses insufficient. Therefore, concerns over risks like terrorism, crime, and business revenue loss increasingly impose the need for enhancing and managing security within the boundaries of these systems so that unwanted incidents (e.g., potential intrusions) can still be detected with higher probabilities. To this end, critical infrastructure organizations step up their efforts to investigate new possibilities for actively engaging in situational awareness practices to ensure a high level of persistent monitoring as well as on-site observation. Compliance with security standards is necessary to ensure that organizations meet regulatory requirements mostly shaped by a set of best practices. Nevertheless, it does not necessarily result in a coherent security strategy that considers the different aims and practical constraints of each organization. In this regard, there is an increasingly growing demand for risk-based security management approaches that enable critical infrastructures to focus their efforts on mitigating the risks to which they are exposed. Broadly speaking, security management involves the identification, assessment, and evaluation of long-term (or overall) objectives and interests as well as the means of achieving them. Due to the critical role of such systems, their decision-makers tend to enhance the system resilience against very unpleasant outcomes and severe consequences. That is, they seek to avoid decision options associated with likely extreme risks in the first place. Practically speaking, this risk attitude can significantly influence the decision-making process in such critical organizations. Towards incorporating the aversion to extreme risks into security management decisions, this thesis investigates thoroughly the capabilities of a recently emerged theory of games with payoffs that are probability distributions. Unlike traditional optimization techniques, this theory provides an alternative decision technique that is more robust to extreme risks and uncertainty. Furthermore, this thesis proposes a new method that gives a decision maker more control over the decision-making process through defining loss regions with different importance levels according to people's risk attitudes. In this way, the static decision analysis used in the distribution-valued games is transformed into a dynamic process to adapt to different subjective risk attitudes or account for future changes in the decision caused by a learning process or other changes in the context. Throughout their different parts, this thesis shows how theoretical models, simulation, and risk assessment models can be combined into practical solutions. In this context, it deals with three facets of security management: allocating limited security resources, prioritizing security actions, and tweaking decision making. Finally, the author discusses experiences and limitations distilled from this research and from investigating the new theory of games, which can be taken into account in future approaches.}, subject = {Spieltheorie}, language = {en} } @phdthesis{Lang2021, author = {Lang, Thomas}, title = {AI-Supported Interactive Segmentation of 3D Volumes}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-9221}, school = {Universit{\"a}t Passau}, pages = {184 Seiten}, year = {2021}, abstract = {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.}, language = {en} } @phdthesis{Alhamzeh2023, author = {Alhamzeh, Alaa}, title = {Language Reasoning by means of Argument Mining and Argument Quality}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-12699}, school = {Universit{\"a}t Passau}, pages = {ix, 154 Seiten}, year = {2023}, abstract = {Understanding of financial data has always been a point of interest for market participants to make better informed decisions. Recently, different cutting edge technologies have been addressed in the Financial Technology (FinTech) domain, including numeracy understanding, opinion mining and financial ocument processing. In this thesis, we are interested in analyzing the arguments of financial experts with the goal of supporting investment decisions. Although various business studies confirm the crucial role of argumentation in financial communications, no work has addressed this problem as a computational argumentation task. In other words, the automatic analysis of arguments. In this regard, this thesis presents contributions in the three essential axes of theory, data, and evaluation to fill the gap between argument mining and financial text. First, we propose a method for determining the structure of the arguments stated by company representatives during the public announcement of their quarterly results and future estimations through earnings conference calls. The proposed scheme is derived from argumentation theory at the micro-structure level of discourse. We further conducted the corresponding annotation study and published the first financial dataset annotated with arguments: FinArg. Moreover, we investigate the question of evaluating the quality of arguments in this financial genre of text. To tackle this challenge, we suggest using two levels of quality metrics, considering both the Natural Language Processing (NLP) literature of argument quality assessment and the financial era peculiarities. Hence, we have also enriched the FinArg data with our quality dimensions to produce the FinArgQuality dataset. In terms of evaluation, we validate the principle of ensemble learning on the argument identification and argument unit classification tasks. We show that combining a traditional machine learning model along with a deep learning one, via an integration model (stacking), improves the overall performance, especially in small dataset settings. In addition, despite the fact that argument mining is mainly a domain dependent task, to this date, the number of studies that tackle the generalization of argument mining models is still relatively small. Therefore, using our stacking approach and in comparison to the transfer learning model of DistilBert, we address and analyze three real-world scenarios concerning the model robustness over completely unseen domains and unseen topics. Furthermore, with the aim of the automatic assessment of argument strength, we have investigated and compared different (refined) versions of Bert-based models that incorporate external knowledge in the decision layer. Consequently, our method outperforms the baseline model by 13 ± 2\% in terms of F1-score through integrating Bert with encoded categorical features. Beyond our theoretical and methodological proposals, our model of argument quality assessment, annotated corpora, and evaluation approaches are publicly available, and can serve as strong baselines for future work in both FinNLP and computational argumentation domains. Hence, directly exploiting this thesis, we proposed to the community, a new task/challenge related to the analysis of financial arguments: FinArg-1, within the framework of the NTCIR-17 conference. We also used our proposals to react to the Touch{\´e} challenge at the CLEF 2021 conference. Our contribution was selected among the «Best of Labs».}, language = {en} } @phdthesis{Niklaus2022, author = {Niklaus, Christina}, title = {From Complex Sentences to a Formal Semantic Representation using Syntactic Text Simplification and Open Information Extraction}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-10540}, school = {Universit{\"a}t Passau}, pages = {xxi, 301 Seiten}, year = {2022}, abstract = {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.}, language = {en} } @phdthesis{Mandarawi2022, author = {Mandarawi, Waseem}, title = {Multi-objective Network Virtualization and its Applicability to Industrial Networks}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-10606}, school = {Universit{\"a}t Passau}, pages = {xv, 156 Seiten}, year = {2022}, abstract = {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.}, language = {en} } @phdthesis{Schmid2021, author = {Schmid, Matthias}, title = {Towards Storing 3D Model Graphs in Relational Databases}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-10353}, school = {Universit{\"a}t Passau}, pages = {243 Seiten}, year = {2021}, abstract = {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.}, language = {en} }