004 Datenverarbeitung; Informatik
Refine
Year of publication
Document Type
- Doctoral Thesis (103)
- Conference Proceeding (7)
- Article (1)
- Master's Thesis (1)
- Other (1)
- Preprint (1)
Has Fulltext
- yes (114)
Keywords
- Graphenzeichnen (7)
- Computersicherheit (6)
- Software Engineering (6)
- Graph (5)
- Multimedia (5)
- Programmanalyse (5)
- Information Retrieval (4)
- Modellierung (4)
- Semantic Web (4)
- Abfrageverarbeitung (3)
Institute
- Fakultät für Informatik und Mathematik (54)
- Mitarbeiter Lehrstuhl/Einrichtung der Fakultät für Informatik und Mathematik (50)
- Sonstiger Autor der Fakultät für Informatik und Mathematik (8)
- Mitarbeiter Lehrstuhl/Einrichtung der Wirtschaftswissenschaftlichen Fakultät (2)
- Institut für IT-Sicherheit und Sicherheitsrecht (ISL) (1)
In our knowledge-driven society, the acquisition and the transfer of knowledge play a principal role. Web search engines are somehow tools for knowledge acquisition and transfer from the web to the user. The search engine results page (SERP) consists mainly of a list of links and snippets (excerpts from the results). The snippets are used to express, as efficiently as possible, the way a web page may be relevant to the query.
As an extension of the existing web, the semantic web or “web 3.0” is designed to convert the presently available web of unstructured documents into a web of data consumable by both human and machines. The resulting web of data and the current web of documents coexist and interconnect via multiple mechanisms, such as the embedded structured data, or the automatic annotation.
In this thesis, we introduce a new interactive artifact for the SERP: the “Semantic Snippet”. Semantic Snippets rely on the coexistence of the two webs to facilitate the transfer of knowledge to the user thanks to a semantic contextualization of the user’s information need. It makes apparent the relationships between the information need and the most relevant entities present in the web page.
The generation of semantic snippets is mainly based on the automatic annotation of the LOD1’s entities in web pages. The annotated entities have different level of impor- tance, usefulness and relevance. Even with state of the art solutions for the automatic annotations of LOD entities within web pages, there is still a lot of noise in the form of erroneous or off-topic annotations. Therefore, we propose a query-biased algorithm (LDRANK) for the ranking of these entities. LDRANK adopts a strategy based on the linear consensual combination of several sources of prior knowledge (any form of con- textual knowledge, like the textual descriptions for the nodes of the graph) to modify a PageRank-like algorithm.
For generating semantic snippets, we use LDRANK to find the more relevant entities in the web page. Then, we use a supervised learning algorithm to link each selected entity to excerpts from the web page that highlight the relationship between the entity and the original information need.
In order to evaluate our semantic snippets, we integrate them in ENsEN (Enhanced Search Engine), a software system that enhances the SERP with semantic snippets.
Finally, we use crowdsourcing to evaluate the usefulness and the efficiency of ENsEN.
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.
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.
In this thesis, we investigates plane drawings of undirected and directed graphs on cylinder surfaces. In the case of undirected graphs, the vertices are positioned on a line that is parallel to the cylinder’s axis and the edge curves must not intersect this line. We show that a plane drawing is possible if and only if the graph is a double-ended queue (deque) graph, i. e., the vertices of the graph can be processed according to a linear order and the edges correspond to items in the deque inserted and removed at their end vertices. A surprising consequence resulting from these observations is that the deque characterizes planar graphs with a Hamiltonian path. This result extends the known characterization of planar graphs with a Hamiltonian cycle by two stacks. By these insights, we also obtain a new characterization of queue graphs and their duals. We also consider the complexity of deciding whether a graph is a deque graph and prove that it is NP-complete. By introducing a split operation, we obtain the splittable deque and show that it characterizes planarity. For the proof, we devise an algorithm that uses the splittable deque to test whether a rotation system is planar. In the case of directed graphs, we study upward plane drawings where the edge curves follow the direction of the cylinder’s axis (standing upward planarity; SUP) or they wind around the axis (rolling upward planarity; RUP). We characterize RUP graphs by means of their duals and show that RUP and SUP swap their roles when considering a graph and its dual. There is a physical interpretation underlying this characterization: A SUP graph is to its RUP dual graph as electric current passing through a conductor to the magnetic field surrounding the conductor. Whereas testing whether a graph is RUP is NP-hard in general [Bra14], for directed graphs without sources and sink, we develop a linear-time recognition algorithm that is based on our dual graph characterization of RUP graphs.
In the last decade, crowdsourcing has proved its ability to address large scale data collection tasks, such as labeling large data sets, at a low cost and in a short time. However, the performance and behavior variability between workers as well as the variability in task designs and contents, induce an unevenness in the quality of the produced contributions and, thus, in the final output quality. In order to maintain the effectiveness of crowdsourcing, it is crucial to control the quality of the contributions. Furthermore, maintaining the efficiency of crowdsourcing requires the time and cost overhead related to the quality control to be at its lowest. While effective, current quality control techniques such as contribution aggregation, worker selection, context-specific reputation systems, and multi-step workflows, suffer from fairly high time and budget overheads and from their dependency on prior knowledge about individual workers.
In this thesis, we address this challenge by leveraging the similarity between completed and incoming tasks as well as the correlation between the worker declarative profiles and their performance in previous tasks in order to perform an efficient task-aware worker selection. To this end, we propose CAWS (Context AwareWorker Selection) method which operates in two phases; in an offline phase, completed tasks are clustered into homogeneous groups for each of which the correlation with the workers declarative profile is learned. Then, in the online phase, incoming tasks are matched to one of the existing clusters and the correspondent, previously inferred profile model is used to select the most reliable online workers for the given task. Using declarative profiles helps eliminate any probing process, which reduces the time and the budget while maintaining the crowdsourcing quality. Furthermore, the set of completed tasks, when compared to a probing task split, provides a larger corpus from which a more precise profile model can be learned. This translates to a better selection quality, especially for harder tasks.
In order to evaluate CAWS, we introduce CrowdED (Crowdsourcing Evaluation Dataset), a rich dataset to evaluate quality control methods and quality-driven task vectorization and clustering. The generation of CrowdED relies on a constrained sampling approach that allows to produce a task corpus which respects both, the budget and type constraints. Beside helping in evaluating CAWS, and through its generality and richness, CrowdED helps in plugging the benchmarking gap present in the crowdsourcing quality control community.
Using CrowdED, we evaluate the performance of CAWS in terms of the quality of the worker selection and in terms of the achieved time and budget reduction. Results shows the following: first, automatic grouping is able to achieve a learning quality similar to job-based grouping. And second, CAWS is able to outperform the state-of-the-art profile-based worker selection when it comes to quality. This is especially true when strong budget and time constraints are present on the requester side.
Finally, we complement our work by a software contribution consisting of an open source framework called CREX (CReate Enrich eXtend). CREX allows the creation, the extension and the enrichment of crowdsourcing datasets. It provides the tools to vectorize, cluster and sample a task corpus to produce constrained task sets and to automatically generate custom crowdsourcing campaign sites.
In this dissertation we generalise the notion of level planar graphs in two directions: track planarity and radial planarity. Our main results are linear time algorithms both for the planarity test and for the computation of an embedding, and thus a drawing. Our algorithms use and generalise PQ-trees, which are a data structure for efficient planarity tests.
Blockchains and distributed ledger technology (DLT) that rely on Proof-of-Work (PoW) typically show limited performance. Several recent approaches incorporate Byzantine fault-tolerant (BFT) consensus protocols in their DLT design as Byzantine consensus allows for increased performance and energy efficiency, as well as it offers proven liveness and safety properties. While there has been a broad variety of research on BFT consensus protocols over the last decades, those protocols were originally not intended to scale for a large number of nodes. Thus, the quest for scalable BFT consensus was initiated with the emerging research interest in DLT. In this paper, we first provide a broad analysis of various optimization techniques and approaches used in recent protocols to scale Byzantine consensus for large environments such as BFT blockchain infrastructures. We then present an overview of both efforts and assumptions made by existing protocols and compare their solutions.
In geo-replicated systems, the heterogeneous latencies of connections between replicas limit the system’s ability to achieve fast consensus. State machine replication (SMR) protocols can be refined for their deployment in wide-area networks by using a weighting scheme for active replication that employs additional replicas and assigns higher voting power to faster replicas. Utilizing more variability in quorum formation allows replicas to swifter proceed to subsequent protocol stages, thus decreasing consensus latency. However, if network conditions vary during the system’s lifespan or faults occur, the system needs a solution to autonomously adjust to new conditions. We incorporate the idea of self-optimization into geographically distributed, weighted replication by introducing AWARE, an automated and dynamic voting weight tuning and leader positioning scheme. AWARE measures replica-replica latencies and uses a prediction model, thriving to minimize the system’s consensus latency. In experiments using different Amazon EC2 regions, AWARE dynamically optimizes consensus latency by self-reliantly finding a fast weight configuration yielding latency gains observed by clients located across the globe.
The increasing cost of energy and the worldwide desire to reduce CO2 emissions has raised concern about the energy efficiency of information and communication technology. Whilst research has focused on data centres recently, this thesis identifies office computing environments as significant consumers of energy. Office computing environments offer great potential for energy savings: On one hand, such environments consist of a large number of hosts. On the other hand, these hosts often remain turned on 24~hours per day while being underutilised or even idle. This thesis analyzes the energy consumption within office computing environments and suggests an energy-efficient virtualized office environment. The office environment is virtualized to achieve flexible virtualized office resources that enable an energy-based resource management. This resource management stops idle services and idle hosts from consuming resources within the office and consolidates utilised office services on office hosts. This increases the utilisation of some hosts while other hosts are turned off to save energy. The suggested architecture is based on a decentralized approach that can be applied to all kinds of office computing environments, even if no centralized data centre infrastructure is available. The thesis develops the architecture of the virtualized office environment together with an energy consumption model that is able to estimate the energy consumption of hosts and network within office environments. The model enables the energy-related comparison of ordinary and virtualized office environments, considering the energy-efficient management of services. Furthermore, this thesis evaluates energy efficiency and overhead of the suggested approach. First, it theoretically proves the energy efficiency of the virtualized office environment with respect to the energy consumption model. Second, it uses Markov processes to evaluate the impact of user behaviour on the suggested architecture. Finally, the thesis develops a discrete-event simulation that enables the simulation and evaluation of office computing environments with respect to varying virtualization approaches, resource management parameters, user behaviour, and office equipment. The evaluation shows that the virtualized office environment saves more than half of the energy consumption within office computing environments, depending on user behaviour and office equipment.