05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Generic templates for monitoring agents(2018) Weise, MarcThis thesis presents an agent-centric approach for monitoring IT resources, which enables the execution of preprocessing and aggregation steps directly on the target systems in order to limit data transfers to a central server and allow a local event detection and treatment. To keep the agent behavior definition as simple as possible, an extendable template model is introduced which can be used to define Monitoring Pipelines by chaining individual processing steps. Furthermore this work demonstrates how a graphical editor can be implemented which also allows non-experts in the field of monitoring to create and modify Monitoring Templates.Item Open Access Personenbezogene Daten im Data Lake(2018) Ebinger, FelixBig-Data-Analysen bieten Wettbewerbsvorteile, ermöglichen Innovationen und können zu einer höheren Qualität von Produkten oder Serviceleistungen beitragen. Insbesondere die Analyse von Kundendaten und des Kundenverhaltens eröffnet vielfältige Möglichkeiten, um dem Kunden auf ihn zugeschnittene Angebote zu unterbreiten und um so zu höheren Umsätzen und zu einer höheren Kundenzufriedenheit beizutragen. Für die dafür benötigten Daten werden geeignete Speichersysteme benötigt. Ein solches System stellt der Data Lake dar. Neben der gut skalierenden und günstigen Speicherung von Daten ist auch die Auswertung der Daten mittels explorativer Analysen bereits im Design angelegt. Gleichzeitig steht aber auch der Schutz, genauer der fehlende Schutz der Privatsphäre, des Einzelnen bei Big Data Verarbeitungen im Mittelpunkt der öffentlichen Aufmerksamkeit und Kritik. Insbesondere wird vor dem so entstehenden „gläsernen Menschen“ und den daraus resultierenden gesellschaftlichen Folgen gewarnt. Die sich daraus ergebenden Fragen, in welchem Umfang und auf welche Art personenbezogene Daten verarbeitet werden dürfen, bedürfen, neben einer ethisch-moralischen, vor allem einer rechtlichen Antwort. Die europäische Datenschutzgrundverordnung stellt hierzu den rechtlichen Rahmen dar, in dem personenbezogene Daten verarbeitet werden dürfen. In dieser Arbeit werden die gesetzlichen Anforderungen mit dem Konzept des Data Lakes abgeglichen und es wird aufgezeigt, wo Herausforderungen beim Design und bei der Implementierung eines Data Lakes entstehen (z.B. Transparenz, Zweckbindung, Recht auf Löschung). Zudem werden Lösungsansätze für diese Herausforderungen entwickelt und vorgestellt. Aus den einzelnen Lösungsansätzen werden zwei Lösungskonzepte für einige der identifizierten Herausforderungen entwickelt. Eines der Konzepte, ein Metadaten-Modell, wird dabei prototypisch umgesetzt und anhand von Use Cases beispielhaft getestet.Item Open Access Feature based volumetric terrain generation(2016) Becher, MichaelTwo-dimensional heightfields are the most common data structure used for storing and rendering of terrain in offline rendering and especially real-time computer graphics. By its very nature, a 2D heightfield cannot store terrain structures with multiple vertical layers such as overhangs and caves. This restriction is lifted if a volumetric data structure is chosen in place of a 2D heightfield. However, the workflow of manual modelling and editing of volumetric terrain usually involves a large number of minor edits and adjustments and is very time consuming. Therefore, I propose to use three-dimensional curve-based primitives to efficiently model prominent, large scale terrain features and present techniques for volumetric generation of a complete terrain surface from the sparse input data by means of diffusion-based algorithms. By combining an efficient, feature-based toolset with a volumetric terrain representation, the modelling workflow is accelerated and simplified while offering the full artistic freedom of volumetric terrain.Item Open Access Goal-driven context-sensitive production processes : a case study using BPMN(2016) Kar, DebasisThe Fourth Industrial Revolution, also known as Industry 4.0 or Industrial Internet, predicts that Smart Factories driven by Internet of Things (IoT) and Cyber-Physical Systems, will reinvent the traditional manufacturing industry into a digitalized, a context-aware, and an automated manufacturing that will flourish with contemporary Information and Communication Technology (ICT). As the IoT are being deployed across production cites of the manufacturing companies, the need of decision making inside a business process based upon the received contextual data such as employee availability, machine status, etc. from the execution environment has transpired. Production processes need to be updated and optimized frequently to stay competitive in the market. Context-sensitive Adaptive Production Processes is an adept concept that illustrates how a business process can be context-sensitive keeping itself aligned with the abstract organizational goals. The notion of Context-sensitive Adaptive Production Processes leads us to Context-sensitive Execution Step (CES), a logical construct, that encompasses multiple alternative processes, albeit the best-fitting alternative can only be selected, optimized, and executed in runtime. Realization of the context-sensitive business processes requires a model-driven approach. Being Business Process Model and Notation (BPMN) the de-facto standard for business processes modeling, business experts of manufacturing companies can use custom CES construct of BPMN to model and execute context-sensitive business processes in a model-driven approach. This case study is based upon a scenario where there exists multiple alternatives to achieve the same goal in production, nevertheless all the alternatives are not suitable at a certain point of time as changes in business objectives and execution environment makes adaption tougher. Properties of intelligent production processes are different from traditional processes. Such properties along with the scrutinized properties of standard BPMN facilitates modeling CES integrated processes in BPMN. From the requirements inferred from these properties, standard BPMN is extended with extensions such that context-sensitive business processes can be modeled and executed seamlessly. Developed extensions include a new type of process construct and a new type of process definition that are technology agnostic. Thus, CES approach provides a comprehensive solution that makes production processes contextsensitive as well as goal-driven in unison.Item Open Access Interactive ray tracing of solvent excluded surfaces(2018) Zahn, SebastianDomain experts in fields concerned with the behavior of molecules, for example biochemists, employ simulations to study a molecule’s individual properties and mutual interactions with other molecules. To obtain an intuitive spatial understanding of the returned data of the simulations, various visualization techniques such as molecular surfaces can be applied on the data. The solvent excluded surface depicts the boundary between the molecule’s and a solvent’s occupied space and therefore the molecules accessibility for the solvent. Insight about a molecule’s potential for interaction such as reactions can be gained by studying the surface’s shape visually. Current implementations for the visualization of the surface usually utilize GPU ray casting to achieve the performance required to allow interactivity such as viewpoint changing. However, this makes implementation of physically motivated effects like ambient occlusion or global illumination difficult. If compute resources do not contain GPUs, which is often the case in compute clusters, expensive software rasterization has to be employed instead. As CPUs offer less parallelism compared to GPUs, overhead introduced by the overdraw of thousands of primitives should be avoided. To mitigate these issues, CPU visualization approaches resurfaced again in recent times. In this work, the solvent excluded surface is visualized interactively using the classic ray tracing approach within the OSPRay CPU ray tracing framework. The described implementation is able to compute and visualize the solvent excluded surface for datasets composed of millions of atoms. Additionally, the surface supports transparency rendering, which allows implementation of a cavity visualization method that uses ambient occlusion.Item Open Access Development and analysis of a window manager concept for consolidated 3D rendering on an embedded platform(2015) Zhao, HanNowadays with the information technology rapidly developing, an increasing number of 2D and 3D graphics are used in automotive displaying systems, to provide vehicle information, driving assistance, etc. With the demand of 3D models interacting with each other, an implementation should have a 3D compositing capability. However, traditional 2D compositing implementations are not capable of 3D models compositing tasks. In order to composite 3D graphics on embedded platform, the 3D compositing implementation is necessary. Therefore, a concept of window manager is developed aiming to composite 3D graphics with an optimized efficiency for embedded platform. Specially for automotive platforms, a virtualization is made to unify multiple Electronic Control Units (ECUs) into one single ECU platform. On this platform, a server and multiple clients are implemented with dedicated Virtual Machines (VMs). The server is in charge of rendering tasks requested from clients. Based on this, a 3D compositing concept is implemented. It handles efficiently the multiple 3D applications situation using a method of off-screen rendering. A server-side virtualization is also implemented by replacing certain client-side commands during commands forwarding. With this virtualization implementation, multiple applications run simultaneously with accessing single 3D GPU only. Moreover, due to this implementation, monolithic rendering operations affecting all applications, e.g. uniform lighting operation, are possible.Item Open Access Location-history partitioning algorithms for privacy in non-trusted geo-social networks(2017) Zhang, QiDue to the rapid development of mobile device technology in the past couple of decades, mobile devices are playing a more and more important part in our daily life. Many mobile services along with mobile devices have integrated into our activities or even reshaped our lifestyle. Location services are one of the main mobile services being widely used. One can share ones location to get to know information nearby, and it could be shared with friends in social media. New mobile applications are showing up at an amazing speed, together with that, the usage of location data is a privacy threat. If too much information is shared, the user's movements could be predicted; highly privacy sensitive locations, such as home location of user, could be leaked. Many location based applications, such as geo-social networks (GSN), use Location Servers to store user position information. However, since GSN providers may not be fully trustworthy or may not be able to protect user data, users may not want to store all of their privacy-sensitive location information with a single provider. Therefore, this thesis focuses on developing and evaluating methods to partition location data among multiple servers as similarly attempted in other approaches. In this thesis, we try to partition location data to achieve privacy protection. We have studied a range of mobility modeling methods that consider the different fundamental dimensions of the location data, i.e., spatial, temporal, and semantic, as well as their combinations. Inspired by those methods, we have proposed partitioning methods to increase privacy protection. Furthermore, a couple of other partition methods, which are combinations of spatial, temporal and semantic, are implemented. Eventually all the partition methods are evaluated with our data.Item Open Access Optimizing the efficiency of data-intensive Data Mashups using Map-Reduce(2017) Sarangi, SunayanaIn order to derive knowledge and information from data through data processing, data integration and data analysis, a variety of Data Mashup tools have been developed in the past. Data Mashups are pipelines that process and integrate data based on different interconnected operators that realize data operations such as filter, join, extraction, alteration or integration. The overall goal is to integrate data from different sources into a single one. Most of these Mashup tools offer a grahical modeling platform, enabling the users to model the data sources, data operations and the data flow, thus, creating a so called Mashup Plan. This enables non-IT experts to perform data operations without having to deal with their technical details. Further, by allowing easy re-modeling and re-execution of the Mashup Plan, it also allows an iterative and explorative trial-an-error integration to enable real time insights into the data. These existing Data Mashup tools are efficient in executing small size data sets, however, they do not emphasize on the run-time efficiency of the data operations. This work is motivated by the limitations of current Data Mashup approaches with regard to data-intensive operations. The run-time of a data operation majorly varies depending on the size of the input data. Hence, in scenarios where one data operation expects inputs from multiple Data Mashup pipelines, which are executed in parallel, a data intensive operation in one of the Data Mashup pipelines leads to a bottleneck, thereby delaying the entire process. The efficiency of such scenarios can be greatly improved by executing the data-intensive operations in a distributed manner. This master thesis copes with this issue through an efficiency optimization of pipeline operators based on Map-Reduce. The Map-Reduce approach enables distributed processing of data to improve the run-time. Map-Reduce is divided into two main steps: (i) the Map step divides a data set into multiple smaller data sets, on which the data operations can be applied in parallel, and (ii) the Reduce step aggregates the results into one data set. The goal of this thesis is to enable a dynamic decision making while selecting suitable implementations for the data operations. This mechanism should be able to dynamically decide, which pipeline operators should be processed in a distributed manner, such as using a Map-Reduce implementation, and which operators should be processed by existing technologies, such as in-memory processing by Web Services. This decision is important because Map-Reduce itself can lead to a significant overhead while processing small data sets. Once it is decided that an operation should be processed using Map-Reduce, corresponding Map-Reduce jobs are invoked that process the data. This dynamic decision making can be achieved through WS-Policies. Web Services use policies to declare in a consistent and standardized manner what they are capable of supporting and which constraints and requirements they impose on their potential requestors. By comparing the capabilities of the Web Service with the requirements of the service requestor, it can be decided if the implementation is suitable for executing the data operation.Item Open Access Large-scale data mining analytics based on MapReduce(2014) Ranjan, SunnyIn this work, we search for possible approaches to large-scale data mining analytics. We perform an exploration about the existing MapReduce and other MapReduce-like frameworks for distributed data processing and the distributed file systems for distributed data storage. We study in detail about Hadoop Distributed File System (HDFS) and Hadoop MapReduce software framework. We analyse the benefits of newer version of Hadoop software framework which provides better scalability solution by segregating the cluster resource management task from MapReduce framework. This version is called YARN and is very flexible in supporting various kinds of distributed data processing other than batchmode processing of MapReduce. We also looked into various implementations of data mining algorithms based on MapReduce to derive a comprehensive concept about developing such algorithms. We also looked for various tools that provided MapRedcue based scalable data mining algorithms. We could only find Mahout as a tool specially based on Hadoop MapReduce. But the tool developer team decided to stop using Hadoop MapReduce and to use instead Apache Spark as the underlying execution engine. WEKA also has a very small subset of data mining algorithms implemented using MapReduce which is not properly maintained and supported by the developer team. Subsequently, we found out that Apache Spark, apart from providing an optimised and a faster execution engine for distributed processing also provided an accompanying library for machine learning algorithms. This library is called Machine Learning library (MLlib). Apache Spark claimed that it is much faster than Hadoop MapReduce as it exploits the advantages of in-memory computations which is particularly more beneficial for iterative workloads in case of data mining. Spark is designed to work on variety of clusters: YARN being one of them. It is designed to process the Hadoop data. We selected to perform a particular data mining task: decision tree learning based classification and regression data mining. We stored properly labelled training data for predictive mining tasks in HDFS. We set up a YARN cluster and run Spark's MLlib applications on this cluster. These applications use the cluster managing capabilities of YARN and the distributed execution framework of Spark core services. We performed several experiments to measure the performance gains, speed-up and scaleup of implementations of decision tree learning algorithms in Spark's MLlib. We found out much better than expected results for our experiments. We achieved a much higher than ideal speed-up when we increased the number of nodes. The scale-up is also very excellent. There is a significant decrease in run-time for training decision tree models by increasing the number of nodes. This demonstrates that Spark's MLlib decision tree learning algorithms for classification and regression analysis are highly scalable.Item Open Access Entwicklung von Algorithmen zur Planung der Wege von fahrerlosen Transportsystemen in einem Logistik-Warehouse(2017) Braunschweiger, DirkIn der Automobilindustrie ist in den letzten Jahren die Anforderungen an die Logistik-Warenhäuser gestiegen. Die steigende Individualisierung von Fahrzeugen ist der Grund dafür. Um die Anforderungen erfüllen zu können, werden in modernen Logistik-Warenhäusern die Waren durch fahrerlose Transportfahrzeuge transportiert. Es existieren viele Algorithmen zur Berechnung des kürzesten Weges für einzelne Fahrzeuge. Diese können in Warenhäusern mit vielen Fahrzeugen nicht eingesetzt werden, da es zu Staus, Deadlocks oder Kollisionen kommen kann. Es existieren bereits Algorithmen, die versuchen diese Probleme zu lösen. Wenige dieser Algorithmen wurden bisher auf die Praxistauglichkeit getestet. Die Algorithmen werden oft mit wenigen Fahrzeugen oder auf kleinen Straßennetzen getestet. Diese Arbeit stellt Algorithmen zur Berechnung von Wegen für mehrere Fahrzeuge vor und analysiert diese anschließend. Die Performanz der Algorithmen wird anhand realer Szenarien aus der Automobilindustrie gemessen. Dafür werden zuerst Straßennetze basierend auf echten Lagerhallen erstellt. Anschließend wird in verschiedenen Benchmarks die Performanz ausgewählter Algorithmen miteinander verglichen. Basierend auf den besten Algorithmen wird ein neuer Algorithmus entwickelt und mit bestehenden Algorithmen verglichen. Der neue Algorithmus benötigt weniger Rechenzeit und berechnet kürzere Wege. Die Ergebnisse werden abschließend mithilfe einer Simulations-Software validiert.