High Voltage Direct Current (HVDC) transmission systems appropriate for bulk power transfer to meet increasing power demands and ideal for interconnecting power systems with distant renewable sources of energy without any chances of loss synchronism, efficiency, and reliability. The main obstacle is however connected with the DC grid protection where the timely diagnosis of faults is critical to prevent any rapid built-up leading to failure of the power electronic devices. Monitoring the Power Quality (PQ) necessitates establishing novel criteria and techniques to deal with the abundance of data that are ever-growing with data flow from sensors and measuring units in the electric grid. This study developed a scalable and efficient clustering methodology for a transient waveform database from a HVDC station. The output could help HVDC Service better characterize the data and develop qualitative criteria for monitoring and analytics. The thesis expects to contribute towards a sustainable and reliable electric grid.

Diploma thesis deals with the topic of social networks and their usage in terms of business support. The main goal of the thesis is to find out the possibilies of using social networks for business support in the perspective fitness industry. Theoretical part contains recent theoretical findings in areas of online marketing, social media, social networks and use of social networks for business. The analysis of the tools for monitoring and analysis of social networks is an important part of the theoretical part. One chosen tool is used for the comprehensive competitor analysis of online shops, that deal with online sales of fitness products and supplements. The outcome of the thesis is the system design of use of social networks for the support of competitiveness in chosen sector.

This master thesis deals with the field of possibilities of using social networks to increase competitiveness. In first part there are theoretically explained terms such as social network, social media and then there is described possibility to use social networks in business. The thesis includes theoretical treatment procedure entry of the company on social networks and describes appropriate monitoring and analyzing tools. The practical part is focused on dance club Beton, there are made the critical analysis of current attitudes of the club forward to social network, competitive analysis and comparison between Beton's and competitor's social network activities. The last part is devoted to selection appropriate monitoring tool and designed a social media monitoring system.

Modern semiconductor manufacturing is a complex process with a multitude of software applications. This application landscape has to be constantly monitored, since the communication and access patterns provide important insights. Because of the high event rates of the equipment log data stream in modern factories, big-data tools are required for scalable state and history analytics. The choice of suitable big-data solutions and their technical realization remains a challenging task. This thesis compares big-data architectures and discovers solutions for log-data ingest, enrichment, analytics and visualization. Based on the use cases and requirements of developers working in this field, a comparison of a custom assembled stack and a complete solution is made. Since the complete stack is a preferable solution, Datadog, Grafana Loki and the Elastic 8 Stack are selected for a more detailed study. These three systems are implemented and compared based on the requirements. All three systems are well suited for big-data logging and fulfill most of the requirements, but show different capabilities when implemented and used.:1 Introduction 1.1 Motivation 1.2 Structure 2 Fundamentals and Prerequisites 2.1 Logging 2.1.1 Log level 2.1.2 CSFW log 2.1.3 SECS log 2.2 Existing system and data 2.2.1 Production process 2.2.2 Log data in numbers 2.3 Requirements 2.3.1 Functional requirements 2.3.2 System requirements 2.3.3 Quality requirements 2.4 Use Cases 2.4.1 Finding specific communication sequence 2.4.2 Watching system changes 2.4.3 Comparison with expected production path 2.4.4 Enrichment with metadata 2.4.5 Decoupled log analysis 3 State of the Art and Potential Software Stacks 3.1 State of the art software stacks 3.1.1 IoT flow monitoring system 3.1.2 Big-Data IoT monitoring system 3.1.3 IoT Cloud Computing Stack 3.1.4 Big-Data Logging Architecture 3.1.5 IoT Energy Conservation System 3.1.6 Similarities of the architectures 3.2 Selection of software stack 3.2.1 Components for one layer 3.2.2 Software solutions for the stack 4 Analysis and Implementation 4.1 Full stack vs. a custom assembled stack 4.1.1 Drawbacks of a custom assembled stack 4.1.2 Advantages of a complete solution 4.1.3 Exclusion of a custom assembled stack 4.2 Selection of full stack solutions 4.2.1 Elastic vs. Amazon 4.2.2 Comparison of Cloud-Only-Solutions 4.2.3 Comparison of On-Premise-Solutions 4.3 Implementation of selected solutions 4.3.1 Datadog 4.3.2 Grafana Loki Stack 4.3.3 Elastic 8 Stack 5 Comparison 5.1 Comparison of components 5.1.1 Collection 5.1.2 Analysis 5.1.3 Visualization 5.2 Comparison of requirements 5.2.1 Functional requirements 5.2.2 System requirements 5.2.3 Quality requirements 5.3 Results 6 Conclusion and Future Work 6.1 Conclusion 6.2 Future Work / Die moderne Halbleiterfertigung ist ein komplexer Prozess mit einer Vielzahl von Softwareanwendungen. Diese Anwendungslandschaft muss ständig überwacht werden, da die Kommunikations- und Zugriffsmuster wichtige Erkenntnisse liefern. Aufgrund der hohen Ereignisraten des Logdatenstroms der Maschinen in modernen Fabriken werden Big-Data-Tools für skalierbare Zustands- und Verlaufsanalysen benötigt. Die Auswahl geeigneter Big-Data-Lösungen und deren technische Umsetzung ist eine anspruchsvolle Aufgabe. Diese Arbeit vergleicht Big-Data-Architekturen und untersucht Lösungen für das Sammeln, Anreicherung, Analyse und Visualisierung von Log-Daten. Basierend auf den Use Cases und den Anforderungen von Entwicklern, die in diesem Bereich arbeiten, wird ein Vergleich zwischen einem individuell zusammengestellten Stack und einer Komplettlösung vorgenommen. Da die Komplettlösung vorteilhafter ist, werden Datadog, Grafana Loki und der Elastic 8 Stack für eine genauere Untersuchung ausgewählt. Diese drei Systeme werden auf der Grundlage der Anforderungen implementiert und verglichen. Alle drei Systeme eignen sich gut für Big-Data-Logging und erfüllen die meisten Anforderungen, zeigen aber unterschiedliche Fähigkeiten bei der Implementierung und Nutzung.:1 Introduction 1.1 Motivation 1.2 Structure 2 Fundamentals and Prerequisites 2.1 Logging 2.1.1 Log level 2.1.2 CSFW log 2.1.3 SECS log 2.2 Existing system and data 2.2.1 Production process 2.2.2 Log data in numbers 2.3 Requirements 2.3.1 Functional requirements 2.3.2 System requirements 2.3.3 Quality requirements 2.4 Use Cases 2.4.1 Finding specific communication sequence 2.4.2 Watching system changes 2.4.3 Comparison with expected production path 2.4.4 Enrichment with metadata 2.4.5 Decoupled log analysis 3 State of the Art and Potential Software Stacks 3.1 State of the art software stacks 3.1.1 IoT flow monitoring system 3.1.2 Big-Data IoT monitoring system 3.1.3 IoT Cloud Computing Stack 3.1.4 Big-Data Logging Architecture 3.1.5 IoT Energy Conservation System 3.1.6 Similarities of the architectures 3.2 Selection of software stack 3.2.1 Components for one layer 3.2.2 Software solutions for the stack 4 Analysis and Implementation 4.1 Full stack vs. a custom assembled stack 4.1.1 Drawbacks of a custom assembled stack 4.1.2 Advantages of a complete solution 4.1.3 Exclusion of a custom assembled stack 4.2 Selection of full stack solutions 4.2.1 Elastic vs. Amazon 4.2.2 Comparison of Cloud-Only-Solutions 4.2.3 Comparison of On-Premise-Solutions 4.3 Implementation of selected solutions 4.3.1 Datadog 4.3.2 Grafana Loki Stack 4.3.3 Elastic 8 Stack 5 Comparison 5.1 Comparison of components 5.1.1 Collection 5.1.2 Analysis 5.1.3 Visualization 5.2 Comparison of requirements 5.2.1 Functional requirements 5.2.2 System requirements 5.2.3 Quality requirements 5.3 Results 6 Conclusion and Future Work 6.1 Conclusion 6.2 Future Work