The sheer increase in the volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark defines the state of the art in big data analytics platforms for (i) exploiting data-flow and in-memory computing and (ii) for exhibiting superior scale-out performance on the commodity machines, little effort has been devoted to understanding the performance of in-memory data analytics with Spark on modern scale-up servers. This thesis characterizes the performance of in-memory data analytics with Spark on scale-up servers.Through empirical evaluation of representative benchmark workloads on a dual socket server, we have found that in-memory data analytics with Spark exhibit poor multi-core scalability beyond 12 cores due to thread level load imbalance and work-time inflation (the additional CPU time spent by threads in a multi-threaded computation beyond the CPU time required to perform the same work in a sequential computation). We have also found that workloads are bound by the latency of frequent data accesses to the memory. By enlarging input data size, application performance degrades significantly due to the substantial increase in wait time during I/O operations and garbage collection, despite 10% better instruction retirement rate (due to lower L1cache misses and higher core utilization).For data accesses, we have found that simultaneous multi-threading is effective in hiding the data latencies. We have also observed that (i) data locality on NUMA nodes can improve the performance by 10% on average,(ii) disabling next-line L1-D prefetchers can reduce the execution time by upto14%. For garbage collection impact, we match memory behavior with the garbage collector to improve the performance of applications between 1.6xto 3x and recommend using multiple small Spark executors that can provide up to 36% reduction in execution time over single large executor. Based on the characteristics of workloads, the thesis envisions near-memory and near storage hardware acceleration to improve the single-node performance of scale-out frameworks like Apache Spark. Using modeling techniques, it estimates the speed-up of 4x for Apache Spark on scale-up servers augmented with near-data accelerators. / <p>QC 20171121</p>

There is a growing volume of global data, which is offering new possibilities for those market participants, who know to take advantage of it. Data, information and knowledge are new highly regarded commodity especially in the banking industry. Traditional data analytics is intended for processing data with known structure and meaning. But how can we get knowledge from data with no such structure? The thesis focuses on Big Data analytics and its use in banking and financial industry. Definition of specific applications in this area and description of benefits for international and Czech banking institutions are the main goals of the thesis. The thesis is divided in four parts. The first part defines Big Data trend, the second part specifies activities and tools in banking. The purpose of the third part is to apply Big Data analytics on those activities and shows its possible benefits. The last part focuses on the particularities of Czech banking and shows what actual situation about Big Data in Czech banks is. The thesis gives complex description of possibilities of using Big Data analytics. I see my personal contribution in detailed characterization of the application in real banking activities.

This thesis deals with the measurement of the quality of the stemming/lemmatization algo-rithm for the Czech language in document processing systems and provides an analysis of the results. The theoretical part of the thesis describes the principles of the full-text search, the possibilities of implementation as well as the common problems which have to be solved in connection with the processing of natural language. Methods of evaluating the quality of lemmatization, using recall and precision, are discussed. In addition, the theoret-ical part covers the method of measuring the index of under-stemming and over-stemming, which can be applied for the purposes of a more detailed evaluation. An experiment for evaluating the lemmatization algorithms is proposed in the second part of the thesis. A specialized application has been developed to perform the experiment in three different systems, namely Apache Lucene, the PostgreSQL database systems and the Microsoft SQL Server. The experiment is based on the Prague Dependency Treebank cor-pus. It has been carried out both for the corpus as a whole and for selected word classes separately. Further analysis of the results for Czech stemmer in Apache Lucene leads to a proposal for several modifications of the algorithm. Such modifications result in measurable improvements. The results achieved show how metrics discussed, together with the values measured, can be used for improving the lemmatization algorithms and thus to improve the full-text search for Czech language.

The aim of this master thesis is to propose a system for monitoring and regulation the climate in groups of terrariums using Raspberry Pi platform and with subsequent well-arranged representation through the web server. Each group of terrariums has its own measure and regulation device Arduino NANO that wirelessly communicates with central computer Raspberry Pi. The measured values are stored in the MySQL database in central computer. These values are then graphically displayed on the web page. Limit values for regulation and regulation times can also be set through web page.

The goal of this thesis is to design and to implement the active and passive monitoring and control system for small company with emphasis on the most automated control and deployment in a production network. System is appropriately designed using the Nette framework and scripting language Perl which is suitable for control computer systems and networks.

The aim of this master thesis is to propose a system for monitoring and regulating the climate in groups of terrariums using the Raspberry Pi platform and subsequent transparent display through the web server. Each group of terrariums has its own control device that wirelessly communicates with the Raspberry Pi control computer. The measured values are stored in the MySQL database on the control computer. The measured values are graphically displayed on the web page.

This master's thesis deals with a system for PC mass management and monitoring. There is a brief description of Puppet, Chef and Ansible, which are tools enabling this functionality. The overall system issues are solved by Ansible. This thesis also includes a script created for a classroom’s remote management according to a beforehand fixed assignment. The system enables remote installation and configuration of applications, creation of users, editing the files, update of the system and many more. A web application was created for this system. Through this application, the system can be controlled via web browser, both from local and public network. Practical examples of the issue dealt with is also part of the thesis.

Gemeinsamer Workshop von Universitaetsrechenzentrum und Professur Rechnernetze und verteilte Systeme (Fakultaet fuer Informatik) der TU Chemnitz. Workshop-Thema: Infrastruktur der ¨Digitalen Universitaet¨ Es gibt viele Möglichkeiten, dynamische Web Inhalte zu erzeugen. Dieser Vortrag soll einen Überblick über Erweiterungsmöglichkeiten auf der Serverseite geben. Mit Hinblick auf Performance werden im Vergleich zum CGI eingebettete Interpreter beleuchtet und spezielle Scriptsprachen wie PHP,Perl oder Tcl genannnt.

Moderne Intranet-Infrastruktur in der Universitätsbibliothek Neue Technologien im Patentinformationszentrum Einsatz bargeldloser Zahlungsmittel an der TU Chemnitz GPRS - General Packet Radio Service UMTS - mehr als nur zum Telefonieren gut Hinweise für WWW-Autoren: Zugriffskontrolle WFM: WWW-basierter Datei-Manager Automatisierte Freischaltung von Datendosen in öffentlichen Räumen der TU Chemnitz Aufrüstung der Computerpools Reichenhainer Str. B401/404 Neuigkeiten zu Campuslizenzen

This work describes models of modern language theory - finite automata, regular grammars and regular expressions. A web application converting among these models is implemented.