Este trabalho compara o desempenho e a estabilidade de dois arcabouços para o processamento de Big Data: Apache Spark e High Performance Analytics Toolkit (HPAT). A comparação foi realizada usando duas aplicações: soma dos elementos de um vetor unidimensional e o algoritmo de clusterização K-means. Os experimentos foram realizados em ambiente distribuído e com memória compartilhada com diferentes quantidades e configurações de máquinas virtuais. Analisando os resultados foi possível concluir que o HPAT tem um melhor desempenho em relação ao Apache Spark nos nossos casos de estudo. Também realizamos uma análise dos dois arcabouços com a presença de falhas. / This work compares the performance and stability of two Big Data processing tools: Apache Spark and High Performance Analytics Toolkit (HPAT). The comparison was performed using two applications: a unidimensional vector sum and the K-means clustering algorithm. The experiments were performed in distributed and shared memory environments with different numbers and configurations of virtual machines. By analyzing the results we are able to conclude that HPAT has performance improvements in relation to Apache Spark in our case studies. We also provide an analysis of both frameworks in the presence of failures.

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Bakalauro baigiamajame darbe buvo sukurtas mobilus žiniatiklio serveris su PostgreSQL ir PHP, bei papildomais moduliais. Buvo atlikta panašių sistemų analizė. Buvo sukurta reikalavimų specifikacija, architektūros specifikacija aliktas testavimas, sukurtas vartotojo ir diegimo vadovas. / Aim of the work – to develop a mobile (portable) web server software with PHP, PostgreSQL and other modules. In this work you can find analysis of others mobile web servers, some information about existing solutions, established requirements for servers software. Based on system requirements specification and architectural specification system testing was conducted in order to find and fix system errors and also check system compatibility with different MS Windows operating systems. User manual is also added.

Este trabalho compara o desempenho e a estabilidade de dois arcabouços para o processamento de Big Data: Apache Spark e High Performance Analytics Toolkit (HPAT). A comparação foi realizada usando duas aplicações: soma dos elementos de um vetor unidimensional e o algoritmo de clusterização K-means. Os experimentos foram realizados em ambiente distribuído e com memória compartilhada com diferentes quantidades e configurações de máquinas virtuais. Analisando os resultados foi possível concluir que o HPAT tem um melhor desempenho em relação ao Apache Spark nos nossos casos de estudo. Também realizamos uma análise dos dois arcabouços com a presença de falhas. / This work compares the performance and stability of two Big Data processing tools: Apache Spark and High Performance Analytics Toolkit (HPAT). The comparison was performed using two applications: a unidimensional vector sum and the K-means clustering algorithm. The experiments were performed in distributed and shared memory environments with different numbers and configurations of virtual machines. By analyzing the results we are able to conclude that HPAT has performance improvements in relation to Apache Spark in our case studies. We also provide an analysis of both frameworks in the presence of failures.

The aim of this thesis is to show how to design and implement an application for network traces analysis using Apache Spark distributed system. Implementation can be divided into three parts - loading data from a distributed HDFS storage, supported network protocols analysis and distributed data processing. As a data visualization tool is used web-based notebook Apache Zeppelin. The resulting application is able to analyze individual packets as well as the entire flows. It supports JSON and pcap as input data formats. The goal of the application is to allow Big Data processing. The greatest impact on its performance has the input data format and allocation of the available cores.

This thesis focuses on the issue of rendering web pages using vector graphics. The experimental CSSBox display engine and existing libraries for rendering vector graphics in PDF and SVG will be described here. The goal of the thesis is design a common structure for these two libraries and unify the process of rendering web pages in vector graphics as much as possible. Analysis of incorrectly implemented parts of existing solutions will be performed here and shortcomings, which will be necessary to implement to the resulting vector graphic meet the standard CSS3, will be describe. Furthermore, the implementation process, including the repair of all non-functioning original solutions, and the principle of unification of individual parts for the generation of both vector formats will be described. The conclusion is dedicated the results of self testing and outputs from generating real websites.

Diese Arbeit behandelt die Entstehung eines Prototypen für die Übertragung von hochsensiblen Daten zwischen verschieden Firmen. Dabei geht Sie auf alle Schritte bei der Entwicklung ein von der Anforderungsanalyse über die Evaluierung einer passenden Technologie und die eigentliche Implementierung bis hin zum Test und der Administration.:1. Einleitung 1 2. Anforderungsanalyse 2 2.1. Ist-Analyse 2 2.2. Definition weiterer Anforderungen 3 2.3. Ergebnisse der Anforderungsanalyse 4 2.3.1. Begriffserklärungen 4 2.3.2. Ablauf 4 2.3.3. Anforderungen 6 3. Evaluierung verschiedener Technologien 8 3.1. E-Mail 8 3.2. SSL über TCP 8 3.3. SVN-Repository 9 3.4. Apache Camel 10 3.5. SIMON (RPC) 10 3.6. RESTful Webservice 11 3.7. OSGi 11 3.8. Apache Shiro 13 3.9. Bouncy Castle 14 3.10. Weitere Technologien 14 4. Implementierungsmöglichkeiten 15 4.1. Vorwort 15 4.2. Aufteilung der Anforderungen in Module für OSGi 16 4.3. Apache Camel 18 4.4. SIMON 19 4.5. RESTful-Webservice 20 4.6. SVN als Transporttechnik 21 4.7. Konfigurationen 22 4.7.1. Serverspezifische Konfiguration 22 4.7.2. Projektspezifische Konfiguration 23 4.8. Bestätigung durch einen Nutzer 23 4.9. Ver-/Entschlüsselung 23 4.10. Konverter 23 5. Implementation der Datenübertragung 25 5.1. Apache Camel 25 5.2. RESTful Webservice 25 5.2.1. Restlet-Ressourcen und iPOJO Abhängigkeitsverwaltung 25 5.2.2. Datenübertragung 26 5.3. SIMON 27 5.4. SVN 27 5.5. Entscheidung 28 6. Implementation mit Apache Camel 30 6.1. iPOJO Abhängigkeitsmanagement 30 6.2. Datenübertragung 32 6.3. Ein- und Ausgabe 33 6.3.1. Eingabe von Daten 33 6.3.2. Ausgabe von empfangenen Daten 33 6.4. Ver-/Entschlüsselung 34 6.5. Authentifikation 35 6.6. Bestätigung durch einen Nutzer 36 6.7. Konverter 37 6.8. Abfragemechanismus 38 6.8.1. Speichern von Nachrichten 39 6.8.2. Anfrage stellen 39 6.8.3. Antworten auf Anfragen 40 6.9. Konfiguration 40 6.9.1. Auslesen der Konfiguration 40 6.9.2. Validierung der Konfiguration 41 6.9.3. Verschlüsselung von sensiblen Informationen 41 6.10. Fehlerbehandlung 42 6.10.1. Datenübertragung 42 6.10.2. Authentifikation 42 6.10.3. Konfiguration 43 6.11. Lizenzen 43 6.11.1. Lizenzübersicht der eingesetzten Technologien 43 6.11.2. Lizenzerklärungen 44 7. Inbetriebnahme und Verwaltung 45 7.1. Inbetriebnahme des Servers 45 7.2. Verwaltung der OSGi Umgebung 45 7.2.1. System-Information 45 7.2.2. Bundle-Übersicht 45 7.2.3. iPOJO Komponenten-Übersicht 45 7.2.4. iPOJO Instanz-Ansicht 46 7.3. Aktualisierung einzelner Komponenten 46 7.4. Bekannte Fehler 46 7.4.1. Endlosschleife nach Verbindungsverlust zum E-Mail-Server 46 7.4.2. Update der Module Commons und Interfaces zeigt keine Wirkung 47 7.4.3. bcprov is no longer valid. 47 8. Test 48 8.1. Unit-Tests 48 8.2. Systemtest 50 9. Ergebnisse und Ausblick 51 Anhang 54

The exponential growth of social media usage has led to massive data sharing, posing challenges for traditional systems in managing and analyzing such vast amounts of data. This surge in data exchange has also resulted in an increase in cyber threats from individuals and criminal groups. Traditional forensic methods, such as evidence collection and data backup, become impractical when dealing with petabytes or terabytes of data. To address this, Big Data Analytics has emerged as a powerful solution for handling and analyzing structured and unstructured data. This thesis explores the use of Apache Flink, an open-source tool by the Apache Software Foundation, to enhance cybercrime forensic research. Unlike batch processing engines like Apache Spark, Apache Flink offers real-time processing capabilities, making it well-suited for analyzing dynamic and time-sensitive data streams. The study compares Apache Flink's performance against Apache Spark in handling various workloads on a single node. The literature review reveals a growing interest in utilizing Big Data Analytics, including platforms like Apache Flink, for cybercrime detection and investigation, especially on social media platforms like X (formerly known as Twitter). Sentiment analysis is a vital technique, but challenges arise due to the unique nature of social data. X (formerly known as Twitter), as a valuable source for cybercrime forensics, enables the study of fraudulent, extremist, and other criminal activities. This research explores various data mining techniques and emphasizes the need for real-time analytics to combat cybercrime effectively. The methodology involves data collection from X, preprocessing to remove noise, and sentiment analysis to identify cybercrime-related tweets. The comparative analysis between Apache Flink and Apache Spark demonstrates Flink's efficiency in handling larger datasets and real-time processing. Parallelism and scalability are evaluated to optimize performance. The results indicate that Apache Flink outperforms Apache Spark regarding response time, making it a valuable tool for cybercrime forensics. Despite progress, challenges such as data privacy, accuracy improvement, and cross-platform analysis remain. Future research should focus on refining algorithms, enhancing scalability, and addressing these challenges to further advance cybercrime forensics using Big Data Analytics and platforms like Apache Flink.