The goal of diploma thesis is disquisition on existing file format intended for data transmission over computer network. Choose one of the compared file format and create a file structure for transmission sports data. Assess the structure of files from existing sports data suppliers and create their own structure. Compare the possible ways of distribution of created data files and give recommendations for services implementers.

This report discusses network automation primarily with Ansible. Ansible is a software from Red Hat that can be used for network automation. The report also goes through YAML which is a standardized way of exchanging data, Jinja2 that is a templating language, Python as well as the security with Ansible. The report also goes through why network automation is needed as well as how much time might be saved with Ansible. Ansible ships with modules for Cisco IOS such as ios_config and ios_command and for Cisco ASA asa_config, asa_command and asa_acl as well as many other modules for Arista, Juniper and for other vendors. Ansible can use new APIs by creating new modules for handling that particular API, which means that the only change needed in the playbooks is to change the module name. Ansible can handle NETCONF API using the netconf_config module or various Juniper modules. Ansible is used in this report to perform certain tasks such as to adding VLAN's, close ports on ASA's, audit network devices configuration as well as to create network diagram using the information from CDP. Ansible can be made as secure as manually doing the tasks except that Ansible can do it faster and more consistently.  For connecting to normal Linux servers Ansible uses OpenSSH which is a default SSH client on most Linux systems and for connecting to network devices it uses Paramiko. The security in Ansible depends on SSH and may or may not have passwords stored locally, Ansible can be as secure as the administrator wants it to be such as using RSA key-pair to authenticate, using vault encrypted credentials or asking the administrator about which username and password to use. Using Ansible network automation can save time, the amount saved depends on what is being done, how many devices it is doing it on as well as how the playbook is written. / Rapporten behandlar nätverksautomation primärt i Ansible. Ansible är en mjukvara från Red Hat som kan användas för nätverksautomering. Rapporten går igenom YAML som är ett sätt att standardisera överförning av data, Jinja2 som är ett mallspråk, Python samt säkerheten i Ansible. Rapporten går dessutom igenom varför vi ens vill ha nätverksautomation och hur mycket tid som möjligtvis kan sparas. Ansible kommer med moduler för Cisco IOS som exempelvis ios_config och ios_command och för Cisco ASA finns moduler så som asa_config, asa_command och asa_acl. För andra tillverkare så finns det moduler för Arista, Juniper och för andra leverantörer. Om en ny API kommer ut för en ny enhet så kan en ny Ansible modul skapas som använder denna, vilket betyder att Ansible playbooks kan då använda sig av de nya modulerna med samma struktur som tidigare. Ansible kan hantera NETCONF API med hjälp av netconf_config modulen och av flertalet Juniper moduler. Ansible kan användas på ett lika säkert sätt som manuellt arbete, med undantag på att Ansible gör det snabbare och mer konsekvent. För uppkoppling till vanliga Linux-servrar så använder Ansible OpenSSH klienten som standard och mot nätverksenheter utan Python installerat så används Python biblioteket Paramiko. Ansible använder sig av SSH och kan ha lösenord sparat i playbooken, utanför i annan fil, i ett krypterat vault, fråga användaren som användarnamn och lösenord samt autentisering med hjälp av RSA nycklar. Ansible används för att skapa olika VLAN, stänga portar på en ASA, granska nätverksenhetens konfiguration gentemot vad den borde ha för konfiguration samt för att skapa nätverksdiagram baserat på informationen från CDP. Genom att använda sig av Ansible nätverksautomation så kan tid sparas, hur mycket beror helt på vad som ska göras, hur många enheter det ska göras på samt hur playbooken faktiskt är skapad.

В работе поднимается проблема отсутствия необходимых методов и технологий тестирования устройств Интернета вещей в автоматическом режиме. В качестве решения проблемы предложена методология тестирования встроенного программного обеспечения «умных» устройств. Методология была реализована в сервисе «ядро тестирования». Апробация работы сервиса проводились на примере проведения испытаний счетчика электрической энергии Милур 307 с интерфейсным модулем Карат-941LW. Автоматизация позволила значительно сократить время испытаний по сравнению с ручным тестированием. / The thesis raises the problem of the lack of necessary methods and technologies for testing IoT devices in automatic mode. As a solution to the problem, a methodology for testing the embedded software of “smart” devices is proposed. The methodology was implemented in the testing core service. The approbation of the service was carried out on the example of testing the Milur 307 electric energy meter with the Karat-941LW interface module. Automation has significantly reduced test time compared to manual testing.

The aim of this master's thesis is a design and implementation of a program for finding security and operational deficiencies of network devices and afterwards, resolving them by generating corrective configuration. Due to a lack of security and misconfiguration, there are a lot of devices exposed to the risk of a security incident. Therefore, the program compares settings with various standards, recommendations, and best practices and generates a report with findings. Afterwards, deficiencies can be eliminated by automatic resolution or manually if automatic resolving is not possible. The program uses regular expressions to find problem settings in previously exported configurations. Implementation is written in Python, and YAML markup language is used too. Another output of this thesis is a checklist, which can be used for the creation of future modules for support of other network device vendors and thus extend the program.

This thesis analyzes the configuration and security requirements of an auto-mated assignment testing system. The requirements for a flexible yet powerfulconfiguration format is discussed in depth, and an appropriate configurationformat is chosen. Additionally, the overall security requirements of this systemis discussed, analyzing the different alternatives available to fulfill the require-ments. / <p>Framläggningen redan avklarad.</p>

This thesis is focused on application development with ARM processor architecture and Linux operating system. It uses cloud hosting services to build, test and deploy applications with container-based virtualization. The application communicates with smart meters on serial buses. It stores measurements in a database. It provides access to devices and measurements in internet network.

Cílem této práce je vytvořit systém, který je schopný automaticky určit množinu testů, které mají být spuštěny, když dojde v ComplianceAsCode projektu ke změně. Navržená metoda vybírá množinu testů na základě statické analýzy změněných zdrojových souborů, přičemž bere v úvahu vnitřní strukturu ComplianceAsCode. Vytvořený systém je rozdělen do čtyř částí - získání změn s využitím verzovacího systému, statická analýza různých typů souborů, zjištění souborů, které jsou ovlivněny těmi změnami, a výpočet množiny testů, které musí být spuštěny pro danou změnu. Naimplementovali jsme analýzu několika různých typů souborů a náš systém je navržen tak, aby byl jednoduše rozšiřitelný o analýzy dalších typů souborů. Vytvořená implementace je nasazena na serveru, kde automaticky analyzuje nové příspěvky do ComplianceAsCode projektu. Automatické spouštění informuje přispěvatelé a vývojáře o nalezených změnách a doporučuje, které testy by pro danou změnu měly být spuštěny. Tím je ušetřen čas strávený při kontrole správnosti příspěvků a čas strávený spouštěním testů.

Mit der wachsenden Popularität von GitHub, dem größten Online-Anbieter von Programm-Quellcode und der größten Kollaborationsplattform der Welt, hat es sich zu einer Big-Data-Ressource entfaltet, die eine Vielfalt von Open-Source-Repositorien (OSR) anbietet. Gegenwärtig gibt es auf GitHub mehr als eine Million Organisationen, darunter solche wie Google, Facebook, Twitter, Yahoo, CRAN, RStudio, D3, Plotly und viele mehr. GitHub verfügt über eine umfassende REST API, die es Forschern ermöglicht, wertvolle Informationen über die Entwicklungszyklen von Software und Forschung abzurufen. Unsere Arbeit verfolgt zwei Hauptziele: (I) ein automatisches OSR-Kategorisierungssystem für Data Science Teams und Softwareentwickler zu ermöglichen, das Entdeckbarkeit, Technologietransfer und Koexistenz fördert. (II) Visuelle Daten-Exploration und thematisch strukturierte Navigation innerhalb von GitHub-Organisationen für reproduzierbare Kooperationsforschung und Web-Applikationen zu etablieren. Um Mehrwert aus Big Data zu generieren, ist die Speicherung und Verarbeitung der Datensemantik und Metadaten essenziell. Ferner ist die Wahl eines geeigneten Text Mining (TM) Modells von Bedeutung. Die dynamische Kalibrierung der Metadaten-Konfigurationen, TM Modelle (VSM, GVSM, LSA), Clustering-Methoden und Clustering-Qualitätsindizes wird als "Smart Clusterization" abgekürzt. Data-Driven Documents (D3) und Three.js (3D) sind JavaScript-Bibliotheken, um dynamische, interaktive Datenvisualisierung zu erzeugen. Beide Techniken erlauben Visuelles Data Mining (VDM) in Webbrowsern, und werden als D3-3D abgekürzt. Latent Semantic Analysis (LSA) misst semantische Information durch Kontingenzanalyse des Textkorpus. Ihre Eigenschaften und Anwendbarkeit für Big-Data-Analytik werden demonstriert. "Smart clusterization", kombiniert mit den dynamischen VDM-Möglichkeiten von D3-3D, wird unter dem Begriff "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA" zusammengefasst. / With the growing popularity of GitHub, the largest host of source code and collaboration platform in the world, it has evolved to a Big Data resource offering a variety of Open Source repositories (OSR). At present, there are more than one million organizations on GitHub, among them Google, Facebook, Twitter, Yahoo, CRAN, RStudio, D3, Plotly and many more. GitHub provides an extensive REST API, which enables scientists to retrieve valuable information about the software and research development life cycles. Our research pursues two main objectives: (I) provide an automatic OSR categorization system for data science teams and software developers promoting discoverability, technology transfer and coexistence; (II) establish visual data exploration and topic driven navigation of GitHub organizations for collaborative reproducible research and web deployment. To transform Big Data into value, in other words into Smart Data, storing and processing of the data semantics and metadata is essential. Further, the choice of an adequate text mining (TM) model is important. The dynamic calibration of metadata configurations, TM models (VSM, GVSM, LSA), clustering methods and clustering quality indices will be shortened as "smart clusterization". Data-Driven Documents (D3) and Three.js (3D) are JavaScript libraries for producing dynamic, interactive data visualizations, featuring hardware acceleration for rendering complex 2D or 3D computer animations of large data sets. Both techniques enable visual data mining (VDM) in web browsers, and will be abbreviated as D3-3D. Latent Semantic Analysis (LSA) measures semantic information through co-occurrence analysis in the text corpus. Its properties and applicability for Big Data analytics will be demonstrated. "Smart clusterization" combined with the dynamic VDM capabilities of D3-3D will be summarized under the term "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA".