This thesis covers the development of web applications with modern programming languages and technologies. The objective of thesis is the research and analysis of modern technologies and subsequent selection of particular programming languages, frameworks and database for development of web application applying those techniques. Based on analysis of modern trends I chose the following technologies: backend development using popular Spring framework based on Java EE, frontend implementation using one of the most widespread scripting languages JavaScript and its known framework AngularJS developed by Google. For data persistence I chose NoSQL database MongoDB. I provided detailed documentation of each implementation step: project definition, requirements analysis, design, implementation and testing.

The traditional definition of a dynamic web application is about a collection of programs executed at server-side to provide content for clients. These types of web applications produce content at server-side and deliver it to their clients via multiple pages. In result, the client-side has the responsibility to render the content and perform a limited amount of calculations to increase the performance and user experience.    Meteor is a web framework designed for developing Single Page Applications and compared with traditional web frameworks, it takes a new approach in which most of the computations are done at the client-side. This leads to having the server-side primarily used for data storage and secondarily performing a limited amount of computations based on the Model View View-Model pattern.    This thesis tries to examine how web development is affected by Meteor framework from different angles by performing an experimental analysis on Meteor framework. It will investigate different attributes of Meteor framework used for developing a real-world application and finally concludes by presenting the advantages and disadvantages of using it.

This paper focuses on NoSQL database systems. These systems currently serve rather as supplement than replacement of relational database systems. The aim of this paper is to compare 4 selected NoSQL database systems (MongoDB, Apache Cassandra, Apache HBase and Redis) with a main focus on performance and scalability. Performance comparison is done using simulated workload in a 4 nodes cluster environment. One relational SQL database is also benchmarked to provide comparison between classic and modern way of maintaining structured data. As the result of comparison I found out that none of these database systems can be labeled as "the best" as each of the compared systems is suitable for different production deployment.

Augmented reality (AR) has revolutionized the way people view the real world, AR has been used across a range of sectors. Recently, researchers examined the possibilities for improving user experience with augmented reality. However, there are few studies on adoption of AR users' interactions with online data resources. The aim of this study is proposing a mobile augmented reality interface for users to interact and engage with online data. The prototype is based on the framework of a PEAR (Public Engagement Augmented Reality) initiative for further AR development. PEAR framework provides an AR extension and enables users to engage with online information through AR representation [1]. This prototype was developed and implemented using Unity game engines C# and Vuforia SDK on the front-end, both NodeJS servers with MongoDB were used on the back-end. The prototype was tested and then used in a 2-week user study to analyse and validate the framework.

The purpose of this paper is to clarify the differences in latency between PostgreSQL and MongoDB as a consequence of their differences in software architecture. This has been achieved through benchmarking of Insert, Read and Update operations with the tool “Yahoo! Cloud Serving Benchmark”, and through source code analysis of both database management systems (DBMSs). The overall structure of the architecture has been researched with Big O notation as a tool to examine the complexity of the source code. The result from the benchmarking show that the latency for Insert and Update operations were lower for MongoDB, while the latency for Read was lower for PostgreSQL. The results from the source code analysis show that both DBMSs have a complexity of O(n), but that there are multiple differences in their software architecture affecting latency. The most important difference was the length of the parsing process which was larger for PostgreSQL. The conclusion is that there are significant differences in latency and source code and that room exists for further research in the field. The biggest limitation of the experiment consist of factors such as background processes which affected latency and could not be eliminated, resulting in a low validity.

Master thesis is focused on analysis, design and development of information system module for the company XYZ s. r. o. The design of the module’s architecture, application objects identification and the development of the module including the economic evaluation are all based on the requirements acquired from the company. Modul is developed in new JavaScript version and uses modern technologies such as React, Redux, REST, Node.js and MongoDB.

The goal of this this is to create an information system for the visualization of geographical data. The main idea is to allow users to create visualizations with their own geographical data, which they can either import from files or directly attach their own database system as a source of data and make use of the data in real-time. The result will be a new web information system that will act as a point of contact between users, geographical data, and visualizations.

In this thesis, there are discussed use-cases of NoSQL database MongoDB implemented as a datastore for user data, which is stored by Datastore stubs in Google App Engine SDK. Existing stubs are not very well optimized for higher load; they significantly slow down application development and testing if there is a need to store larger data sets in these storages. The analysis is focused on features of MongoDB, Google App Engine NoSQL Datastore and interfaces for data manipulation in SDK - Datastore Service Stub API. As a result, there was designed and implemented new datastore stub, which is supposed to solve problems of existing stubs. New stub uses MongoDB as a database layer for storing testing data and it is fully integrated into Google App Engine SDK.

This master‘s thesis focuses on distributed processing of big data from network communication. It begins with exploring network communication based on TCP/IP model with focus on data units on each layer, which is necessary to process during analyzation. In terms of the actual processing of big data is described programming model MapReduce, architecture of Apache Hadoop technology and it‘s usage for processing network flows on computer cluster. Second part of this thesis deals with design and following implementation of the application for processing network flows from network communication. In this part are discussed main and problematic parts from the actual implementation. After that this thesis ends with a comparison with available applications for network analysis and evaluation set of tests which confirmed linear growth of acceleration.

This thesis focuses on investigating and creating an application for filtering user-generated content. The method was to examine how profanity and racist expressions are used and manipulated to evade filtering processes in similar systems. Focus also went on to study different algorithms to get this process to be quick and efficient, i.e., to process as many names in the shortest amount of time possible. This is because the client needs to filter millions of new uploads every day. The result shows that the application detects profanity and manipulated profanity. Data from the customer’s database was also used for testing purposes, and the result showed that the application also works in practice. The performance test shows that the application has a fast execution time. We could see this by approximating it to a linear func-tion with respect to time and the number of names entered. The conclusion was that the filter works and discovers profanity not detected earlier. Future updates to strengthen the decision process could be to introduce a third-party service, or a web interface where you can manually control decisions. Execution time is good and shows that 10 million names can be pro-cessed in about 6 hours. In the future, one can parallelize queries to the database so that multiple names can be processed simultaneously. / Denna avhandling fokuserar på att utreda och skapa en applikation för filtrering av användargenererat innehåll. Metoden gick ut på att undersöka hur svordomar samt rasistiska uttryck används och manipuleras för att undgå filtrerings processer i liknande system. Fokus gick även ut på att studera olika algoritmer för att få denna process att vara snabb och effektiv, dvs kunna bearbeta så många namn på kortast möjliga tid. Detta beror på att kunden i detta sammanhang får in miljontals nya uppladdningar varje dag, som måste filtreras innan använding. Resultatet visar att applikationen upptäcker svordomar i olika former. Data från kundens databas användes också för test syfte, och resultatet visade att applikationen även fungerar i praktiken. Prestanda testet visar att applikationen har en snabb exekveringstid. Detta kunde vi se genom att estimera den till en linjär funktion med hänsyn till tid och antal namn som matats in. Slutsatsen blev att filtret fungerar och upptäcker svordomar som inte upptäckts tidigare i kundens databas. För att stärka besluten i processen kan man i framtida uppdateringar införa tredje parts tjänster, eller ett web interface där man manuelt kan styra beslut. Exekverings tiden är bra och visar att 10 miljoner namn kan bearbetas på cirka 6 timmar. I framtiden kan man parallellisera förfrågningarna till databasen så att flera namn kan bearbetas samtidigt.