There has been a growth of a class of databases known as the Not only SQL (NoSQL) databases in recent years. Its quick growth has been fueled by a high demand by businesses as it offers a convenient way to store data and is significantly different from our traditional relational databases. It is easy to process unstructured data, offers a cloud-friendly ap- proach and grows through the distribution of data over lots of commodity computers. Most of these NoSQL databases are distributed in several different locations, spanning countries and are known as geo-distributed cloud datastores. We work to customize one of these known as Cassandra. Given the size of the database and the size of applications accessing the data stored, it has been challenging to customize it to meet existing application Service Level Agreement (SLAs). We live in an era of data breaches and even though some types of information are stripped of all sensitive data, there are ways to easily identify and link it to data of real persons or government. Data saved in different countries are subject to the rules and regulations of that specific country and security measures employed to safeguard consumer data. In this thesis, we describe mechanisms for selectively replicating data in a large scale NoSQL datastore in respect of privacy and legal regulations. We introduce an easily extensible constraint language to implement these policy constraints through the creation of a pluggable topology provider in the configuration files of Cassandra. Experiments using the modified Cassandra trunk demonstrate that our techniques work well, respect response times and improves read and write latencies.

Goal of this master thesis is to analyze existing algorithms for ensuring consistency in real-time synchronization of vector graphics between server and multiple clients, testing them, describe main differences and selection of the best for this purpose. The theoretical part is devoted to realtime comunication over HTTP, to WebSockets, to concurrency algorithms and to basics of application architecture. The practical part describes the technology used in this thesis, as well as actual application design a its implementation. The result of this thesis is an overview of concurrency algorithms, the choise of technology for real-time communication between server and multiple clients and implementation of selected technologies and algorithms in Node.js application.

This diploma thesis is about graph databases, which belong to the category of database systems known as NoSQL databases, but graph databases are beyond NoSQL databases. Graph databases are useful in many cases thanks to native storing of interconnections between data, which brings advantageous properties in comparison with traditional relational database system, especially in querying. The main goal of the thesis is: to describe principles, properties and advantages of graph database; to design own convenient graph database use case and to realize the template verifying designed use case. The theoretical part focuses on the description of properties and principles of the graph database which are then compared with relational database approach. Next part dedicates analysis and explanation of the most typical use cases of the graph database including the unsuitable use cases. The last part of thesis contains analysis of own graph database use case in which several principles are defined. The principles can be applied separately. There are crucial analytical operations in the use case. The analytical operations search the causes with their rate of influence on amount or change in the value of the indicator. This part also includes the realization of the template verifying the use case in the graph database. The template consists of the database structure design, the concrete database data and analytical operations. In the end the returned results from graph database are verified by the alternative calculations without using the graph database.

In this thesis, I want to explore present (y. 2012/2013) modern trends in Business Intelligence and focus specifically on the rapidly evolving and, in my (and not only) opinion, a very perspective area of analysis and use of Big Data in large enterprises. The first, introductory part contains general information and the formal conditions as aims of the work, on whom the work is oriented and where it could be used. Then there are described inputs and outputs, structure, methods to achieve the objectives, potential benefits and limitations in this part. Because at the same time I work as a data analyst in the largest bank Czech Republic, Czech Savings Bank, I focused on the using of Big Data in the banking, because I think, that it is possible to achieve great benefits from collecting and analyzing Big Data in this area. The thesis itself is divided into 3 parts (chapters 2, 3-4, 5). In the second chapter you will learn, how developed the area of BI, how it evolved historically, what is BI today and what future is predicted to the BI by the experts like the world famous and respected analyst firm Gartner. In the third chapter I will focus on Big Data itself, what this term means, how Big Data differs from traditional business information available from ERP, ECM, DMS and other enterprise systems. You will learn about ways to store and process this type of data, as well as about the existing and applicable technologies, focused on Big Data analysis. In the fourth chapter I focus on the using of Big Data in business, information in this chapter will reflect my personal views on the potential of Big Data, based on my experience during practice in Czech Savings Bank. The final part will summarize this thesis, assess, how I fulfilled the objectives defined at the beginning, and express my opinion on perspective of the trend of Big Data analytics, based to the analyzed during the writing this thesis information and knowledge.

Arbetet jämför fyra olika NoSQL-databaser med fokus på tidseffektivitet. De fyra databaserna är MongoDB, RavenDB, ArangoDB och Couchbase. Studien består av en benchmark för att mäta tidseffektiviteten av de fyra databaserna och en litteraturstudie av hur tidseffektiviteten påverkas av optimeringslösningar. Tillsammans bidrar dessa metoder till en slutsats från båda perspektiven då de kompletterar varandra och ger en grund för resultatets betydelse. Arbetets grund ligger i ett tidigare examensarbete som går ut på att jämföra en SQL-databas mot en NoSQL-databas med en benchmark. Resultatet av studien visar att för de flesta databaser så ökar söktiden för en query i korrelation med ökningen av query-komplexiteten, och att tidseffektiviteten mellan de olika databaserna varierar vid sökningar med hög komplexitet. Framtida arbeten som kan baseras på denna studie är att göra en liknande benchmark på ett dataset som är större eller att en annan typ av databas används.

Den mänskliga klimatpåverkan i Sverige påstås vara i genomsnitt åtta ton per person årligen, vilket är långt ifrån Parisavtalets mål som innebär att den genomsnittliga globala utsläppen bör som högst vara ett ton per person. Under år 2017 uppskattades svenskarnas klimatpåverkan från klädinköp till 4,2 miljoner ton koldioxidekvivalenter sammanlagt under plaggets livslängd. Genom att fördubbla denna livslängd med hjälp av återanvändning skulle klimatpåverkan kunna minskas till nästan hälften, där ett sätt att återanvända är att handla begagnat. Många kan däremot uppleva en stor osäkerhet i detta då varor kan vara piratkopierade.  Syftet med detta projekt är därmed att bidra till tryggare begagnathandel som är både miljövänlig och plånboksvänlig samt förminska handel med piratkopiering. Målet i projektet  är en applikation som skall kunna identifiera märkesvarors äkthet med hjälp av QR-koder och en därtill kopplad databas.  Resultatet av arbetet är en applikation, Safe Second, som kan identifiera märkesvaror samt miljömärkta varor. Applikationen är tillgänglig för Androidenheter där Android Studio är plattformen som har använts för implementation av både frontend och backend. Ingående i backend är databasen där information lagras samt upphämtas, denna är skapad med Firebase Realtime Database. I applikationen har en funktion för lagerstatus även blivit implementerad och uppdateras i realtid, i syfte att kunna underlätta samt öka intresset kring handel i fysiska second hand-butiker, vilket antas kommer att fungera utifrån 93,4% av testpersoner i en enkätundersökning. / The human climate impact in Sweden is claimed to be an average of eight tons per person annually, far from the Paris Agreement's goal, which means that the average global emissions should be a maximum of one ton per person. In 2017, the Swedes' climate impact from clothing purchases was estimated at 4,2 million tons of carbon dioxide equivalents in total during the garment's lifespan. By doubling this lifespan with the help of reuse, the climate impact could be reduced to almost half, where one way of reuse is to shop second-hand. Many, on the other hand, may experience great uncertainty in this as goods can be pirated. The purpose of this project is thus to contribute to safer second-hand trade that is both environmentally friendly and wallet-friendly and to reduce trade in piracy. The goal of the project is an application that will be able to identify the authenticity of branded goods with the help of QR codes and a database attached to it. The result of the work is an application, Safe Second, which can identify branded goods and eco-labeled goods. The application is available for Android devices, where Android Studio is the platform that has been used for the implementation of both frontend and backend. The backend includes the database where information is stored and retrieved; this is created with Firebase Realtime Database. In the application, a function for stock status has been implemented and updated in real-time to facilitate and increase interest in trading in physical second-hand stores, which is assumed to work based on 93,4% of test subjects in a survey.

This thesis deals with NoSQL databases, which can be used for effective storage of sensors data with character of time series. The aim is to design and implement own solution for database designed to store time series data, with usage of NoSQL.

This thesis focuses on system for persistence of GPON communication. Theoretical part deals with problems of GPON and NG-PON optical networks, NoSQL database systems and MongoDB database. Practical part contains design of a database schema for MongoDB database and source code in programming languages Python and C# for working with this database. The thesis is finalized by performance testing, proving that the database design and source code implementation is capable of handling real world traffic.

Introduction: Nowadays, the data generated by global communication systems is enormously increasing.  There is a need by Telecommunication Industries to monitor and manage this data generation efficiently. Apache Cassandra is a NoSQL database that manages any formatted data and a massive amount of data flow efficiently.  Aim: This project is focused on developing a new random compaction strategy and evaluating this random compaction strategy's performance. In this study, limitations of generic compaction strategies Size Tiered Compaction Strategy and Leveled Compaction Strategy will be investigated. A new random compaction strategy will be developed to address the limitations of the generic Compaction Strategies. Important performance metrics required for the evaluation of the strategy will be studied. Method: In this study, a grey literature review is done to understand the working of Apache Cassandra, different compaction strategies' APIs. A random compaction strategy is developed in two phases of development. A testing environment is created consisting of a 4-node cluster and a simulator. Evaluated the performance by stress-testing the cluster using different workloads. Conclusions: A stable RCS artifact is developed. This artifact also includes the support of generating random threshold from any user-defined distribution. Currently, only Uniform, Geometric, and Poisson distributions are supported. The RCS-Uniform's performance is found to be better than both STCS and  LCS. The RCS-Poisson's performance is found to be not better than both STCS and LCS. The RCS-Geometric's performance is found to be better than STCS.

Background: Cassandra is a NoSQL database, where the data in the background is stored in the immutable tables which are called SSTables. These SSTables are subjected to a method called Compaction to reclaim the disk space and to improve READ performance. Size Tiered Compaction Strategy and Leveled Compaction Strategy are the most used generic compaction strategies for different use cases. Space Amplification and Write Amplification are the main limitations of the above compaction strategies, respectively. This research aims to address the limitations of existing generic compaction strategies. Objectives: A new random compaction strategy will be created to improve the efficiency and effectiveness of compaction. This newly created random compaction strategy will be evaluated by comparing the read, write and space amplification with the existing generic compaction strategies, for different use cases. Methods: In this study, Design Science has been used as a research method to answer both the research questions. Focus groups meetings have been conducted to gain knowledge on the limitations of existing compaction strategies, newly created random compaction strategy, and it’s appropriate solutions. During the evaluation, The metrics have been collected from Prometheus server and visualization is carried out in Grafana server. The compaction strategies are compared significantly by performing statistical tests. Results: The results in this study showed that the random compaction strategy is performing almost similar to Leveled Compaction Strategy. The Random Compaction Strategy solves the space amplification problem and write amplification problem in the Size Tiered Compaction Strategy and Leveled Compaction Strategy, respectively. In this section, eight important metrics have been analyzed for all three compaction strategies. Conclusions: The main artefact of this research is a new Random Compaction Strategy. After performing two iterations, a new stable random compaction strategy is designed. The results were analyzed by comparing the Size Tiered Compaction Strategy, Leveled Compaction Strategy and Random Compaction Strategy on two different use cases. The new random compaction strategy has performed great for Ericsson buffer management use case.