I detta arbete undersöks det hur en NoSQL-databas presterar jämfört med en relationsdatabas när kommunikation sker med en webbapplikation. Testning sker med hjälp av en PHP-applikation och Ajax för att simulera användningen av en webbapplikation. Den data som kommer lagras i databaserna kommer vara strukturerad och databaserna kommer vara på ett odistribuerat system. Metoden är teknikorienterade experiment men för framtida arbeten kan dessa tester utföras som en fallstudie för att ytterligare simulera en skarp användning av en webbapplikation.I detta arbete förklaras anledningen till framtagningen av NoSQL-databaser. Saker som diskussionen om att NoSQL kommer ta över platsen som de mest använda databaserna från relationsdatabaser tas också upp. Förhoppningsvis kan detta arbete ge viss insikt till den diskussionen.Resultatet av detta arbete visar att NoSQL kan passa bra för databaser som har en last som ärskriv- och uppdateringstung, men också att relationsdatabaser fortfarande passar bra i många fall.

This master thesis deals with designing and implementing mobile application dedicated for Android smartphone devices. The application will help tourists in Bratislava to better navigate between main points of interest. An analysis of internal as well as external environment precedes the design process and builds a solid base of functional, cybersecure and graphic requirements to which the solution needs to adhere to.

This thesis deals with the creating, visualization and processing data in a military environment. The task is to design and implement a system that enables the creation, visualization and processing ESM data. The result of this work is a ESMBD application that allows using a classical approach, which is a relational database, and BigData technologies for data storage and manipulation. The comparison of data processing speed while using the classic approach (Postgres database) and BigData technologies (Cassandra databases and Hadoop) has been carried out as well.

The diploma thesis deals with the problem of automated searching for reviews on web pages and also the saving of found reviews. In this work are described in detail possibilities of storing unstructured data and subsequent selection of the most suitable storage. The main part of the work deals with the analysis of HTML structure, so that it is possible to find the required information on the website. This work also deals with ways to determine the similarity of text strings in order to determine what product the review found belongs to. The Python programming language was used for implementation.

Context: Data is at the heart of any information system. Choosing the appropriate database and its operation is a major decision for any company and choosing from the pool of different options can can feel overwhelming. In this thesis we take a look at the main factors to consider when making your decision, to help you with the whole process. This thesis will explore the selection, prioritization and considerations when choosing a database. It is aimed at exploring the recent uptake and impact of NoSQL in companies and analyze the results of the literature and empirical study. Aim and Objectives: Our aim is to investigate the recent uptakeand continued use of NoSQL databases in software development companies. It is imperative to know how companies are choosing to adopt the right technology for their application. The objective is to provide instructions for companies on how to choose the right DB for their needs and what to consider. Method: Interviews are conducted to find out the process/approach that practitioners employ when choosing the database technology. Then an analysis of the considerations and their priority is conducted using a questionnaire. The focus is on the considerations, meaning factors to consider when choosing a database. Results: The result of the interviews show that infrastructure is the most essential consideration when choosing a DB, and the survey questionnaire show that consistency is the most essential consideration. Conclusions: The result suggests that there are several essential considerations when choosing a database. Furthermore, we conclude that the challenges of adopting NoSQL technology may be the following: only provide eventual consistency, which can impact availability and performance, reliability, the challenges of transitioning, keeping track, lacking data integrity, handling of complex queries, and security and privacy risks.

Meepo AB was investigating the possibility of developing a social rating and recommendation service. In a recommendation service, the user ratings are collected in a database, this data is then used in recommendation algorithms to create individual user recommendations. The purpose of this study was to find out which  demands are put on a DBMS, database management system, powering a recommendation service, what impact the NoSQL databases have on the performance of recommendation services compared to traditional relational databases, and which DBMS is most suited for storing the data needed to host a recommendation service. Five distinct NoSQL and Relational DBMS were examined, from these three candidates were chosen for a closer comparison. Following a study of recommendation algorithms and services, a test suite was created to compare DBMS performance in different areas using a data set of 100 million ratings. The results show that MongoDB had the best performance in most use cases, while Neo4j and MySQL struggled with queries spanning the whole data set. This paper however never compared performance for real production code. To get a better comparison, more research is needed. We recommend new performance tests for MongoDB and Neo4j using implementations of recommendation algorithms, a larger data set, and more powerful hardware. / Meepo AB undersökte möjligheten att utveckla en social betygs- och rekommendationstjänst. I en rekommendationstjänst samlas användarbetyg i en databas, för att sedan användas i en rekommendationsalgoritm för att skapa individuella rekommendationer till användarna. Syftet med studien var att ta reda på vilka krav som ställs på ett DBMS, databassystem, som driver en rekommendationstjänst, vilken inverkan NoSQL-databaser har på prestandan för rekommendationstjänster jämfört med traditionella relationsdatabaser och vilket DBMS som är mest lämpat för användning i en rekommendation tjänst. Fem olika NoSQL- och Relationsdatabaser undersöktes, från dessa valdes tre kandidater ut för en närmare jämförelse. Efter en studie i rekommendationsalgoritmer och rekommendationstjänster skapades en testsvit för att jämföra databasernas prestanda i olika områden. Till detta användes ett dataset med 100 miljoner betyg. Resultaten visar att MongoDB hade bäst prestanda i flest användningsfall, medan Neo4j och MySQL hade problem med sökningar som sträcker sig över hela datasetet. I denna uppsats jämförs dock inte prestandan med riktig produktionskod. För en bättre jämförelse behövs mer forskning. Vi rekommenderar nya prestandamätningar för MongoDB och Neo4j med implementationer av rekommendationsalgoritmer, ett större dataset och mer kraftfull hårdvara.

Background. Security Information and Event Management (SIEM) systems today are sophisticated sets of software packages combined with hardware platforms, which can perform real-time analysis on security events and can respond to them before potential damage due to the actions of intruders. A huge number of systems rely on the continuous transmission of data through computer networks. Nowadays it is difficult to imagine a sphere of human activity that would not be affected by information technologies and would not use computer networks. Along with the means of protecting information, the technologies that are used by cybercriminals to achieve their goals are also improving. Moreover, the so-called insiders - information security perpetrators inside the protected perimeter, who can cause much more damage by their actions, as they are among the legitimate users and can have access to more confidential information - are becoming a growing threat. Objectives. To identify insider activity within an acceptable time inside the network, we need to develop a methodology to detect abnormal activity within the network using advanced data processing techniques, based on machine learning. After recreating the data processing system, we will also need to determine the most efficient algorithm that can be applied to the task of insider detection. Methods. The work analyzed research papers with similar objectives to investigate methods and technologies for securing against intruder intrusions, in conjunction with a study of machine learning techniques for detecting anomalies in the data. Experimental data were also collected containing information about network activity within the same network over two weeks. With this data, it is possible to conduct an experiment in network traffic processing using state-of-the-art technology. Results. During the study of relevant works, several effective ways to detect anomalies in the data were identified, technologies for processing large amounts of data using NoSQL were studied, and work on creating an experimental bench was performed. As a result, the experimental data obtained was sufficient to verify the effectiveness of the obtained solution. Conclusions. As a result, we analyzed existing approaches to detect insider activity within a computer system. Algorithms based on machine learning and big data processing methods were evaluated. In addition, a model for representing big data in NoSQL format was developed, which made it possible to create an architecture of a system for detecting insiders in computer networks using a graph database and machine learning methods.

Context: This study compares the popularity and the performance of two DBMS. The two systems are SQL and NoSQL. The objective of the study is to determine which DBMS junior developers should learn first, in order to provide a head-start to their future career. Methods: To determine the most popular DBMS, surveys are collected from the Internet and are meta-analyzed. In order to determine the best performing DBMS, a SLR method that leads to a meta-analysis is conducted and tests the execution time of the read operation. Results: The research findings suggest that SQL is a more popular DBMS than the NoSQL system. This is verified statistically through the Fisher-Freeman-Halton Test, p <.001. As far as performance goes, the SQL DMBS performs a bit better compared to the NoSQL system if descriptive statistics is considered for 100 (M=12.4, SD=19.11),(M=174.4, SD=284.6) and 1000 (M=50.77, SD=113.5), (M=228.8, SD=276.6) records. However, once the t-test is performed it reveals that there is no statistical significance. Thus, the statistical test suggests that both DBMS perform equally well for both 100 and 1000 records t (8) = 1.27, p = .24 and t (8) = 1.11, p = .3 with a small effect size Cohen’sd, (d1=0.27) and (d2=0.28) respectively. Conclusion: Based on our research results and accounting for the importance of the date that this study has been conducted (2021), we recommend that junior developers should focus on learning a SQL DBMS first as their primary backend skillset for the foreseeablefuture.

In der vorliegenden Arbeit werden die Datenbankmanagementsysteme MongoDB, ScyllaDB, OrientDB, Aerospike und Redis mit dem Yahoo! Cloud Serving Benchmark unter der Verwendung von SSD-Speicher getestet. Dazu werden zuerst die verschiedenen NoSQL-Systemtypen beschrieben. Besonderheiten von SSD-Speicher werden zusammengefasst. Anschließend werden Besonderheiten der ausgewählten Datenbankmanagementsystemen und des Yahoo! Cloud Serving Benchmarks beschrieben, um die durchzuführenden Benchmarks zu planen. Weiterhin wird die verwendete Hardware beschrieben, um eine Replikation dieser Benchmarks zu ermöglichen und ein besseres Bild der zu messenden Performance zu bieten. Nach der Planung der Durchführung der Benchmarks, werden die verschiedenen Datenbankmanagementsysteme auf der oberen Grenze getestet, welche die gegebene Hardware bietet. Mit den Ergebnissen dieser werden weitere Benchmarks unter diversen Bedingungen geplant und durchgeführt. Die Ergebnisse werden jeweils ausgewertet und in dieser Arbeit eingebunden. Diese sind von den gegebenen Umständen stark beeinflusst, sodass allgemeingültige Aussagen nicht möglich sind. Zuletzt wird im Ausblick, welche inhaltliche Lücken und Fragen offen stehen oder weitere zusammenhängende Problemstellungen beschrieben.:1 Einleitung 1.1 Motivation 1.2 Vorgehensweise 2 Gegenstand des Benchmarks 2.1 Modell 2.1.1 Key-Value Store 2.1.2 Document Store 2.1.3 Wide-Column Store 2.1.4 Graph Store 2.1.5 Multi-Model 2.2 Medium 2.2.1 SSD 2.2.2 In-Memory 3 Technische Randbedingungen des Benchmarks 3.1 Ausgewählte Datenbankmanagementsysteme 3.2 Yahoo! Cloud Serving Benchmark 3.3 Genutzte Hardware 3.4 Testlauf des Benchmarks 3.5 Erzielter Vergleich 4 Erste Testreihe: 150GB 21 4.1 Aufgetretene Probleme 4.2 Verwendete Einstellungen 4.3 Ergebnisse: erster Versuch 4.4 Ergebnisse: 150GB 5 Testreihen: Übergreifende Szenarien 5.1 Testreihe 50GB 5.2 Testreihe 10GB 5.3 Testreihe Großes Feld 5.4 Testreihe Sekundärindex 5.5 Testreihe Latenz 5.6 Testreihe Discord 6 Ergebnisse DBMS-intern 6.1 MongoDB 6.2 ScyllaDB 6.3 OrientDB 6.4 Aerospike 6.5 Redis 7 Schlussteil 7.1 Auswertung 7.1.1 YCSB-Tool 7.1.2 MongoDB 7.1.3 ScyllaDB 7.1.4 Aerospike 7.1.5 OrientDB 7.1.6 Redis 7.1.7 SSD-Speicher 7.2 Zusammenfassung 7.3 Ausblick

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / There are currently several types of databases that have different ways of manipulating data that affects the performance of transactions when dealing with the information stored. And it is very important for companies to manage information fast, so they do not lose any operation because of a bad performance of a database, in the same way, they need to operate fast while keeping the integrity of the information. Likewise, every database category's purpose is to serve a specific or specifics use cases to perform fast to manage the information when needed, so in this paper, we study and analyze the SQL, NoSQL and In Memory databases to understand their fit uses cases and make performance tests to build a decision tree that can help to take the decision to choose what database category to use to maintain a good performance. The precision of the tests of relational databases was 96.26% in NoSQL databases was 91.83% and finally in IMDBS was 93.87%.