Efektivní správa a lokalizace předmětů je výzvou v menších firmách, které nedisponují profesionálním systémem pro správu inventáře. S rostoucím množstvím předmětů, zejména těch méně často používaných, se zvyšuje riziko jejich nesystematického ukládání a obtížného dohledání. Implementace digitálního nástroje pro správu položek by mohla pomoci menším podnikům nebo maloobchodním provozovnám, kde by bylo možné efektivně sledovat umístění produktů a usnadnit tak jejich vyhledávání. Cílem této práce je návrh a implementace aplikace umožňující uživatelům spravovat a vyhledávat umístění jejich položek. Systém bude zahrnovat uživatelskou autentizaci, správu knihovny sdílených objektů, editory tvarů, nábytku a podlah, které umožní uživatelům modelovat své prostory. Klíčovou funkcionalitou bude správa položek v rámci vytvořeného modelu, včetně jejich přidávání, přesouvání a vyhledávání. Výstupem práce bude funkční aplikace s intuitivním uživatelským rozhraním, která umožní efektivní organizaci předmětů v různých prostředích.

Personal cars drive through Czech and Slovak cities every day, yet their exterior remains an unused advertising surface that no local platform rents per kilometer driven. The thesis presents Ad-on-Wheels, a platform in which drivers earn per kilometer with an advertisement mounted and companies create and pay for campaigns themselves. The solution uses Spring Boot microservices, a Spring Cloud gateway, SwiftUI and React clients, Apache Cassandra for GPS samples, and MySQL for ride and earnings data.

Sportovní tanec je estetická a subjektivně hodnocená disciplína, v níž hraje zásadní roli lidský faktor rozhodování. Přestože existuje přesně definovaný systém hodnocení, tzv. skating systém, jeho výstupy jsou silně ovlivněny individuálními preferencemi porotců. Taneční veřejnost tak často vnímá hodnocení jako netransparentní a neprůhledné. Vzniká tak potřeba nástroje, který by umožnil přehlednou analýzu hodnocení a podpořil transparentnost celého systému. Cílem práce je navrhnout a implementovat webovou aplikaci umožňující uživatelům přístup k analýze a predikci soutěžních výsledků ve sportovním tanci. Aplikace bude postavena na modulární architektuře a rozdělena na frontendovou část pro interaktivní vizualizaci dat, backend poskytující REST API a výpočetní a sběrné moduly pro analýzu a získávání dat. Součástí řešení bude infrastruktura pro automatizované stahování a zpracování výsledků, například z webu Českého svazu tanečního sportu (ČSTS), jejich uložení do databáze a následná prezentace statistik a souvisejících informací uživatelům. Vedle analýzy historických dat bude aplikace také poskytovat funkcionalitu pro odhad možných výsledků budoucích soutěží na základě dostupných údajů, jako jsou předchozí výkony soutěžních párů, profily porotců nebo složení startovní listiny. Cílem této části je nabídnout uživatelům nástroj pro prediktivní pohled na soutěže. Celý systém bude navržen s důrazem na přehlednost, efektivitu a budoucí rozšiřitelnost.

This thesis presents a benchmarking system to compare the performance of six Java ORM frameworks, namely JDBC, Spring Data JPA, Ebean, jOOQ, MyBatis, and Cayenne, over a relational database system MySQL using a microservices architecture. The system executes a diverse set of SQL queries, partially taken from the TPC-H benchmark, and collects metrics such as execution time and memory usage. A web interface allows users to configure experiments and visualize results. The software solution is modular, extensible and allows ORM selection decisions to be made based on data in real-valued applications.

Redundancy is a common feature of large and heterogeneous datasets, often arising from data integration, denormalization, or performance-oriented design. It can take various forms, such as partial duplicates, repeated rows, or overlapping attributes. Rather than being an issue to eliminate outright, redundancy can support faster access patterns, provide fault tolerance, or offer alternative paths for interpreting data. To take advantage of these benefits, it is essential to detect and understand redundancy in a structured and explainable way. This bachelor’s thesis introduces an interactive tool for detecting redundant rows in CSV files. It relies on data profiling methods such as identifier detection, reference analysis, and functional dependency discovery to find and describe different types of redundancy. The developed prototype offers users an intuitive interface for exploring, checking, and interpreting redundancy.

In the world we can encounter many optimization techniques for querying over relational data, but none for multi-model data. To create one, we need to get an adequate dataset over which to train the model. As is the case with the model itself, a suitable dataset has not yet been created. Thus, in this thesis we will focus on creating a tool that can be used to create a dataset that would enable the creation of a machine learning based optimization tool. First, we will analyze the tools that are currently available and allow the collection of statistical metadata from database systems. We then select suitable parameters that would be good to include in the resulting dataset. Finally, we create and describe a tool that automates the whole process of creating the dataset.

The rise of Big Data has highlighted the limitations of relational databases while handling large datasets, leading to the growth of NoSQL databases. This has made DBMS benchmarking crucial for performance evaluation and decision-making. This thesis compares relational (MySQL, SQLite), graph (Neo4j, ArangoDB), docu- ment (MongoDB), and column-family (Cassandra) databases. We analyze the expressive power of their query languages and their runtime efficiency across varying data sizes. We conclude, that there's no "number one" solution for all use cases. The choice depends on factors like data volume, query complexity, and the need for joins. For complex queries and frequent joins, MySQL and SQLite are the most expressive but may struggle with very large datasets. Cassandra and MongoDB excel in perfor- mance and scalability but require efficient schema design and targeted data redundancy. ArangoDB presents a versatile option capable of handling multiple data models but might require further investigation into its performance compared to Neo4j.

In today's landscape, with a multitude of available database systems, it is often hard to choose which one would fit our needs best. In this thesis we focus on choosing a performant database system from a choice between PostgreSQL, Virtuoso, OrientDB, ScyllaDB, Couchbase, and RavenDB. We compare the static properties and features of said database systems, and we include a brief discussion on data extraction and trans- formation, for which we developed a helper library for Python. We then assess dataset import times into each database system. To determine the most efficient database, we measure query performance across multiple dataset sizes, and finally we offer a recom- mendation based on the results, and discuss further possible considerations.

With the increasing importance of (multi-model) NoSQL systems without or with partial schema, which would precisely define each attribute in the records, is an option to reverse infer this schema from the data needed for plenty of applications. Project MM-infer created earlier as a master thesis enables to extract this schema. This project however suffers from faults in performance which limited its usability mainly on large and complex data. This thesis continues in MM-infer project and optimizes primaly its speed. It also fixes some bugs, which were inevitably created during its creation. At last it increases precision of the inferred schema by adding new heuristics which identify main elements in the schema from one of its algorithms.

Querying over multi-model data is a challenging task even for expert users, as they typically need to master a number of query languages and be aware of the logical repre- sentation of the data. In this thesis, we propose a graphical query language over multi-model data and im- plement it in the form of a prototype application. The proposed query language primarily targets less experienced users, aiming at simple querying over data with only knowledge of its structure. The work includes an attached prototype that represents the data using a categorical representation strikingly similar to a graph. We take advantage of this simi- larity and therefore store the data in the Neo4j graph database. For proof of concept, we translate our proposed language into Cypher and transitively query over the multi-model data stored using the categorical representation in Neo4j.

Multi-Model database systems combine the advantages of traditional and NoSQL database systems. However, the management of these systems is challenging, as users have to design an appropriate storage strategy for their data. One of the most influential factors in the storage strategy is the selection of indexes. Indexes can significantly improve query performance, but they require additional storage space and maintenance overhead. Index selection problem is well-studied in the context of single-model Database Management Systems (DBMSs), but there is a lack of research in the context of multi-model database systems. We address this problem by conducting a survey of current state-of-the-art index selection algorithms and evaluating their applicability to other DBMSs. The results reveal the strengths and weaknesses of existing algorithms and highlight the need for specialized algorithms for multi-model database systems. Moreover, we formulate open questions and suggest future research directions in this field. Our research provides a foundation for the development of efficient index selection algorithms for multi-model DBMSs.

The aim of the bachelor's thesis is to create a tool that supports modeling in the UML and the newly created categorical model (CAT), as well as transla- tion from UML model to CAT model. Thesis contains basic description of UML and CAT, research of popular tools for modeling, programming and user docu- mentation. The appendices contains tests, design pattern and sequence diagrams of selected methods. The outcome of the thesis is functional, user friendly tool available on the Windows operating system. The advantages of the tool are for ex- ample possibility to change grafic properties of objects, save and load file in JSON format or open multiple canvases in one tool window.