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Experimental Analysis of Query Languages in Modern Database Systems
Čorovčák, Martin ; Koupil, Pavel (advisor) ; Holubová, Irena (referee)
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.
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Mental health monitoring platform
Trefil, Patrik ; Škoda, Petr (advisor) ; Holubová, Irena (referee)
The National Institute of Mental Health needed to digitalize its research and therapeu- tic practice. One of the processes was the collaboration of therapists with patients/clients through questionnaires. In this work, we conducted analysis, design, and implementation of a web application digitalizing this process. The application enables the creation and processing of questionnaires. This functionality can also be used for data collection for research purposes. Among the primary qualitative requirements of the institute were the extensibility of the application to include other forms of collaboration and the ability to run the application on its own infrastructure. These requirements were addressed by appropriately dividing the application into components and using Docker for easy de- ployment. The application was successfully handed over to the institute for deployment. 1
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An application of AI methods for refining the storage strategy in multi-model database systems: A survey
Miháľ, Filip ; Koupil, Pavel (advisor) ; Holubová, Irena (referee)
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. 1
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Unified Querying of Multi-Model Data
Crha, Daniel ; Holubová, Irena (advisor) ; Pokorný, Jaroslav (referee)
The vast majority of current multi-model querying solutions require the user to have intimate knowledge of the specific models involved. There exists a single approach for truly unified multi-model querying, but this approach is not practically usable for most users due to its complexity. In this thesis we present MMQL, a multi-model query lan- guage based on category theory, which was designed using SPARQL as a basis. Using MMQL, users can formulate multi-model, multi-database queries without needing to know about the way the data is stored. We also present our proposal for the implemen- tation of MMQL, including the required supporting algorithms. To verify the validity of our proposal, we built the proof-of-concept tool MM-quecat, an implementation of basic MMQL concepts. We then evaluated MM-quecat in a scenario involving PostgreSQL and MongoDB, querying both databases with a single MMQL query. As we present one of the first ever approaches for unified multi-model querying, we also analyze the weak- nesses and limitations of the proposed approach, opening the door for future iterations and improvements. 1
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Modelling and Management of Multi-Model Data
Koupil, Pavel ; Holubová, Irena (advisor) ; Klettke, Meike (referee) ; Krátký, Michal (referee)
Title: Modelling and Management of Multi-Model Data Author: Pavel Koupil (Čontoš) Department: Department of Software Engineering Supervisor: doc. RNDr. Irena Holubová, Ph.D., Department of Software Engi- neering Abstract: With the advent of multi-model database management systems, the boundaries of many approaches to data processing were pushed. The aspect of multi-model data introduces a new dimension of complexity and new chal- lenges not seen in single-model systems. We have to address issues arising from the combination of interconnected and often contradictory logical models, such as, e.g., order-preserving/-ignorant, aggregate-oriented/-ignorant, schema-full/- less/-mixed approaches, intra- and inter-model references, intra- and inter-model integrity constraints, or full and partial intra- and inter-model data redundancy. Hence, a number of mature and verified approaches for various data manage- ment tasks commonly used for single-model DBMSs cannot be directly applied to multi-model DBMSs. This thesis aims to propose a new family of unified approaches for both conceptual and logical multi-model modelling and data management. We first analyse the state-of-the-art of related areas. Then we propose abstract data structures to represent multi-model schema and data. These structures are then utilised...
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Evolution Management in Multi-Model Databases
Bártík, Jáchym ; Holubová, Irena (advisor) ; Nečaský, Martin (referee)
Multi-model databases allow us to combine the advantages of various data models by storing different types of data in different models. However, this technology is still relatively immature, lacks standardization, and there are not any tools that would allow us to model multi-model data or manage their evolution. This thesis (i) provides an in- troduction to MM-cat, i.e., the framework for modeling multi-model data, (ii) describes the implementation of the framework, (iii) designs a workflow and a set of schema modi- fication operations to facilitate evolution management and (iv) performs experiments to prove their reliability and scalability. 1
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Model-driven approach for data schema definitions modeling
Stenchlák, Štěpán ; Nečaský, Martin (advisor) ; Holubová, Irena (referee)
This work analyzes, formalizes, and implements a framework for multi-level concep- tual modeling of various serialization formats based on Model-Driven Architecture and previously developed tools XCase and eXolutio. It enables users to model their schema from a conceptual model in one general form from which multiple schema formats can be derived alongside documentation and transformation scripts. The thesis introduces base formalisms and findings and analyzes advanced requirements for the following work in this area, such as evolution and inheritance of schemas. The primary use case of the tool is modeling formal open standards for publishing open data for the government and public institutions of the Czech Republic. Nevertheless, the intent is to make the tool for general schema modeling. 1
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Comparative Analysis of Multi-model Databases
Guliyev, Eldar ; Holubová, Irena (advisor) ; Koupil, Pavel (referee)
BACHELOR THESIS ABSTRACT Eldar Guliyev Comparative Analysis of Multi-model Databases The thesis is devoted to performance analysis of multi-model database management systems. Data models, multi-model DBMS and query languages were studied. Based on comparison of existing database benchmarks and multi-model DBMS functionality, requirements to the benchmarking process were identified. For the performance benchmarking, a cross-platform benchmarking application with graphical user interface was designed and implemented. The benchmarking application has a plugin architecture giving the possibility to create a DLL-plugin and test a DBMS which is not supported in the initial release. ArangoDB, RavenDB and MongoDB were tested with focus on document and graph data models.
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Schema Inference for Multi-model Data
Hricko, Sebastián ; Holubová, Irena (advisor) ; Kopecký, Michal (referee)
In recent years, multi-model databases have become very popular as the individual models better suit the different domains, use cases or scenarios. NoSQL databases are an integral part of the multi-model world of big and variable datasets. While the usage of these databases is relatively simple and functional, in some cases, we lack insight into the structure of the data and the possible interconnection between the data in various databases and different models. This thesis presents a novel approach that generates a schema of the multi-model data concerning the undeclared relationships between the models. Firstly we analyse the existing single-model approaches and point out the main flaws. We then propose the universal multi-model approach and implement it as a proof of concept. 1
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XML Schema Evolution
Malý, Jakub ; Holubová, Irena (advisor) ; Klímek, Jakub (referee)
In the presented work we study the XML data evolution, reasons and consequences of XML schema evolution in particular. The thesis contains a survey of the existing approaches to this problem. The approach presented in this work extends the XSem conceptual model with the support for multiple versions of the model. Thanks to this extension, it is possible to define a set of changes between two versions of a schema. The thesis contains a description of an algorithm that compares two versions of a schema and produces a revalidation script in XSL.
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