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A tool for conceptual modelling of multi-model data
Hvizdoš, Richard ; Koupil, Pavel (advisor) ; Stenchlák, Štěpán (referee)
A tool for conceptual modelling of multi-model data Abstract Richard Hvizdoš 8 May 2023 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. 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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A tool for querying multi-model data
Bakhtin, Artem ; Koupil, Pavel (advisor) ; Bártík, Jáchym (referee)
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. 1
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A Universal Approach for Anomaly Detection in Log Files
Tomala, Radovan ; Koupil, Pavel (advisor) ; Pilát, Martin (referee)
The goal of this thesis is to propose a solution for universal anomaly detection in log files. This thesis first provides theoretical background and overview of related work. Se- lected approaches are then extensively evaluated on multiple data sets. Based on results of evaluation, solution prototype is proposed. This prototype consists of modules respon- sible for detecting different anomaly types. To be specific, anomalous error sequences, anomalous occurrence of log parameters and network topology change can be detected. The error sequence detector integrates selected existing approaches and parameter de- tector utilizes own method based on log parsing and parameter count vector creation. Furthermore, the network topology change detector implements novel minimum span- ning tree based algorithm. Finally, improved log parser that is able to parse logs from different systems and formats is proposed to ensure universality across systems. 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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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 NoSQL Databases
Veinhardt Latták, Ivan ; Koupil, Pavel (advisor) ; Svoboda, Martin (referee)
NoSQL databases are becoming increasingly more popular due to their undeniable advantages in the context of storing and processing big data, mainly horizontal scala- bility and the lack of a requirement to define a data schema upfront. In the absence of explicit schema, however, an implicit schema inherent to the stored data still exists and can be inferred. Once inferred, a schema is of great value to the stakeholders and database maintainers. Nevertheless, the problem of schema inference is non-trivial and is still the subject of ongoing research. We explore the many aspects of NoSQL schema inference and data modeling, analyze a number of existing schema inference solutions in terms of their inner workings and capabilities, point out their shortcomings, and devise (1) a novel horizontally scalable approach based on the Apache Spark platform and (2) a new NoSQL Schema metamodel capable of modeling i.a. inter-entity referential relation- ships and deeply nested JSON constructs. We then experimentally evaluate the newly designed approach along with the preexisting solutions with respect to their functional and performance capabilities. 1
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