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
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:452107 |
| Date | January 2021 |
| Creators | Veinhardt Latták, Ivan |
| Contributors | Koupil, Pavel, Svoboda, Martin |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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