JSON Schema Inference Approaches

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10418922" target="_blank" >RIV/00216208:11320/20:10418922 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-030-65847-2_16" target="_blank" >https://doi.org/10.1007/978-3-030-65847-2_16</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-65847-2_16" target="_blank" >10.1007/978-3-030-65847-2_16</a>

Jazyk výsledku

angličtina

Název v původním jazyce

JSON Schema Inference Approaches

Popis výsledku v původním jazyce

Since the traditional relational database systems are not capable of following the contemporary requirements on Big Data processing, a family of NoSQL databases emerged. It is not an exception for such systems not to require an explicit schema for the data they store. Nevertheless, application developers must maintain at least the so-called implicit schema. In certain situations, however, the presence of an explicit schema is still necessary, and so it makes sense to propose methods capable of schema inference just from the structure of the available data. In the context of document NoSQL databases, namely those assuming the JSON data format, we focus on several representatives of the existing inference approaches and provide their thorough comparison. Although they are often based on similar principles, their features, support for the detection of references, union types, or required and optional properties differ greatly. We believe that without adequately tackling their disadvantages we identified, uniform schema inference and modeling of the multi-model data simply cannot be pursued straightforwardly.

Název v anglickém jazyce

JSON Schema Inference Approaches

Popis výsledku anglicky

Since the traditional relational database systems are not capable of following the contemporary requirements on Big Data processing, a family of NoSQL databases emerged. It is not an exception for such systems not to require an explicit schema for the data they store. Nevertheless, application developers must maintain at least the so-called implicit schema. In certain situations, however, the presence of an explicit schema is still necessary, and so it makes sense to propose methods capable of schema inference just from the structure of the available data. In the context of document NoSQL databases, namely those assuming the JSON data format, we focus on several representatives of the existing inference approaches and provide their thorough comparison. Although they are often based on similar principles, their features, support for the detection of references, union types, or required and optional properties differ greatly. We believe that without adequately tackling their disadvantages we identified, uniform schema inference and modeling of the multi-model data simply cannot be pursued straightforwardly.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA20-22276S" target="_blank" >GA20-22276S: Unifikovaná správa multi-model dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Conceptual Modeling

ISBN

978-3-030-65846-5

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

11

Strana od-do

173-183

Název nakladatele

Springer International Publishing

Místo vydání

Austria

Místo konání akce

Vienna, Austria

Datum konání akce

3. 11. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000604160100016