A Comparative Analysis of JSON Schema Inference Algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10457385" target="_blank" >RIV/00216208:11320/22:10457385 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.5220/0011046000003176" target="_blank" >https://doi.org/10.5220/0011046000003176</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0011046000003176" target="_blank" >10.5220/0011046000003176</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Comparative Analysis of JSON Schema Inference Algorithms

Popis výsledku v původním jazyce

NoSQL databases are becoming increasingly more popular due to their undeniable advantages in the context of storing and processing Big Data, mainly horizontal scalability and minimal requirement to define a schema upfront. In the absence of the explicit schema, however, an implicit schema inherent to the stored data still exists and it needs to be reverse engineered from the data. Once inferred, it is of a great value to the stake-holders and database maintainers. Nevertheless, the problem of schema inference is non-trivial and is still the subject of ongoing research. In this paper we provide a comparative analysis of five recent proposals of schema inference approaches targeting the JSON format. We provide both static and dynamic comparison of the approaches. In the former case we compare various features. In the latter case we involve both functional and performance analysis. Finally, we discuss remaining challenges and open problems.

Název v anglickém jazyce

A Comparative Analysis of JSON Schema Inference Algorithms

Popis výsledku anglicky

NoSQL databases are becoming increasingly more popular due to their undeniable advantages in the context of storing and processing Big Data, mainly horizontal scalability and minimal requirement to define a schema upfront. In the absence of the explicit schema, however, an implicit schema inherent to the stored data still exists and it needs to be reverse engineered from the data. Once inferred, it is of a great value to the stake-holders and database maintainers. Nevertheless, the problem of schema inference is non-trivial and is still the subject of ongoing research. In this paper we provide a comparative analysis of five recent proposals of schema inference approaches targeting the JSON format. We provide both static and dynamic comparison of the approaches. In the former case we compare various features. In the latter case we involve both functional and performance analysis. Finally, we discuss remaining challenges and open problems.

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

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

ENASE: PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING

ISBN

978-989-758-568-5

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

379-386

Název nakladatele

SCITEPRESS

Místo vydání

SETUBAL

Místo konání akce

Virtual Event

Datum konání akce

25. 4. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000814765400037