A universal approach for multi-model schema inference

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10448404" target="_blank" >RIV/00216208:11320/22:10448404 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Z102_n3FDd" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Z102_n3FDd</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s40537-022-00645-9" target="_blank" >10.1186/s40537-022-00645-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A universal approach for multi-model schema inference

Popis výsledku v původním jazyce

The variety feature of Big Data, represented by multi-model data, has brought a new dimension of complexity to all aspects of data management. The need to process a set of distinct but interlinked data models is a challenging task. In this paper, we focus on the problem of inference of a schema, i.e., the description of the structure of data. While several verified approaches exist in the single-model world, their application for multi-model data is not straightforward. We introduce an approach that ensures inference of a common schema of multi-model data capturing their specifics. It can infer local integrity constraints as well as intra- and inter-model references. Following the standard features of Big Data, it can cope with overlapping models, i.e., data redundancy, and it is designed to process efficiently significant amounts of data.To the best of our knowledge, ours is the first approach addressing schema inference in the world of multi-model databases.

Název v anglickém jazyce

A universal approach for multi-model schema inference

Popis výsledku anglicky

The variety feature of Big Data, represented by multi-model data, has brought a new dimension of complexity to all aspects of data management. The need to process a set of distinct but interlinked data models is a challenging task. In this paper, we focus on the problem of inference of a schema, i.e., the description of the structure of data. While several verified approaches exist in the single-model world, their application for multi-model data is not straightforward. We introduce an approach that ensures inference of a common schema of multi-model data capturing their specifics. It can infer local integrity constraints as well as intra- and inter-model references. Following the standard features of Big Data, it can cope with overlapping models, i.e., data redundancy, and it is designed to process efficiently significant amounts of data.To the best of our knowledge, ours is the first approach addressing schema inference in the world of multi-model databases.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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 periodika

Journal of Big Data

ISSN

2196-1115

e-ISSN

2196-1115

Svazek periodika

9

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

46

Strana od-do

1-46

Kód UT WoS článku

000839641800001

EID výsledku v databázi Scopus

2-s2.0-85135861976