Graph Pattern Index for Neo4j Graph Databases

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10395844" target="_blank" >RIV/00216208:11320/19:10395844 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-26636-3_4" target="_blank" >http://dx.doi.org/10.1007/978-3-030-26636-3_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-26636-3_4" target="_blank" >10.1007/978-3-030-26636-3_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Graph Pattern Index for Neo4j Graph Databases

Popis výsledku v původním jazyce

Nowadays graphs have become very popular in domains like social media analytics, healthcare, natural sciences, BI, networking, etc. Graph databases (GDB) allow simple and rapid retrieval of complex graph structures that are difficult to model in traditional information systems based on a relational DBMS. GDB are designed to exploit relationships in data, which means they can uncover patterns difficult to detect using traditional methods. We introduce a new method for indexing graph patterns within a GDB modelled as a labelled property graph. The index is based on so called graph pattern trees of variations and stored in the same database where the database graph. The method is implemented for Neo4j GDB engine and analysed on three graph datasets. It enables to create, use and update indexes that are used to speed-up the process of matching graph patterns. The paper provides details of the implementation, experiments, and a comparison between queries with and without using indexes.

Název v anglickém jazyce

Graph Pattern Index for Neo4j Graph Databases

Popis výsledku anglicky

Nowadays graphs have become very popular in domains like social media analytics, healthcare, natural sciences, BI, networking, etc. Graph databases (GDB) allow simple and rapid retrieval of complex graph structures that are difficult to model in traditional information systems based on a relational DBMS. GDB are designed to exploit relationships in data, which means they can uncover patterns difficult to detect using traditional methods. We introduce a new method for indexing graph patterns within a GDB modelled as a labelled property graph. The index is based on so called graph pattern trees of variations and stored in the same database where the database graph. The method is implemented for Neo4j GDB engine and analysed on three graph datasets. It enables to create, use and update indexes that are used to speed-up the process of matching graph patterns. The paper provides details of the implementation, experiments, and a comparison between queries with and without using indexes.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Data Management Technologies and Applications. DATA 2018.

ISBN

978-3-030-26635-6

Počet stran výsledku

22

Strana od-do

69-90

Počet stran knihy

210

Název nakladatele

Springer

Místo vydání

Neuveden

Kód UT WoS kapitoly

—