Enhanced adaptive partitioning in a distributed graph database

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F20%3A00345598" target="_blank" >RIV/68407700:21240/20:00345598 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1080/24751839.2020.1829387" target="_blank" >https://doi.org/10.1080/24751839.2020.1829387</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/24751839.2020.1829387" target="_blank" >10.1080/24751839.2020.1829387</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhanced adaptive partitioning in a distributed graph database

Popis výsledku v původním jazyce

Nowadays, open-source graph databases do not include an inherent mechanism for data relocation that would be based on their usage. They often do not offer even appropriate monitoring that could help to make such a decision. Information about data utilization could, however, work as an input to some decision- making process about more suitable data regrouping that could be much more efficient in terms of intra-network communication. Therefore, we created a module for the graph computational framework TinkerPop that logs traffic generated by the user queries. These logged records serve as an input for the algorithm of Adaptive Partitioning that we enhanced with better balancing, avoidance of local optima and the notion of weighted graphs. This approach yields a 70–80% improvement in intra-network communication, which is comparable to other methods, namely Ja-be-Ja, that offers similar results but has higher computational demands.

Název v anglickém jazyce

Enhanced adaptive partitioning in a distributed graph database

Popis výsledku anglicky

Nowadays, open-source graph databases do not include an inherent mechanism for data relocation that would be based on their usage. They often do not offer even appropriate monitoring that could help to make such a decision. Information about data utilization could, however, work as an input to some decision- making process about more suitable data regrouping that could be much more efficient in terms of intra-network communication. Therefore, we created a module for the graph computational framework TinkerPop that logs traffic generated by the user queries. These logged records serve as an input for the algorithm of Adaptive Partitioning that we enhanced with better balancing, avoidance of local optima and the notion of weighted graphs. This approach yields a 70–80% improvement in intra-network communication, which is comparable to other methods, namely Ja-be-Ja, that offers similar results but has higher computational demands.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Journal of Information and Telecommunication

ISSN

2475-1847

e-ISSN

2475-1847

Svazek periodika

5

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

17

Strana od-do

104-120

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—