Improving the Performance of M-tree Family by Nearest-Neighbor Graphs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F07%3A00206190" target="_blank" >RIV/00216208:11320/07:00206190 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Improving the Performance of M-tree Family by Nearest-Neighbor Graphs

Popis výsledku v původním jazyce

The M-tree and its variants have been proved to provide an efficient similarity search in database environments. In order to further improve their performance, in this paper we propose an extension of the M-tree family, which makes use of nearest-neighbor (NN) graphs. Each tree node maintains its own NN-graph, a structure that stores for each node entry a reference (and distance) to its nearest neighbor, considering just entries of the node. The NN-graph can be used to improve filtering of non-relevantsubtrees when searching (or inserting new data). The filtering is based on using ?sacrifices? ? selected entries in the node serving as pivots to all entries being their reverse nearest neighbors (RNNs). We propose several heuristics for sacrifice selection; modified insertion; range and kNN query algorithms. The experiments have shown the M-tree (and variants) enhanced by NN-graphs can perform significantly faster, while keeping the construction cheap.

Název v anglickém jazyce

Improving the Performance of M-tree Family by Nearest-Neighbor Graphs

Popis výsledku anglicky

The M-tree and its variants have been proved to provide an efficient similarity search in database environments. In order to further improve their performance, in this paper we propose an extension of the M-tree family, which makes use of nearest-neighbor (NN) graphs. Each tree node maintains its own NN-graph, a structure that stores for each node entry a reference (and distance) to its nearest neighbor, considering just entries of the node. The NN-graph can be used to improve filtering of non-relevantsubtrees when searching (or inserting new data). The filtering is based on using ?sacrifices? ? selected entries in the node serving as pivots to all entries being their reverse nearest neighbors (RNNs). We propose several heuristics for sacrifice selection; modified insertion; range and kNN query algorithms. The experiments have shown the M-tree (and variants) enhanced by NN-graphs can perform significantly faster, while keeping the construction cheap.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

<a href="/cs/project/GP201%2F05%2FP036" target="_blank" >GP201/05/P036: Efektivní metrické vyhledávání v rozsáhlých multimediálních databázích</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2007

Kód důvěrnosti údajů

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

Název statě ve sborníku

11th East-European Conference on Advances in Databases and Information Systems ADBIS 2007

ISBN

978-3-540-75184-7

ISSN

—

e-ISSN

—

Počet stran výsledku

17

Strana od-do

—

Název nakladatele

Springer

Místo vydání

Varna, Bulgaria

Místo konání akce

Varna, Bulgaria

Datum konání akce

1. 1. 2007

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000250283400014