Towards Artificial Priority Queues for Similarity Query Execution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00101035" target="_blank" >RIV/00216224:14330/18:00101035 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8402023/" target="_blank" >https://ieeexplore.ieee.org/document/8402023/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICDEW.2018.00020" target="_blank" >10.1109/ICDEW.2018.00020</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Artificial Priority Queues for Similarity Query Execution

Popis výsledku v původním jazyce

Content-based retrieval in large collections of unstructured data is challenging not only from the difficulty of the defining similarity between data images where the phenomenon of semantic gap appears, but also the efficiency of execution of similarity queries. Search engines providing similarity search typically organize various multimedia data, e.g. images of a photo stock, and support k-nearest neighbor query. Users accessing such systems then look for data items similar to their specific query object and refine results by re-running the search with an object from the previous query results. This paper is motivated by unsatisfactory query execution performance of indexing structures that use metric space as a convenient data model. We present performance behavior of two state-of-the-art representatives and propose a new universal technique for ordering priority queue of data partitions to be accessed during kNN query evaluation. We verify it in experiments on real-life data-sets.

Název v anglickém jazyce

Towards Artificial Priority Queues for Similarity Query Execution

Popis výsledku anglicky

Content-based retrieval in large collections of unstructured data is challenging not only from the difficulty of the defining similarity between data images where the phenomenon of semantic gap appears, but also the efficiency of execution of similarity queries. Search engines providing similarity search typically organize various multimedia data, e.g. images of a photo stock, and support k-nearest neighbor query. Users accessing such systems then look for data items similar to their specific query object and refine results by re-running the search with an object from the previous query results. This paper is motivated by unsatisfactory query execution performance of indexing structures that use metric space as a convenient data model. We present performance behavior of two state-of-the-art representatives and propose a new universal technique for ordering priority queue of data partitions to be accessed during kNN query evaluation. We verify it in experiments on real-life data-sets.

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/GA16-18889S" target="_blank" >GA16-18889S: Analytika pro velká nestrukturovaná data</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)

ISBN

9781538663066

ISSN

2473-3490

e-ISSN

—

Počet stran výsledku

6

Strana od-do

78-83

Název nakladatele

IEEE

Místo vydání

Paris, France

Místo konání akce

Paris, France

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—