Towards Artificial Priority Queues for Similarity Query Execution
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Towards Artificial Priority Queues for Similarity Query Execution
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA16-18889S" target="_blank" >GA16-18889S: Big Data Analytics for Unstructured Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)
ISBN
9781538663066
ISSN
2473-3490
e-ISSN
—
Number of pages
6
Pages from-to
78-83
Publisher name
IEEE
Place of publication
Paris, France
Event location
Paris, France
Event date
Jan 1, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—