Towards Artificial Priority Queues for Similarity Query Execution

ANTOL, Matej and Vlastislav DOHNAL. Towards Artificial Priority Queues for Similarity Query Execution. In 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW). Paris, France: IEEE. p. 78-83. ISBN 978-1-5386-6306-6. doi:10.1109/ICDEW.2018.00020. 2018.

Basic information

• Original name: Towards Artificial Priority Queues for Similarity Query Execution
• Authors: ANTOL, Matej (703 Slovakia, belonging to the institution) and Vlastislav DOHNAL (203 Czech Republic, guarantor, belonging to the institution).
• Edition: Paris, France, 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW), p. 78-83, 6 pp. 2018.
• Publisher: IEEE

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Czech Republic
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• WWW: URL
• RIV identification code: RIV/00216224:14330/18:00101035
• Organization unit: Faculty of Informatics
• ISBN: 978-1-5386-6306-6
• ISSN: 2473-3490
• Doi: http://dx.doi.org/10.1109/ICDEW.2018.00020
• UT WoS: 000440300600014
• Keywords in English: similarity search;index structure;knn algorithm evaluation;query processing optimization;metric space
• Tags: approximate search, DISA, index structures, performance evaluation
• Tags: International impact, Reviewed

Abstract

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.

Links

• GA16-18889S, research and development project: Name: Analytika pro velká nestrukturovaná data (Acronym: Big Data Analytics for Unstructured Data)

Investor: Czech Science Foundation

• MUNI/A/1213/2017, interní kód MU: Name: Aplikovaný výzkum na FI: bezpečnost počítačových systémů, SW architektury kritických infrastruktur, zpracování velkých dat, vizualizace dat a virtuální realita

Investor: Masaryk University, Applied research at FI: computer systems security, SW architecture of critical infrastructure, big data processing, data visualization and virtual reality, Category A