Continuous Time-Dependent kNN Join by Binary Sketches

D 2018

Continuous Time-Dependent kNN Join by Binary Sketches

NÁLEPA, Filip, Michal BATKO and Pavel ZEZULA

Basic information

Original name

Continuous Time-Dependent kNN Join by Binary Sketches

Authors

NÁLEPA, Filip (203 Czech Republic, guarantor, belonging to the institution), Michal BATKO (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution)

Edition

New York, IDEAS 2018 : 22nd International Database Engineering & Applications Symposium, June 18-20, 2018, Villa San Giovanni, Italy, p. 64-73, 10 pp. 2018

Publisher

ACM

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

RIV identification code

RIV/00216224:14330/18:00100951

Organization unit

Faculty of Informatics

ISBN

978-1-4503-6527-7

DOI

http://dx.doi.org/10.1145/3216122.3216159

Keywords in English

continuous kNN similarity join; time-dependent similarity; binary sketches

Abstract

V originále

An important functionality of current social applications is real-time recommendation, which is responsible for suggesting relevant published data to the users based on their preferences. By representing the users and the published data in a metric space, each user can be recommended with their k nearest neighbors among the published data. We consider the scenario when the relevance of a published data item to a user decreases as the data gets older, i.e., a time-dependent distance function is applied. We define the problem as the continuous time-dependent kNN join and provide a solution to a broad range of time-dependent functions. In addition, we propose a binary sketch-based approximation technique used to speed up the join evaluation by replacing expensive metric distance computations with cheap Hamming distances.

Links

GA16-18889S, research and development project

Name: Analytika pro velká nestrukturovaná data (Acronym: Big Data Analytics for Unstructured Data)

Investor: Czech Science Foundation

Citovat

NÁLEPA, Filip, Michal BATKO and Pavel ZEZULA. Continuous Time-Dependent kNN Join by Binary Sketches. Online. In Bipin C. Desai. IDEAS 2018 : 22nd International Database Engineering & Applications Symposium, June 18-20, 2018, Villa San Giovanni, Italy. New York: ACM, 2018, p. 64-73. ISBN 978-1-4503-6527-7. Available from: https://dx.doi.org/10.1145/3216122.3216159.

@inproceedings{1418219,
   author = {Nálepa, Filip and Batko, Michal and Zezula, Pavel},
   address = {New York},
   booktitle = {IDEAS 2018 : 22nd International Database Engineering & Applications Symposium, June 18-20, 2018, Villa San Giovanni, Italy},
   doi = {http://dx.doi.org/10.1145/3216122.3216159},
   editor = {Bipin C. Desai},
   keywords = {continuous kNN similarity join; time-dependent similarity; binary sketches},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York},
   isbn = {978-1-4503-6527-7},
   pages = {64-73},
   publisher = {ACM},
   title = {Continuous Time-Dependent kNN Join by Binary Sketches},
   year = {2018}
}

TY  - JOUR
ID  - 1418219
AU  - Nálepa, Filip - Batko, Michal - Zezula, Pavel
PY  - 2018
TI  - Continuous Time-Dependent kNN Join by Binary Sketches
PB  - ACM
CY  - New York
SN  - 9781450365277
KW  - continuous kNN similarity join
KW  - time-dependent similarity
KW  - binary sketches
N2  - An important functionality of current social applications is real-time recommendation, which is responsible for suggesting relevant published data to the users based on their preferences. By representing the users and the published data in a metric space, each user can be recommended with their k nearest neighbors among the published data. We consider the scenario when the relevance of a published data item to a user decreases as the data gets older, i.e., a time-dependent distance function is applied. We define the problem as the continuous time-dependent kNN join and provide a solution to a broad range of time-dependent functions. In addition, we propose a binary sketch-based approximation technique used to speed up the join evaluation by replacing expensive metric distance computations with cheap Hamming distances.
ER  -

NÁLEPA, Filip, Michal BATKO and Pavel ZEZULA. Continuous Time-Dependent kNN Join by Binary Sketches. Online. In Bipin C. Desai. \textit{IDEAS 2018 : 22nd International Database Engineering \&{} Applications Symposium, June 18-20, 2018, Villa San Giovanni, Italy}. New York: ACM, 2018, p.~64-73. ISBN~978-1-4503-6527-7. Available from: https://dx.doi.org/10.1145/3216122.3216159.

Detailed Information on Publication Record