Continuous Time-Dependent kNN Join by Binary Sketches

D 2018

Continuous Time-Dependent kNN Join by Binary Sketches

NÁLEPA, Filip, Michal BATKO a Pavel ZEZULA

Základní údaje

Originální název

Continuous Time-Dependent kNN Join by Binary Sketches

Autoři

NÁLEPA, Filip (203 Česká republika, garant, domácí), Michal BATKO (203 Česká republika, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

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

Nakladatel

ACM

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/18:00100951

Organizační jednotka

Fakulta informatiky

ISBN

978-1-4503-6527-7

DOI

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

Klíčová slova anglicky

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

Štítky

core_B, DISA, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 30. 4. 2019 07:40, RNDr. Pavel Šmerk, Ph.D.

Anotace

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.

Návaznosti

GA16-18889S, projekt VaV

Název: Analytika pro velká nestrukturovaná data (Akronym: Big Data Analytics for Unstructured Data)

Investor: Grantová agentura ČR, Big Data Analytics for Unstructured Data

Citovat

NÁLEPA, Filip, Michal BATKO a 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, s. 64-73. ISBN 978-1-4503-6527-7. Dostupné z: 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 a 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, s.~64-73. ISBN~978-1-4503-6527-7. Dostupné z: https://dx.doi.org/10.1145/3216122.3216159.

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