Continuous Time-Dependent kNN Join by Binary Sketches

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00100951" target="_blank" >RIV/00216224:14330/18:00100951 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3216122.3216159" target="_blank" >http://dx.doi.org/10.1145/3216122.3216159</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3216122.3216159" target="_blank" >10.1145/3216122.3216159</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Continuous Time-Dependent kNN Join by Binary Sketches

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Continuous Time-Dependent kNN Join by Binary Sketches

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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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

IDEAS 2018 : 22nd International Database Engineering & Applications Symposium, June 18-20, 2018, Villa San Giovanni, Italy

ISBN

9781450365277

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

64-73

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

Villa San Giovanni, Italy

Datum konání akce

18. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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