Evaluating top-k algorithms with various sources of data and user preferences

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F11%3A10106669" target="_blank" >RIV/00216208:11320/11:10106669 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.scopus.com" target="_blank" >http://www.scopus.com</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-24764-4_23" target="_blank" >10.1007/978-3-642-24764-4_23</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluating top-k algorithms with various sources of data and user preferences

Popis výsledku v původním jazyce

Our main motivation is the data access model and aggregation algorithm for middleware by R. Fagin, A. Lotem and M. Naor. They assume data attributes in a variety of repositories ordered by a grade of attribute values of objects. Moreover they assume theuser has an aggregation function, which eventually qualifies an object to top-k answers. In this paper we adopt a model of various users (there is no single ordering of objects in repositories and no single aggregation) with user preference learning algorithm on the middleware side. We present a new model of repository for simultaneous access by many users. The model is an extension of original model of Fagin, Lotem, Naor. Our solution is based on a model of fast learning of user preferences from his/her reactions. Experiments are focused on the performance of top-k algorithms (both TA and NRA) using data integration on an experimental prototype of our solution. Cache size, network latency and batch size were the features studied in exp

Název v anglickém jazyce

Evaluating top-k algorithms with various sources of data and user preferences

Popis výsledku anglicky

Our main motivation is the data access model and aggregation algorithm for middleware by R. Fagin, A. Lotem and M. Naor. They assume data attributes in a variety of repositories ordered by a grade of attribute values of objects. Moreover they assume theuser has an aggregation function, which eventually qualifies an object to top-k answers. In this paper we adopt a model of various users (there is no single ordering of objects in repositories and no single aggregation) with user preference learning algorithm on the middleware side. We present a new model of repository for simultaneous access by many users. The model is an extension of original model of Fagin, Lotem, Naor. Our solution is based on a model of fast learning of user preferences from his/her reactions. Experiments are focused on the performance of top-k algorithms (both TA and NRA) using data integration on an experimental prototype of our solution. Cache size, network latency and batch size were the features studied in exp

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Lecture Notes in Computer Science

ISSN

0302-9743

e-ISSN

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Svazek periodika

2011

Číslo periodika v rámci svazku

7022

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

12

Strana od-do

258-269

Kód UT WoS článku

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EID výsledku v databázi Scopus

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