Aggregate Function Generalization to Temporal Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00351728" target="_blank" >RIV/68407700:21240/21:00351728 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICTAI52525.2021.00098" target="_blank" >http://dx.doi.org/10.1109/ICTAI52525.2021.00098</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICTAI52525.2021.00098" target="_blank" >10.1109/ICTAI52525.2021.00098</a>
Alternative languages
Result language
angličtina
Original language name
Aggregate Function Generalization to Temporal Data
Original language description
In this article, we define an approximate generalization of aggregate functions for relational data with temporal attributes. This generalization is parametrized to allow simulation of a range of common aggregate functions and optionally take into account time. The parameters are not optimized, but we rather rely on repeated stochastic sampling of the parameters. We then apply a common regularized linear model to train a model on this high-dimensional space. Experimental results on 11 datasets suggest that there are datasets where incorporating time dimension into the model leads to an improvement in the predictive accuracy of the trained models.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)
ISBN
978-1-6654-0898-1
ISSN
1082-3409
e-ISSN
2375-0197
Number of pages
5
Pages from-to
614-618
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Washington
Event date
Nov 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000747482300090