- Tytuł:
- Efficient astronomical data condensation using approximate nearest neighbors
- Autorzy:
-
Łukasik, Szymon
Lalik, Konrad
Sarna, Piotr
Kowalski, Piotr A.
Charytanowicz, Małgorzata
Kulczycki, Piotr - Powiązania:
- https://bibliotekanauki.pl/articles/907932.pdf
- Data publikacji:
- 2019
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
big data
astronomical observation
data reduction
nearest neighbor search
kd-trees
duży zbiór danych
obserwacja astronomiczna
redukcja danych
wyszukiwanie najbliższego sąsiada
drzewo kd - Opis:
- Extracting useful information from astronomical observations represents one of the most challenging tasks of data exploration. This is largely due to the volume of the data acquired using advanced observational tools. While other challenges typical for the class of big data problems (like data variety) are also present, the size of datasets represents the most significant obstacle in visualization and subsequent analysis. This paper studies an efficient data condensation algorithm aimed at providing its compact representation. It is based on fast nearest neighbor calculation using tree structures and parallel processing. In addition to that, the possibility of using approximate identification of neighbors, to even further improve the algorithm time performance, is also evaluated. The properties of the proposed approach, both in terms of performance and condensation quality, are experimentally assessed on astronomical datasets related to the GAIA mission. It is concluded that the introduced technique might serve as a scalable method of alleviating the problem of the dataset size.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 467-476
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł