Skip to content


  • Research Article
  • Open Access

A Data-Driven Multidimensional Indexing Method for Data Mining in Astrophysical Databases

EURASIP Journal on Advances in Signal Processing20052005:841610

Received: 1 June 2004

Published: 14 September 2005


Large archives and digital sky surveys with dimensions of bytes currently exist, while in the near future they will reach sizes of the order of . Numerical simulations are also producing comparable volumes of information. Data mining tools are needed for information extraction from such large datasets. In this work, we propose a multidimensional indexing method, based on a static R-tree data structure, to efficiently query and mine large astrophysical datasets. We follow a top-down construction method, called VAMSplit, which recursively splits the dataset on a near median element along the dimension with maximum variance. The obtained index partitions the dataset into nonoverlapping bounding boxes, with volumes proportional to the local data density. Finally, we show an application of this method for the detection of point sources from a gamma-ray photon list.

Keywords and phrases

  • multidimensional indexing
  • VAMSplit R-tree
  • nearest-neighbor query
  • one-class SVM
  • point sources

Authors’ Affiliations

Dipartimento di Fisica, Università degli Studi di Udine, Udine, Italy
INFN, Sezione di Trieste, Gruppo Collegato di Udine, Udine, Italy
Dipartimento di Matematica e Informatica, Università degli Studi di Udine, Udine, Italy


© Frailis et al. 2005