Survey of Object-Based Data Reduction Techniques in Observational Astronomy

Szymon Lukasik, André Moitinho, Piotr A. Kowalski, António Falcaõ, Rita A. Ribeiro, Piotr Kulczycki

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Dealing with astronomical observations represents one of the most challenging areas of big data analytics. Besides huge variety of data types, dynamics related to continuous data flow from multiple sources, handling enormous volumes of data is essential. This paper provides an overview of methods aimed at reducing both the number of features/attributes as well as data instances. It concentrates on data mining approaches not related to instruments and observation tools instead working on processed object-based data. The main goal of this article is to describe existing datasets on which algorithms are frequently tested, to characterize and classify available data reduction algorithms and identify promising solutions capable of addressing present and future challenges in astronomy.

Original languageEnglish
Pages (from-to)579-587
Number of pages9
JournalOpen Physics
Issue number1
Publication statusPublished - Jan 2016


  • Astronomy
  • Big data
  • Data condensation
  • Dimensionality reduction
  • Feature extraction


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