Perspects in astrophysical databases

TL;DR

This paper reviews data management and mining techniques essential for handling large astrophysical datasets, emphasizing efficient access, metadata management, and scalable clustering and classification methods.

Contribution

It provides a comprehensive review of solutions for efficient data access, metadata handling, and scalable analysis techniques in astrophysical databases.

Findings

Multidimensional access methods improve data retrieval efficiency.

Proper metadata handling simplifies data management.

Scalable clustering and classification techniques are crucial for large datasets.

Abstract

Astrophysics has become a domain extremely rich of scientific data. Data mining tools are needed for information extraction from such large datasets. This asks for an approach to data management emphasizing the efficiency and simplicity of data access; efficiency is obtained using multidimensional access methods and simplicity is achieved by properly handling metadata. Moreover, clustering and classification techniques on large datasets pose additional requirements in terms of computation and memory scalability and interpretability of results. In this study we review some possible solutions.