A Communication Efficient and Scalable Distributed Data Mining for the Astronomical Data

TL;DR

This paper introduces a scalable distributed data mining method for astronomical data that reduces network transmission and downloading costs while maintaining analysis accuracy, enabling efficient processing of massive datasets.

Contribution

It proposes a distributed load balancing PCA approach to optimize computation distribution and minimize data transfer costs in astronomical data analysis.

Findings

Reduces downloading cost by ~90% with negligible accuracy loss

Outperforms existing methods in transmission cost

Effective on complex astronomical datasets

Abstract

In 2020, ~60PB of archived data will be accessible to the astronomers. But to analyze such a paramount data will be a challenging task. This is basically due to the computational model used to download the data from complex geographically distributed archives to a central site and then analyzing it in the local systems. Because the data has to be downloaded to the central site, the network BW limitation will be a hindrance for the scientific discoveries. Also analyzing this PB-scale on local machines in a centralized manner is challenging. In this virtual observatory is a step towards this problem, however, it does not provide the data mining model. Adding the distributed data mining layer to the VO can be the solution in which the knowledge can be downloaded by the astronomers instead the raw data and thereafter astronomers can either reconstruct the data back from the downloaded knowledge or use the knowledge directly for further analysis.Therefore, in this paper, we present Distributed Load Balancing Principal Component Analysis for optimally distributing the computation among the available nodes to minimize the transmission cost and downloading cost for the end user. The experimental analysis is done with Fundamental Plane(FP) data, Gadotti data and complex Mfeat data. In terms of transmission cost, our approach performs better than Qi. et al. and Yue.et al. The analysis shows that with the complex Mfeat data ~90% downloading cost can be reduced for the end user with the negligible loss in accuracy.