Scaling industrial applications for the Big Data era

Davor Šutić1 and Ervin Varga1

  1. Faculty of Technical Sciences, Trg D. Obradovića 6
    21000 Novi Sad, Serbia
    {sutic, evarga}@uns.ac.rs

Abstract

Industrial applications tend to rely increasingly on large datasets for regular operations. In order to facilitate that need, we unite the increasingly available hardware resources with fundamental problems found in classical algorithms. We show solutions to the following problems: power flow and island detection in power networks, and the more general graph sparsification. At their core lie respectively algorithms for solving systems of linear equations, graph connectivity and matrix multiplication, and spectral sparsification of graphs, which are applicable on their own to a far greater spectrum of problems. The novelty of our approach lies in developing the first open source and distributed solutions, capable of handling large datasets. Such solutions constitute a toolkit, which, aside from the initial purpose, can be used for the development of unrelated applications and for educational purposes in the study of distributed algorithms.

Key words

distributed computing, big data, smart grid

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200531039S

How to cite

Šutić, D., Varga, E.: Scaling industrial applications for the Big Data era. Computer Science and Information Systems, https://doi.org/10.2298/CSIS200531039S