Leveraging similarity joins for signal reconstruction

Abolfazl Asudeh, Azade Nazi, Jees Augustine, Saravanan Thirumuruganathan, Nan Zhang, Gautam Das, Divesh Srivastava

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

Signal reconstruction problem (SRP) is an important optimization problem where the objective is to identify a solution to an underdetermined system of linear equations that is closest to a given prior. It has a substantial number of applications in diverse areas including network traffic engineering, medical image reconstruction, acoustics, astronomy and many more. Most common approaches for SRP do not scale to large problem sizes. In this paper, we propose a dual formulation of this problem and show how adapting database techniques developed for scalable similarity joins provides a significant speedup. Extensive experiments on real-world and synthetic data show that our approach produces a significant speedup of up to 20x over competing approaches.

Original languageEnglish
Pages (from-to)1276-1288
Number of pages13
JournalProceedings of the VLDB Endowment
Volume11
Issue number10
DOIs
Publication statusPublished - 1 Jan 2018
Event44th International Conference on Very Large Data Bases, VLDB 2018 - Rio de Janeiro, Brazil
Duration: 27 Aug 201731 Aug 2017

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ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

Cite this

Asudeh, A., Nazi, A., Augustine, J., Thirumuruganathan, S., Zhang, N., Das, G., & Srivastava, D. (2018). Leveraging similarity joins for signal reconstruction. Proceedings of the VLDB Endowment, 11(10), 1276-1288. https://doi.org/10.14778/3231751.3231752