Missing Data Interpolation Using Compressive Sensing: An Application for Sales Data Gathering

  • S. Spoorthy
  • Sandhyasree Thaskani
  • Adithya Sood
  • M. Girish Chandra
  • P. Balamuralidhar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 390)


Tasks like survey analysis involve collection of large amounts of data from different sources. However, there are several situations where exhaustive data collection could be quite cumbersome or infeasible. In this paper, we propose a novel Compressive Sensing (CS)-based framework to recover the original data from less number of collected data points in the case of market or survey research. We utilize the historical data to establish sparsity of data, and further introduce the concept of logical proximity for better recovery results. Additionally, we also present a conceptual idea toward adaptive sampling using data stream sketching, which suggests whether the collected data measurements are sufficient or not. The proposed CS-based methodology is tested with toy-sized examples and the results are presented to demonstrate its utility.


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Copyright information

© Springer India 2016

Authors and Affiliations

  • S. Spoorthy
    • 1
  • Sandhyasree Thaskani
    • 2
  • Adithya Sood
    • 3
  • M. Girish Chandra
    • 1
  • P. Balamuralidhar
    • 1
  1. 1.TCS Innovation LabsBangaloreIndia
  2. 2.TRDDCPuneIndia
  3. 3.Indian Institute of ManagementIndoreIndia

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