Designing a human-machine hybrid computing system for unstructured data analytics

Koushik Sinha, Geetha Manjunath, Bidyut Gupta, Shahram Rahimi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Current machine algorithms for analysis of unstructured data typically show low accuracies due to the need for human-like intelligence. Conversely, though humans are much better than machine algorithms on analyzing unstructured data, they are unpredictable, slower and can be erroneous or even malicious as computing agents. Therefore, a hybrid platform that can intelligently orchestrate machine and human computing resources would potentially be capable of providing significantly better benefits compared to either type of computing agent in isolation. In this paper, we propose a new hybrid human-machine computing platform with integrated service level objectives (SLO) management for complex tasks that can be decomposed into a dependency graph where nodes represent subtasks. Initial experimental results are highly encouraging. To the best of our knowledge, ours is the first work that attempts to design such a hybrid human-machine computing platform with support for addressing the three SLO parameters of accuracy, budget and completion time. Copyright ISCA.

Original languageEnglish
Title of host publicationProceedings of the 31st International Conference on Computers and Their Applications, CATA 2016
PublisherThe International Society for Computers and Their Applications (ISCA)
Pages23-28
Number of pages6
ISBN (Electronic)9781943436026
Publication statusPublished - 2016
Externally publishedYes
Event31st International Conference on Computers and Their Applications, CATA 2016 - Las Vegas, United States
Duration: 4 Apr 20166 Apr 2016

Other

Other31st International Conference on Computers and Their Applications, CATA 2016
CountryUnited States
CityLas Vegas
Period4/4/166/4/16

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Keywords

  • Crowdsourcing
  • Data analytics
  • Human augmented computing
  • Microtask
  • Service level objectives
  • Task scheduling

ASJC Scopus subject areas

  • Computer Science Applications

Cite this

Sinha, K., Manjunath, G., Gupta, B., & Rahimi, S. (2016). Designing a human-machine hybrid computing system for unstructured data analytics. In Proceedings of the 31st International Conference on Computers and Their Applications, CATA 2016 (pp. 23-28). The International Society for Computers and Their Applications (ISCA).