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TopCoder's Open Community Challenge Process Yields 970 Fold Increase in Speed for Big Data Genomics Sequencing Algorithm
Date:2/7/2013

to the applicability of deploying crowds as an innovation partner for extraordinarily difficult challenges where there are significant personnel and paradigmatic bottlenecks," he said. "This paper highlights the use of an alternative organizational form that is cost effective and productive. Many more organizations should also be considering how to effectively use crowds for problem solving."

The results achieved by contest participants in only fourteen days improved significantly upon the existing solutions available to academic researchers, decreasing processing time by up to three orders of magnitude with accuracy reaching the theoretical maximum. While the solvers were virtually devoid of domain-specific knowledge, abstracting the problem into general algorithmic and mathematical terms allowed a wide range of non-domain experts to address an important, complex problem. These contestants brought to the problem whatever skills and expertise they had or could find, likely yielding a far more diverse toolkit than would be available locally, and generated significant diversity in technical approaches.  Accessing such diversity may be particularly important, as big data biomedical analytics is a rapidly evolving field in which it is difficult to know a priori the kind, quality and breadth of expertise needed to generate an effective solution. 

The work was funded by Harvard Business School's Division of Research and Faculty Development, the NASA Tournament Lab at Harvard's Institute for Quantitative Social Science, and Harvard Catalyst. The research effort also included invaluable insights and contributions from coauthors Po-Ru Loh (MIT), Lars Backstrom (TopCoder), Carliss Baldwin (HBS), Eric Lonstein (HBS), Mike Lydon (TopCoder) and Alan MacCormack (HBS).

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