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Can We Locally Compute Sparse Connected Subgraphs?

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Computer Science – Theory and Applications (CSR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10304))

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Abstract

How can we solve optimization problems on data that is so large, that we cannot hope to view more than a miniscule fraction of it? When attempting to solve optimization problems on big data, we are presented with a double catastrophe, as both the inputs to and the outputs from the computation are large. One ray of hope is that often, the portion of the output that is needed by the user is, in fact, of a more manageable size. In such a situation, it would be useful if one could find very fast ways of computing only the portion of the output that is required by the user.

R. Rubinfeld—Supported by ISF grant 1147/09 and NSF grant CCF-1650733.

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Notes

  1. 1.

    We use the word probe to refer to the LCAs views of locations in the input and query to refer to the user requests to the LCA.

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  1. MIT, Cambridge, USA

    Ronitt Rubinfeld

  2. Tel Aviv University, Tel Aviv, Israel

    Ronitt Rubinfeld

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    Pascal Weil

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Rubinfeld, R. (2017). Can We Locally Compute Sparse Connected Subgraphs?. In: Weil, P. (eds) Computer Science – Theory and Applications. CSR 2017. Lecture Notes in Computer Science(), vol 10304. Springer, Cham. https://doi.org/10.1007/978-3-319-58747-9_6

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