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Workshop on Algorithms and Data Structures

WADS 2017: Algorithms and Data Structures pp 349–360Cite as

Improved Distance Sensitivity Oracles via Tree Partitioning

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10389))

Abstract

We introduce an improved structure of distance sensitivity oracle (DSO). The task is to pre-process a non-negatively weighted graph so that a data structure can quickly answer replacement path length for every triple of source, terminal and failed vertex. The previous best algorithm [Bernstein and Karger, 2009] constructs in time (\(\tilde{O}(\cdot )\) suppresses poly-logarithmic factors.) \(\tilde{O}(mn)\) a distance sensitivity oracle of size \(O(n^2\log n)\) that processes queries in O(1) time. As an improvement, our oracle takes up \(O(n^2)\) space, while preserving O(1) query efficiency and \(\tilde{O}(mn)\) preprocessing time. One should notice that space complexity and query time of our novel data structure are asymptotically optimal.

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Author information

Authors and Affiliations

  1. Tsinghua University, Beijing, China

    Ran Duan & Tianyi Zhang

Authors
  1. Ran Duan
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  2. Tianyi Zhang
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Corresponding author

Correspondence to Tianyi Zhang .

Editor information

Editors and Affiliations

  1. Computer Science, University of Toronto , Toronto, Ontario, Canada

    Faith Ellen

  2. Computer Science, Memorial University of Newfoundland , St. John’s, Newfoundland and Labrador, Canada

    Antonina Kolokolova

  3. Carleton University, Ottawa, Ontario, Canada

    Jörg-Rüdiger Sack

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Duan, R., Zhang, T. (2017). Improved Distance Sensitivity Oracles via Tree Partitioning. In: Ellen, F., Kolokolova, A., Sack, JR. (eds) Algorithms and Data Structures. WADS 2017. Lecture Notes in Computer Science(), vol 10389. Springer, Cham. https://doi.org/10.1007/978-3-319-62127-2_30

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  • DOI: https://doi.org/10.1007/978-3-319-62127-2_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62126-5

  • Online ISBN: 978-3-319-62127-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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