Probabilistic Kinetic Data Structures

نویسندگان

  • Betul Atalay
  • Sorelle A. Friedler
  • Dianna Xu
چکیده

We introduce a probabilistic kinetic data structure that uses a motion model to maintain a specific geometric structure to within a user-specified confidence value. We also introduce a certificate-based error model and accompanying problem-specific proofs that the robust error model is implied by this new, more geometrically generalizable, error model. We show that the probabilistic kinetic data structure translation from a classic kinetic data structure (KDS) is efficient and approximately correct under the certificate-based error model based on the user-given confidence value. Significantly, this means that any existing KDS is efficient under this probabilistic framework.

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تاریخ انتشار 2013