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Dynamic State-Space Partitioning in External-Memory Graph Search

Authors: Rong Zhou and Eric A. Hansen

Published in: Dagstuhl Seminar Proceedings, Volume 9491, Graph Search Engineering (2010)


Abstract
State-of-the-art external-memory graph search algorithms rely on a hash function, or equivalently, a state-space projection function, that partitions the stored nodes of the state-space search graph into groups of nodes that are stored as separate files on disk. The scalability and efficiency of the search depends on properties of the partition: whether the number of unique nodes in a file always fits in RAM, the number of files into which the nodes of the state-space graph are partitioned, and how well the partitioning of the state space captures local structure in the graph. All previous work relies on a static partitioning of the state space. In this paper, we introduce a method for dynamic partitioning of the state-space search graph and show that it leads to substantial improvement of search performance.

Cite as

Rong Zhou and Eric A. Hansen. Dynamic State-Space Partitioning in External-Memory Graph Search. In Graph Search Engineering. Dagstuhl Seminar Proceedings, Volume 9491, pp. 1-7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)


Copy BibTex To Clipboard

@InProceedings{zhou_et_al:DagSemProc.09491.2,
  author =	{Zhou, Rong and Hansen, Eric A.},
  title =	{{Dynamic State-Space Partitioning in External-Memory Graph Search}},
  booktitle =	{Graph Search Engineering},
  pages =	{1--7},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{9491},
  editor =	{Lubos Brim and Stefan Edelkamp and Erik A. Hansen and Peter Sanders},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09491.2},
  URN =		{urn:nbn:de:0030-drops-24334},
  doi =		{10.4230/DagSemProc.09491.2},
  annote =	{Keywords: External-memory graph search, heuristic search}
}
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