### Abstract

This paper proposes and solves α-autonomy and κ-stops shortest path problems in large spatial databases. Given a source s and a destination d, an α-autonomy query retrieves a sequence of data points connecting s and d, such that the distance between any two consecutive points in the path is not greater than α. A κ-stops query retrieves a sequence that contains exactly κ intermediate data points. In both cases our aim is to compute the shortest path subject to these constraints. Assuming that the dataset is indexed by a data-partitioning method, the proposed techniques initially compute a sub-optimal path by utilizing the Euclidean distance information provided by the index. The length of the retrieved path is used to prune the search space, filtering out large parts of the input dataset. In a final step, the optimal (α-autonomy or κ-stops) path is computed (using only the non-eliminated data points) by an exact algorithm. We discuss several processing methods for both problems, and evaluate their efficiency through extensive experiments.

Original language | English |
---|---|

Pages (from-to) | 181-199 |

Number of pages | 19 |

Journal | Lecture Notes in Computer Science |

Volume | 3633 |

Publication status | Published - 2005 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

### Cite this

*Lecture Notes in Computer Science*,

*3633*, 181-199.

**Constrained shortest path computation.** / Terrovitis, Manolis; Bakiras, Spiridon; Papadias, Dimitris; Mouratidis, Kyriakos.

Research output: Contribution to journal › Article

*Lecture Notes in Computer Science*, vol. 3633, pp. 181-199.

}

TY - JOUR

T1 - Constrained shortest path computation

AU - Terrovitis, Manolis

AU - Bakiras, Spiridon

AU - Papadias, Dimitris

AU - Mouratidis, Kyriakos

PY - 2005

Y1 - 2005

N2 - This paper proposes and solves α-autonomy and κ-stops shortest path problems in large spatial databases. Given a source s and a destination d, an α-autonomy query retrieves a sequence of data points connecting s and d, such that the distance between any two consecutive points in the path is not greater than α. A κ-stops query retrieves a sequence that contains exactly κ intermediate data points. In both cases our aim is to compute the shortest path subject to these constraints. Assuming that the dataset is indexed by a data-partitioning method, the proposed techniques initially compute a sub-optimal path by utilizing the Euclidean distance information provided by the index. The length of the retrieved path is used to prune the search space, filtering out large parts of the input dataset. In a final step, the optimal (α-autonomy or κ-stops) path is computed (using only the non-eliminated data points) by an exact algorithm. We discuss several processing methods for both problems, and evaluate their efficiency through extensive experiments.

AB - This paper proposes and solves α-autonomy and κ-stops shortest path problems in large spatial databases. Given a source s and a destination d, an α-autonomy query retrieves a sequence of data points connecting s and d, such that the distance between any two consecutive points in the path is not greater than α. A κ-stops query retrieves a sequence that contains exactly κ intermediate data points. In both cases our aim is to compute the shortest path subject to these constraints. Assuming that the dataset is indexed by a data-partitioning method, the proposed techniques initially compute a sub-optimal path by utilizing the Euclidean distance information provided by the index. The length of the retrieved path is used to prune the search space, filtering out large parts of the input dataset. In a final step, the optimal (α-autonomy or κ-stops) path is computed (using only the non-eliminated data points) by an exact algorithm. We discuss several processing methods for both problems, and evaluate their efficiency through extensive experiments.

UR - http://www.scopus.com/inward/record.url?scp=26444468924&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=26444468924&partnerID=8YFLogxK

M3 - Article

VL - 3633

SP - 181

EP - 199

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SN - 0302-9743

ER -