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International Symposium on Spatial and Temporal Databases

SSTD 2015: Advances in Spatial and Temporal Databases pp 40–58Cite as

ParetoPrep: Efficient Lower Bounds for Path Skylines and Fast Path Computation

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9239)

Abstract

Computing cost-optimal paths in network data is an important task in many application areas like transportation networks, computer networks, or social graphs. In many cases, the cost of an edge can be described by various cost criteria. For example, in a road network possible cost criteria are distance, time, ascent, energy consumption or toll fees. In such a multicriteria network, path optimality can be defined in various ways. In particular, optimality might be defined as a combination of the given cost factors. To avoid finding a suitable combination function, methods like path skyline queries return all potentially optimal paths. To compute alternative paths in larger networks, most efficient algorithms rely on lower bound cost estimations to approximate the remaining costs from an arbitrary node to the specified target. In this paper, we introduce ParetoPrep, a new method for efficient lower bound computation which can be used as a preprocessing step in multiple algorithms for computing path alternatives. ParetoPrep requires less time and visits less nodes in the network than state-of-the-art preprocessing steps. Our experiments show that path skyline and linear path skyline computation can be significantly accelareted by ParetoPrep.

Keywords

  • Optimal Path
  • Target Node
  • Query Time
  • Skyline Query
  • Cost Criterion

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-22363-6_3
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Notes

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    http://www.openstreetmap.org/.

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Acknowledgements

This research has received funding from the Shared E-Fleet project (in the IKTII program), by the German Federal Ministry of Economics and Technology (grant no. 01ME12107).

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Authors and Affiliations

  1. Institute for Informatics, Ludwig-Maximilians-University Munich, Oettingenstr. 67, 80538, Munich, Germany

    Gregor Jossé & Matthias Schubert

  2. Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani 3, 39100, Bozen-bolzano, Italy

    Michael Shekelyan

Authors
  1. Michael Shekelyan
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  2. Gregor Jossé
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  3. Matthias Schubert
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Corresponding author

Correspondence to Gregor Jossé .

Editor information

Editors and Affiliations

  1. Naval Academy Research Institute, Brest naval, France

    Christophe Claramunt

  2. University of Florida, Gainesville, Florida, USA

    Markus Schneider

  3. Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Raymond Chi-Wing Wong

  4. Emory University, Atlanta, USA

    Li Xiong

  5. Gachon University, Gyeonggi-do, Korea (Republic of)

    Woong-Kee Loh

  6. University of Southern California, Los Angeles, USA

    Cyrus Shahabi

  7. Department of Computer Science, Pusan National University, Pusan, Korea (Republic of)

    Ki-Joune Li

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Shekelyan, M., Jossé, G., Schubert, M. (2015). ParetoPrep: Efficient Lower Bounds for Path Skylines and Fast Path Computation. In: , et al. Advances in Spatial and Temporal Databases. SSTD 2015. Lecture Notes in Computer Science(), vol 9239. Springer, Cham. https://doi.org/10.1007/978-3-319-22363-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-22363-6_3

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