Encyclopedia of Algorithms

2008 Edition
| Editors: Ming-Yang Kao

Engineering Algorithms for Large Network Applications

2002; Schulz, Wagner, Zaroliagis
  • Christos Zaroliagis
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30162-4_125

Problem Definition

Dealing effectively with applications in large networks, it typically requires the efficient solution of one ore more underlying algorithmic problems. Due to the size of the network, a considerable effort is inevitable in order to achieve the desired efficiency in the algorithm.             

One of the primary tasks in large network applications is to answer queries for finding best routes or paths as efficiently as possible. Quite often, the challenge is to process a vast number of such queries on-line: a typical situation encountered in several real-time applications (e. g., traffic information systems, public transportation systems) concerns a query‐intensive scenario, where a central server has to answer a huge number of on-line customer queries asking for their best routes (or optimal itineraries). The main goal in such an application is to reduce the (average) response time for a query.

Answering a best route (or optimal itinerary) query translates in computing...

Keywords

Short Path Query Time Short Path Problem Good Route Edge Cost 
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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Recommended Reading

  1. 1.
    Delling, D., Holzer, M., Müller, K., Schulz, F., Wagner, D.: High‐Performance Multi-Level Graphs. In: 9th DIMACS Challenge on Shortest Paths, Nov 2006. Rutgers University, USA (2006)Google Scholar
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    Delling, D., Sanders, P., Schultes, D., Wagner, D.: Highway Hierarchies Star. In: 9th DIMACS Challenge on Shortest Paths, Nov 2006 Rutgers University, USA (2006)Google Scholar
  3. 3.
    Goldberg, A.V., Harrelson, C.: Computing the Shortest Path: A * Search Meets Graph Theory. In: Proc. 16th ACM-SIAM Symposium on Discrete Algorithms – SODA, pp. 156–165. ACM, New York and SIAM, Philadelphia (2005)Google Scholar
  4. 4.
    Gutman, R.: Reach-based Routing: A New Approach to Shortest Path Algorithms Optimized for Road Networks. In: Algorithm Engineering and Experiments – ALENEX (SIAM, 2004), pp. 100–111. SIAM, Philadelphia (2004) Google Scholar
  5. 5.
    Holzer, M., Schulz, F., Wagner, D.: Engineering Multi-Level Overlay Graphs for Shortest-Path Queries. In: Algorithm Engineering and Experiments – ALENEX (SIAM, 2006), pp. 156–170. SIAM, Philadelphia (2006)Google Scholar
  6. 6.
    Pyrga, E., Schulz, F., Wagner, D., Zaroliagis, C.: Efficient Models for Timetable Information in Public Transportation Systems. ACM J. Exp. Algorithmic 12(2.4), 1–39 (2007)MathSciNetGoogle Scholar
  7. 7.
    Sanders, P., Schultes, D.: Highway Hierarchies Hasten Exact Shortest Path Queries. In: Algorithms – ESA 2005. Lect. Note Comp. Sci. 3669, 568–579 (2005)Google Scholar
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    Sanders, P., Schultes, D.: Engineering Highway Hierarchies. In: Algorithms – ESA 2006. Lect. Note Comp. Sci. 4168, 804–816 (2006)Google Scholar
  9. 9.
    Schulz, F., Wagner, D., Weihe, K.: Dijkstra's Algorithm On-Line: An Empirical Case Study from Public Railroad Transport. ACM J. Exp. Algorithmics 5(12), 1–23 (2000)MathSciNetGoogle Scholar
  10. 10.
    Schulz, F., Wagner, D., Zaroliagis, C.: Using Multi-Level Graphs for Timetable Information in Railway Systems. In: Algorithm Engineering and Experiments – ALENEX 2002. Lect. Note Comp. Sci. 2409, 43–59 (2002)Google Scholar
  11. 11.
    Wagner, D., Willhalm, T., Zaroliagis, C.: Geometric Containers for Efficient Shortest Path Computation. ACM J. Exp. Algorithmics 10(1.3), 1–30 (2005)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Christos Zaroliagis
    • 1
  1. 1.Department of Computer Engineering & InformaticsUniversity of PatrasPatrasGreece