The MapReduce-based approach to improve the shortest path computation in large-scale road networks: the case of A algorithm

This paper deals with an efficient parallel and distributed framework for intensive computation with A* algorithm based on MapReduce concept. The A* algorithm is one of the most popular graph traversal algorithm used in route guidance. It requires exponential time computation and very costly hardwar...

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Bibliographic Details
Published in:Journal of big data Vol. 5; no. 1; pp. 1 - 24
Main Authors: Adoni, Wilfried Yves Hamilton, Nahhal, Tarik, Aghezzaf, Brahim, Elbyed, Abdeltif
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 03.05.2018
Springer Nature B.V
SpringerOpen
Subjects:
A algorithm
Advanced Soft Computing Methodologies and Applications in Social Media Big Data Analytics
Algorithms
Big Data
Communications Engineering
Computation
Computational Science and Engineering
Computer Science
Computing time
Data Mining and Knowledge Discovery
Database Management
Experiments
Feasibility
Frame analysis
Hadoop
Information Storage and Retrieval
Large-scale network
MapReduce
Mathematical Applications in Computer Science
Network reliability
Networks
Path-finding
Reliability
Roads & highways
Shortest-path problems
Transportation networks
Large-scale network
A
Parallel and distributed computing
Hadoop
Big Data
HDFS
Path-finding
algorithm
MapReduce
ISSN:2196-1115, 2196-1115
Online Access:Get full text
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Summary:This paper deals with an efficient parallel and distributed framework for intensive computation with A* algorithm based on MapReduce concept. The A* algorithm is one of the most popular graph traversal algorithm used in route guidance. It requires exponential time computation and very costly hardware to compute the shortest path on large-scale networks. Thus, it is necessary to reduce the time complexity while exploiting a low cost commodity hardwares. To cope with this situation, we propose a novel approach that reduces the A* algorithm into a set of Map and Reduce tasks for running the path computation on Hadoop MapReduce framework. An application on real road networks illustrates the feasibility and reliability of the proposed framework. The experiments performed on a 6-node Hadoop cluster proves that the proposed approach outperforms A* algorithm and achieves significant gain in terms of computation time.
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ISSN:2196-1115
2196-1115
DOI:10.1186/s40537-018-0125-8