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An Improved Route-Finding Algorithm Using Ubiquitous Ontology-Based Experiences Modeling

Author

Listed:
  • Maryam Barzegar
  • Abolghasem Sadeghi-Niaraki
  • Maryam Shakeri
  • Soo-Mi Choi

Abstract

Every day, people are hired in different organizations and old and retiring employees are eliminated from enterprise systems. Eliminating these individuals from organizations leads to the loss of their spatial experiences. In addition, since new employees lack relevant experience, they need a long time to develop the correct skills for the company and may even cause damage to the organization during this learning process. Therefore, storing the spatial experience of individuals is a critical issue. Due to the intelligence of ubiquitous Geospatial Information System (GIS), any experience from any user can be received and stored. In the future, based on these experiences, an appropriate service to each user may be provided as needed. This paper aims to propose an ontology-based model to store spatial experiences in the field of ubiquitous GIS route finding. For this purpose, first ontology is designed for route finding, and then according to this ontology, an ontology-based route-finding algorithm is developed for ubiquitous GIS. Finally, this algorithm is implemented for Tehran, Iran, and its results are compared with the shortest path algorithm (Dijkstra’s algorithm) in terms of the route length and travel time for peak traffic time. The results show that while the route length obtained from the ontology-based algorithm is more than Dijkstra’s algorithm, the travel time is lower, and on some routes the difference in travel time saved reaches 35 minutes.

Suggested Citation

  • Maryam Barzegar & Abolghasem Sadeghi-Niaraki & Maryam Shakeri & Soo-Mi Choi, 2019. "An Improved Route-Finding Algorithm Using Ubiquitous Ontology-Based Experiences Modeling," Complexity, Hindawi, vol. 2019, pages 1-15, November.
    • Handle: RePEc:hin:complx:9584397
    DOI: 10.1155/2019/9584397
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    References listed on IDEAS

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    1. Dawen Xia & Xiaonan Lu & Huaqing Li & Wendong Wang & Yantao Li & Zili Zhang, 2018. "A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data," Complexity, Hindawi, vol. 2018, pages 1-16, January.
    2. Gergely Marcell Honti & Janos Abonyi, 2019. "A Review of Semantic Sensor Technologies in Internet of Things Architectures," Complexity, Hindawi, vol. 2019, pages 1-21, June.
    3. Qingchun Meng & Zhen Zhang & Xiaole Wan & Xiaoxia Rong, 2018. "Properties Exploring and Information Mining in Consumer Community Network: A Case of Huawei Pollen Club," Complexity, Hindawi, vol. 2018, pages 1-19, November.
    4. Jing Geng & Shuliang Wang & Wenxia Gan & Hanning Yuan & Zeqiang Chen & Ziqiang Yuan & Tianru Dai, 2019. "Promoting Geospatial Service from Information to Knowledge with Spatiotemporal Semantics," Complexity, Hindawi, vol. 2019, pages 1-14, January.
    5. Pradorn Sureephong & Nopasit Chakpitak & Yacine Ouzrout & Abdelaziz Bouras, 2008. "An Ontology-based Knowledge Management System for Industry Clusters," Post-Print hal-00284594, HAL.
    6. Usharani Hareesh Govindarajan & Amy J. C. Trappey & Charles V. Trappey, 2018. "Immersive Technology for Human-Centric Cyberphysical Systems in Complex Manufacturing Processes: A Comprehensive Overview of the Global Patent Profile Using Collective Intelligence," Complexity, Hindawi, vol. 2018, pages 1-17, February.
    7. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
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