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.
Highlights
The drivers’ experience class includes two subclasses: experimental routes and nonexperimental routes. e experimental route class encompasses the paths which the experienced drivers have driven. ese routes are collected via an app and are converted to OWL classes. is class, itself, includes two subclasses: traffic and without-traffic classes. e paths which have been passed at peak traffic times (7–10 AM and 16–20 PM) are categorized under the traffic subclass. e nonexperimental route class encompasses the paths obtained from the route-finding procedure
If a new path has been created and the time entered by the user is within the pick range, it will be stored in the traffic subclass of the nonexperimental route class
The stored route is displayed to the user and the algorithm terminates, without needing to conduct route finding
Summary
Knowledge management is a process that helps organizations to detect, select, organize, and publish important information and skills that are considered as organizational memory and which are not typically organized. is enables organizations to e ectively manage learning issues, strategic planning, and dynamic decision making. e causes of the advent of knowledge management can be considered as follows: (1) transformation of the industrial business model; in the past, the assets of an organization were fundamentally tangible and nancial assets (production facilities, cars, land, and so on); (2) an extraordinary increase in the amount of information and its electronic storage and increased access to information have generally added value to knowledge; (3) the changes in the age pyramid of populations and the demographic properties that are mentioned in only a few sources; and (4) specializing in activities may hold the risk of losing organizational and expertise knowledge through the transfer or dismissal of employees. Taxi associations are one of the organizations that need to organize organizational memory. In this organization, taxi drivers acquire skills and knowledge by repeating their daily route many times so that after some years, they become experts and experienced person in their work. Taxi drivers acquire skills and knowledge by repeating their daily route many times so that after some years, they become experts and experienced person in their work This experience is a kind of knowledge which is gained after many. As finding a route between locations and destinations by taxi drivers is a location-based activity that is conducted by taxi drivers in a city, the experiences of taxi drivers in finding good routes are spatial experiences. Provides the opportunity of reusing domain knowledge, and makes domain assumptions explicit [3]
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