Tracking System for GPS Devices and Mining of Spatial Data AIDA ALISPAHIC, DZENANA DONKO Department for Computer Science and Informatics Faculty of Electrical Engineering, University of Sarajevo Zmaja od Bosne bb, Kampus Univerziteta, 71000 Sarajevo BOSNIA AND HERZEGOVINA aida.alispahic@gmail.com, ddonko@etf.unsa.ba Abstract: - This paper presents one implementation of tracking system of GPS devices and describes solution for different issues during the design, such as detection of current position, communication to databases and storing the data. Once the warehouse is created the multidimensional data model - cube, is produced and OLAP operations are performed in order to obtain different statistics. Mining of spatial data is possible in order to produce predictable data important for decision support. One of the results is identification of congested areas and support for selecting the fastest route. Key-Words: - location based services; tracking system; data cube; data mining 1 Introduction One of the main problems we are facing today in the IT oriented business world is congestion with information system data. The issue is reflected in the inability to recognize useful knowledge hidden in these data. Advanced technology provides the ability to solve the aforementioned problem with the use of data mining. With the goal of presenting information in a manner familiar to the user, visual presentation of data is used more often these days. Visual presentation is mainly associated with the virtual space. Digitization of space is based on spatial data. It found it roots in relatively new technology - location-based services (LBS). Sometimes, there is wrong identification of LBS as geographical information systems (GIS) as they are two separate technologies. LBS became possible recently thanks to the fast development and wide acceptance of technologies such as mobile phones, Internet, global positioning system (GPS), which had not been developed at the time when GIS were first developed [1]. One the main aspect in LBS is finding the location of users using their mobile device. Users’ locations are considered as the spatial context in LBS applications. Addition to this, spatial data warehousing results from the convergence of two technologies, spatial data handling and multidimensional data analysis, respectively. In business data warehouses, the spatial dimension is increasingly considered of strategic relevance for the analysis of enterprise data [2]. In addition, navigation and tracking are very common features that improve business processes. Visual presentation of data to the users from overcrowded information systems, identification of useful knowledge from these systems, and navigation and tracking is usage of data mining and spatial data. There is a lot of research work in this area to properly design and implement applications that tracking GPS devices [3]. In this paper we have used the spatial data from two databases in order to create spatial data warehouses and multidimensional models of spatial data, and to manipulate the created model by On- Line Analytical Processing (OLAP) and Extraction, Transformation and Loading (ETL) process: publicly available databases used by OpenStreetMap application and database that contains data collected using the system for the tracking of GPS devices. The system is implemented with the use of mobile devices with integrated GPS. In this paper we address the following: • The system for sending information about the current position of mobile device • Recording and manipulation of spatial data through the storage of data • Data mining of spatial data The second section of this paper gives a detailed description of the problem. The third section presents the model, shows the implementation of the solutions and lists some of the ideas for the possible improvement. The conclusion is given in the final fourth section of the paper. 2 Description of the Problem In the business world there is a need for determining the most popular, busiest and congested locations in a given area. The solution to this problem is based on Recent Advances in Computer Science and Applications ISBN: 978-960-474-317-9 100