Abstract
Location-based services (LBSs) that collect and utilize location data in real time through mobile devices have been widely used recently. LBSs use a clustering algorithm to extract a user’s point of interest (POI) from a stay point; the POI is defined as a place that an individual stays in or uses for a given amount of time. However, the DBSCAN algorithm increases the amount of unnecessary iterative clustering computations as the amount of stay point data increases, thus causing an increase in overall computation time. Therefore, in this paper, we propose an algorithm to improve big data-based POI extraction speed.
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Jang, YH., Yang, SS., Park, MH., Park, SC., Kim, H. (2020). Design of POI Extraction Speed Improving Algorithm Based on Big Data. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2018 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-9341-9_5
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DOI: https://doi.org/10.1007/978-981-13-9341-9_5
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