Abstract
Discovery of sequential patterns in large transaction databases for personalized services is gaining importance in several industries. Although a huge amount of mobile location data of consumers is available with the service providers, it is hardly put to use owing its complexity and size. To facilitate this, an approach that represents the entire area by a location grid and records the movements across the cells as sequences has been proposed. A new algorithm for mining sequential data is devised to find frequent travel patterns from location data and analyze user travel patterns. The algorithm is asymmetric in nature and is parallelized on the GPGPU processor and tested for performance. Our experiments assert that asymmetric nature of the algorithm doesn’t allow the performance to elevate despite parallelization, especially with large data.
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Upadhyaya, S.R., Toth, D. (2012). An Experiment with Asymmetric Algorithm: CPU Vs. GPU. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29035-0_21
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DOI: https://doi.org/10.1007/978-3-642-29035-0_21
Publisher Name: Springer, Berlin, Heidelberg
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