Fast matrix multiplies using graphics hardware
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
Fast and approximate stream mining of quantiles and frequencies using graphics processors
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
GPUTeraSort: high performance graphics co-processor sorting for large database management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
Frequent itemset mining on graphics processors
Proceedings of the Fifth International Workshop on Data Management on New Hardware
Scalable clustering using graphics processors
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
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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.