Increased availability of Multi-Core processors is forcing us to redesign algorithms and applications so as to exploit the available computational power from multiple cores. It is not un-common to employ memory mapping of files in applications involving huge I/O bandwidth to improve the response/service times. This paper mainly focuses on performance of memory mapped files on MultiCore processors. Experiments are carried out with k-means algorithm, a popular Data mining (DM) clustering algorithm, to explore the potential of Multi-Core hardware under OpenMP API and POSIX threads. Observations are made both with static and dynamic threads of OpenMP. Experiments are also conducted with both simulated and real data sets. Experiments indicate that memory mapping of files gives considerable benefit on MultiCore processors also. In addition, the benefit increased with increased physical memory size. Also, the benefit of memory mapping with the selected algorithm is increasing with number of cores.

Nowadays, Social media positions remain a favorite combination as clients studying to distribute their occurrences, activities on the network. These websites receive large quantities of user-supplied elements during the vast difference natural world results of various varieties, reach. An effective advertisement marketing approach is designed by using the products that contain the information, additionally client inclinations and conclusions on information about products. By this kind of similarity between concepts of profile, a precise matching method is developed to match the profile of the Web services and user. In this work, the approach is developed to search the concept similarity in the second phase of the process of query rewriting is performed after extraction queries. Similarity measure techniques are very much useful in processing database queries such as top-k queries, reverse top-k queries, k-nearest neighbor queries and other different types of queries related to trading sales activities