Efficient Sort Search on Massive Data

Abstract

Efficient top-N retrieval of records from a database has been an active research field for many years. The problem from a real world application point of view has the order of records according to some similarity function on an attribute is not unique. Many researchers have same values in several attributes and thus their ranking in those attributes is arbitrary (based on random choice).For instance, in large person databases many individuals have the same first name, the same date of birth, or live in the same city. Existing algorithms are ill-equipped to handle such cases efficiently. We introduce a Dynamic TMS searcher, which retrieves larger chunks of records from the sorted lists using fixed limits, and which focuses its efforts on records that are ranked high in more than one ordering and thus are more promising candidates. We experimentally show that our method outperforms Dynamic Sorting Algorithm (DSA) for top-k retrieval in those very common cases where we used with dynamically scheduling the resources based on the data which are provided with , this efficient short search algorithm along with the massive data retrieval on a very fine tuple data's can be of a different dataset. Here in this project we are going to use these logics for the need of solution in the field of medical research, where there are many manageable databases that are been used in a common path for the end of healthy need and the retrieval of solution for the cause of illness to a human being.