String Search Approximation on Spatial Data through a Novel Approach

In several applications finding objects closest to a specific location that contains a set of keywords. For example, online yellow pages allows user to specify an address and a set of keywords. In response, the user obtains a list of related information whose description contains these keywords and it’s ordering according to their distance from the specified address. The complexities involved in nearest neighbor search on spatial data and keyword search on text data have been extensively studied individually. To the best of our knowledge there is no fully efficient method to answer spatial keyword queries exclusively, where the queries specify both the location and a set of keywords. Here the survey is done on current techniques to cope with the problem of string matching that allow errors. In many of the fast rising areas such as information retrieval and computational biology this is becoming a more and more important issue. In this study main focus is kept on spatial string searching and mostly on edit distance, its statistical behavior, its history and current developments, and the central ideas of the techniques and their complexities. The main objective of this survey is to present an overview of the new and efficient approach in approximating string search.