A Review of Best Keyword Cover Search

Abstract

Spatial database stores the information about the spatial objects which are associated with the keywords to indicate the information such as its business/services/features. None of the individual objects is associated with all query keywords, this motivates studies to retrieve multiple objects, called keyword cover, which together cover all query keywords and are close to each other. In m closest keyword search, it covers a set of query keywords and minimum distance between objects. From last few years, keyword rating increases its availability and importance in object evaluation for the decision making. This is the main reason for developing the new algorithm called best keyword cover which is consider inter-distance as well as the keyword rating provided by the customers through the online business. m closest keyword search algorithm combines the objects from different query keywords to generate candidate keyword covers. Baseline algorithm and keyword nearest neighbor expansion algorithms are used to find the best keyword cover. The performance of the m closest keyword algorithm drops dramatically, when the number of query keyword increases. This work proposes to solve generic version problem of the existing algorithm called keyword nearest neighbor expansion which reduces the resulted candidate keyword covers.