Nowadays, data analytics in spatial database objects are associated with keywords. In the past decade, searching the keyword was a major focusing and active area to the researchers within the database server and information retrieval community in various applications. In recent years, the maximising the availability and ranking the most frequent keyword items evaluation in the spatial database are used to make the decision better. This motivates to carry out research towards of closest keyword cover search, which is also known as fine tuned keyword cover search methodology; it considers both inter object distance and keyword ranking of items in the spatial environment. Baseline algorithm derived in this area has its own drawbacks. While searching the keyword increases, the query result performance can be minimised gradually by generating the candidate keyword cover. To resolve this problem a new scalable methodology can be proposed in this paper. Copyright © 2020 Inderscience Enterprises Ltd.