Cloud computing is an emerging technology where computing resources are delivered as a service over a network which is accessed by many cloud users. Cloud services on the real-world application attain the fundamental resource sharing and low-cost preserving characteristics. While increasing the number of user requests, the most significant deal is the identification and retrieval of top-k queries in cloud environments. Several techniques have been developed to retrieve the top-k queries, but effective modeling of query result retrieval on cloud services with less complexity is not attained. In order to improve the query result retrieval rate, Top-k Query Multi-Keyword Score Threshold (Top-k QMKST) technique is developed. This technique considers four processes for retrieving the top-k results in minimum time. At first, multiple keywords are extracted from the query, and then the B+ tree indexing is used to index the data with the objective of reducing the response time and space complexity. Third, a score value is calculated using Kullback–Leibler Divergence which provides the probable results of keywords occurrences among a collection of keywords in an index list. At last Monotonic weighted score aggregation function is used for assigning the weight to the resultant content score. Experimental evaluation is carried out with different parameters and the results showed that the Top-k QMKST technique is better in case of query result retrieval with minimum false positive rate, reduced response time and space complexity.
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Lekshmi, K.K., Prem, M.V. Multi-keyword score threshold and B+ tree indexing based top-K query retrieval in cloud. Peer-to-Peer Netw. Appl. 13, 1990–2000 (2020). https://doi.org/10.1007/s12083-019-00794-4
- Cloud computing
- Top-k query retrieval
- B+ tree index
- Kullback–Leibler divergence
- Score threshold
- Monotonic weighted score aggregation function