THE SEAL OF SEARCH WORDS IN MANY DESCRIPTIVE DATASETS

Sk. Arifunneesa, Y.Rajesh Babu

Abstract


Unlike the tree indicators used in existing companies, our index is less receptive when it comes to increasing dimensions and metrics with multidimensional data. The unwanted candidates are cut in line with the distances between the MBR of the points or keywords and also with the best diameter. NKS queries are useful for many applications, for example, to analyze images in social systems, search for graphics patterns, perform geographic searches in GIS systems, etc. We produce exactly as well as the approximate form of formula. In this document, we consider that objects marked with keywords are baked in a vector space. Keyword-based search in rich, multidimensional data sets helps with many new applications and tools. From these data sets, we observe the queries that request the most precise categories of points that comply with the set of confirmed keywords. Our experimental results in real and synthetic datasets reveal that ProMiSH has up to 60 times more acceleration in tree-based art techniques. We recommend a unique method known as ProMiSH that uses random index structures and random fragmentation and achieves high scalability and acceleration. We carry out extensive experimental studies to demonstrate the performance of the proposed techniques.


Keywords


Projection And Multi Scale Hashing; Querying; Multi-Dimensional Data; Indexing; Hashing

References


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Vishwakarma Singh, Bo Zong, and Ambuj K. Singh, “Nearest Keyword Set Search inMulti-Dimensional Datasets”, ieee transactions on knowledge and data engineering, vol. 28, no. 3, march 2016.


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