Normalised LCS-based method for indexing multidimensional data cube. (1st January 2013)

• Record Type: Journal Article
• Authors: Sharma, Mayank; Rajpal, Navin; Reddy, B.V. Ramana; Purwar, Ravindra Kumar
• Is Part Of: International journal of intelligent information and database systems. Volume 7:Number 2(2013)
• Page Start: 180
• Page End: 204
• Publication Date: 2013-01-01
• Subjects: longest common subsequence -- LCS -- feature distance -- string approximation; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

Query processors fail to retrieve information of interest in the presence of misspelled keywords given by users. The above problem persists because most of currently used indexing system does not have fault-tolerance ability to map the misspelled keywords to the correct records stored at physical level of databases. Therefore, the information retrieval systems need additional support of spell check mechanism with limitations for correction of misspelled keywords before submitting them to query processors. In this paper, a data indexing system is introduced for indexing multidimensional data cube, which maps the keywords to the records stored at physical level in multidimensional data structure and also has normalised longest common subsequence-based string approximation method to find correct keywords against misspelled keywords which comes directly to indexing processes through user queries. It provides more than 90% accurate results in mapping misspelled keywords to the physically stored records. These results are consistent even for large datasets.