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dc.contributor.advisor Premaratne, SC
dc.contributor.author Wijayasekara, GO
dc.date.accessioned 2017-11-28T16:48:43Z
dc.date.available 2017-11-28T16:48:43Z
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/12914
dc.description.abstract Information plays a significant role in our day to day life. Information affects how we look at things and make decisions. Therefore, accurate information must be readily available. With technology reaching new heights, in modern day systems and applications, the main source for information storage is an underlying database system. Retrieving information from a database management system requires a specific expert skill set which predominantly includes knowledge on Structured Query Language and domain specific knowledge. In recent times there is a rising demand for non-expert users to be able to directly extract information from an underlying database management system. For industry specific Customer Relationship Management applications like the Customer Relationship Index used in large organizations like the Brandix Group this is seen as both a challenge and an opportunity to be explored. Implementation of a Natural Language Interface to the underlying database of the Customer Relationship Index allows a typical user to retrieve the required information from the underlying database using natural language like English without prior programming or technical knowledge. For above reasons the NLIDB converter for the Customer Relationship Index is introduced. This application takes the users query to be run against the underlying database in natural language and returns the corresponding T-SQL statement which can then be run against the database to extract results. The NLIDB converter has shown that it can successfully and efficiently convert questions in natural language to the corresponding T-SQL tatement with an accuracy rate of more than 80% for two types of output T-SQL statement formats for the Customer Relationship Index database. en_US
dc.language.iso en en_US
dc.title NLIDB converter for customer relationship index en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty IT en_US
dc.identifier.degree MSc in Information Technology. en_US
dc.identifier.department Department of Information Technology en_US
dc.date.accept 2017-05
dc.identifier.accno TH3394 en_US


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