원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.7 No.6
2014.12
pp.223-232
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Cloud is a heterogeneous group of services and one of that is data storage. Generally the data stored in cloud is in very large amount, retrieval of the same requires precision and accuracy. Many methods are being proposed to retrieve data from cloud and strict match algorithm is one of them. In this paper we use of Levenshtein distance to find out the amount of similarity between two different strings. We compare the keyword given by user with our set of words, find out similarity and provide guesses to the user as accurate as possible.
목차
Abstract
1. Introduction
2. Literature Survey
3. Analysis of Existing Techniques
3.1 Ranked Keyword Search
3.2 Traditional Keyword Search
4. Motivation
5. Architecture of System
5.1. Creating database for the medical words
5.2. Creating ontology for the above database
5.3. GUI for user to search a keyword
5.4. Algorithm to find the word
5.5 Algorithm to find similar words
6. Mechanism Adopted and Tools Used
7. Results and Discussions
7.1 Lightweight ontology for the patients, doctors and nurse tables and their relations.
7.2 The sample database we created for different patients with their details like blood group, date of birth, diseases they are suffering from and doctors assigned to them.
7.3 In HTML page, we take input from user like patients name and retrieve all the data.
7.4 We use Levenstein’s Distance algorithm to check the similarity between 2 strings.
7.5 Performance statistics
8. Conclusion
References
1. Introduction
2. Literature Survey
3. Analysis of Existing Techniques
3.1 Ranked Keyword Search
3.2 Traditional Keyword Search
4. Motivation
5. Architecture of System
5.1. Creating database for the medical words
5.2. Creating ontology for the above database
5.3. GUI for user to search a keyword
5.4. Algorithm to find the word
5.5 Algorithm to find similar words
6. Mechanism Adopted and Tools Used
7. Results and Discussions
7.1 Lightweight ontology for the patients, doctors and nurse tables and their relations.
7.2 The sample database we created for different patients with their details like blood group, date of birth, diseases they are suffering from and doctors assigned to them.
7.3 In HTML page, we take input from user like patients name and retrieve all the data.
7.4 We use Levenstein’s Distance algorithm to check the similarity between 2 strings.
7.5 Performance statistics
8. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보