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RISS 인기검색어
- 검색키워드
- 저자 : Kavita K. Beldar (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Query Evaluation on Probabilistic Databases Using Indexing and MapReduce
Kavita K. Beldar,M. D. Gayakwad,Debnath Bhattacharyya,Hye-jin Kim 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.10
Entity resolution technique is used for recognize the duplicate tuples which signify similar real world entities. Existing resolution technique is unable to solve the problems of higher level of heterogeneity and additional continual data alteration. Working on this type of database, there is necessitated to enumerate the integrity of data. The new approach is introduced here on probabilistic databases by unmerged duplicates for processing complex queries. This is achieved by using probabilistic databases. For competent access toward entity resolution data over a large collection of possible resolution worlds, new indexing technique is presented here. Also, a computation of query processing is reduced by using indexing structure. The focus is on set similarity relation on very big probabilistic database by using MapReduce technique. MapReduce is a popular paradigm that can process large volume data more efficiently. In this paper, different approaches proposed using MapReduce to deal with this task: 1. merge data set with MapReduce and merge data set without MapReduce, 2. Merge data set with MapReduce using Hadoop. This approaches implemented on windows and Hadoop framework and performed compressing experiments to their performances. Also the speedup ratio for both is tested.
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A Comparative Analysis on Contingence Structured Data Methodologies
Kavita K. Beldar,M. D. Gayakwad,Debnath Bhattacharyya,Tai-hoon Kim 보안공학연구지원센터 2016 International Journal of Software Engineering and Vol.10 No.5
Heterogeneous structured datas give rise to different kind of information caliber issues regarding real-world structured datas. Identical records also a one major issue. Strategy to eliminate identical records results in unsureness to select among true uniform records. Available methods based on expert observation and destructive decisions do not proved effective solution to such problems. This project solves these issues of identical records elimination will solve by de-duplication procedure as data accessing tasks with unsure outcomes. This project implements method to overcome unsureness of identical records that tightly conceal the proper instances of input and gives effective results for identical record.
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