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of Computer Engineering in Research Trends (IJCERT)

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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Query Aware Determinization of Uncertain Objects

P.Jhancy, K.Lakshmi , Dr.S.Prem Kumar, ,
Affiliations
Pursuing M.Tech, CSE Branch, Dept of CSE
Assistant Professor, Department of Computer Science and Engineering</br>Professor & HOD, Department of computer science and engineering, G.Pullaiah College of Engineering and Technology, Kurnool, Andhra Pradesh, India.
:NOT ASSIGNED


Abstract
The main aim of this paper is to think about the trouble of determining probabilistic data to allow such data to be stored in legacy systems that agree only deterministic input. Probabilistic data may be produced by mechanized data analysis methods such as entity resolution, information extraction, and speech processing etc. The target is to make a deterministic depiction of probabilistic data that optimizes the excellence of the end-application built on deterministic data. We discover such a determinization problem in the background of two dissimilar data processing jobs – selection and triggers queries. Here approaches such as thresholding or top-1 selection usually used for determinization lead to suboptimal presentation for such applications. As an alternative, we expand a query-aware strategy and demonstrate its rewards over existing solutions through a complete empirical evaluation over real and synthetic datasets.


Citation
P.Jhancy,K.Lakshmi,Dr.S.Prem Kumar."Query Aware Determinization of Uncertain Objects". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.2, Issue 12,pp.904-907, December - 2015, URL :https://ijcert.org/ems/ijcert_papers/V2I1217.pdf,


Keywords : uncertain data, query workload, data quality, branch and bound algorithm.

References
[1] D. V. Kalashnikov, S. Mehrotra, J. Xu, and N. Venkatasubramanian, “A semantics-based approach for speech annotation of images,” TKDE’11. 
[2] J. Li and J. Wang, “Automatic linguistic indexing of pictures by a statistical modeling approach,” PAMI’03. 
[3] C. Wangand, F. Jing, L. Zhang, and H. Zhang, “Image annotation refinement using random walk with restarts,” ACM Multimedia’06. 
[4] B. Minescu, G. Damnati, F. Bechet, and R. de Mori, “Conditional use of word lattices, confusion networks and 1-best string hypotheses in a sequential interpretation strategy,” ICASSP’07.
 [5] R. Nuray-Turan, D. V. Kalashnikov, S. Mehrotra, and Y. Yu, “Attribute and object selection queries on objects with probabilistic attributes,” ACM TODS’11. 
[6] J. Li and A. Deshpande, “Consensus answers for queries over proba-bilistic databases,” PODS’09. 
[7] M. B. Ebarhimi and A. A. Ghorbani, “A novel approach for frequent phrase mining in web search engine query streams,” CNSR ’07.
 [8] S. Bhatia, D. Majumdar, and P. Mitra, “Query suggestions in the absence of query logs,” SIGIR ’11. 
[9] C. Manning and H. Schutze, Foundations of Statistical Natural Lan-guage Processing. MIT Press, 1999. 
[10] D. V. Kalashnikov and S. Mehrotra, “Domainindependent data cleaning via analysis of entityrelationship graph,” ACM TODS’06. 
[11] K. Schnaitter, S. Abiteboul, T. Milo, and N. Polyzotis, “On-line index selection for shifting workloads,” SMDB’07.
 [12] P. Unterbrunner, G. Giannikis, G. Alonso, D. Fauser, and D. Kossmann, “Predictable performance for unpredictable workloads,” VLDB’09. 
[13] R. Cheng, J. Chen, and X. Xie, “Cleaning uncertain data with quality guarantees,” PVLDB’08. V. Jojic, S. Gould, and D. Koller, “Accelerated dual decomposition for map inference,” ICML ’1


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DOI:10.22362/ijcert