Uncertain Tagging of Images using Query Aware Determinization

Ms.Komal R. Ghuge

Abstract


Determinizing probabilistic data is the problem that can be solved by enable such data to be stored in legacy system. legacy system is used to store the data and that system accepts only deterministic input. Probabilistic data may be generated by automated data analysis/enrichment technique such as information extraction. The goal is to generate a deterministic representation of probabilistic data to optimize the quality of the end-application that are built on deterministic data. To explore such a determinization problem in the context of two different data processing tasks such as triggers and selection queries. Some approaches such as thresholding or top-1 selection traditionally used for determinization lead to suboptimal performance for such applications. Instead, develop a query-aware strategy and show its advantages over existing solutions through a comprehensive empirical evaluation over real and synthetic datasets. The problem of determinizing probabilis- tic data is solved by workload of triggers/queries, and by finding the deterministic representation of the data that optimize certain qual- ity metrics of the answer to these triggers/queries. System solves the problem of determinization by minimizing the expected cost of the answer to queries. Efficient algorithm is used to optimize set-based quality metrics, such as F-measure. Correlations among tags can be leveraged in solutions to get better results.

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