k-Factor-Based Cosine Similarity Measurement

Siddiqui, Nadia; Islam, Saiful

doi:10.1007/978-981-13-1747-7_63

Cited by 2 publications

(1 citation statement)

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“…The input user query is sent to the reducer through the similarity measure and thereby, the query is sent to the mapper based on the distances. The distance between the query and representative data/cluster centre is computed using PESM [13], cosine similarity measurement [29] and Jaya–SCA-based similarity measure. Hence, the similarity measure is expressed as…”

Section: Mapreduce Framework For Data Indexing and Retrieval Based On...mentioning

confidence: 99%

Efficient indexing and retrieval of patient information from the big data using MapReduce framework and optimisation

Merlin¹,

M²

2021

Journal of Information Science

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Large and complex data becomes a valuable resource in biomedical discovery, which is highly facilitated to increase the scientific resources for retrieving the helpful information. However, indexing and retrieving the patient information from the disparate source of big data is challenging in biomedical research. Indexing and retrieving the patient information from big data is performed using the MapReduce framework. In this research, the indexing and retrieval of information are performed using the proposed Jaya-Sine Cosine Algorithm (Jaya–SCA)-based MapReduce framework. Initially, the input big data is forwarded to the mapper randomly. The average of each mapper data is calculated, and these data are forwarded to the reducer, where the representative data are stored. For each user query, the input query is matched with the reducer, and thereby, it switches over to the mapper for retrieving the matched best result. The bilevel matching is performed while retrieving the data from the mapper based on the distance between the query. The similarity measure is computed based on the parametric-enabled similarity measure (PESM), cosine similarity and the proposed Jaya–SCA, which is the integration of the Jaya algorithm and the SCA. Moreover, the proposed Jaya–SCA algorithm attained the maximum value of F-measure, recall and precision of 0.5323, 0.4400 and 0.6867, respectively, using the StatLog Heart Disease dataset.

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Section: Mapreduce Framework For Data Indexing and Retrieval Based On...mentioning

confidence: 99%