TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Optimization Driven MapReduce Framework for Indexing and Retrieval of Big Data |
| 영문제목(English Title) |
Optimization Driven MapReduce Framework for Indexing and Retrieval of Big Data |
| 저자(Author) |
Hemn Barzan Abdalla
Awder Mohammed Ahmed
M. A. Al Sibahee
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| 원문수록처(Citation) |
VOL 14 NO. 05 PP. 1886 ~ 1908 (2020. 05) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively
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| 키워드(Keyword) |
Big data indexing
MapReduce
Sparse FCM
Retrieval
Data clustering
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| 파일첨부 |
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