Reduced Time Compression in Big Data Using MapReduce Approach and Hadoop.
An exponential rise has been observed in the data volume over the time when considering a real time environment. A phenomenal feature termed as 'Predictability' helps in predicting and portraying related data to the user according to their needs. Moreover, classification of Big Data is usually a ted...
| Published in: | Journal of Medical Systems Vol. 43; no. 8 |
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| Main Authors: | , |
| Format: | computer program equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137490030&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137490030 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2019 vid: 43 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137490030 137490030 137490030 10.1007/s10916-019-1369-3 137490030 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Reduced Time Compression in Big Data Using MapReduce Approach and Hadoop. aug: au: Meena, K. Sujatha, J. affil: Department of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India sug: subj: Data Analytics Methods Computing Methodologies Data Management Methods Time Factors Climate Agriculture Data Mining Cluster Analysis Algorithms ab: An exponential rise has been observed in the data volume over the time when considering a real time environment. A phenomenal feature termed as 'Predictability' helps in predicting and portraying related data to the user according to their needs. Moreover, classification of Big Data is usually a tedious and lengthy task. The technique of MapReduce Framework performs the data processing that being paralleled by data distribution in small chunks through the clusters. This Map Reduce technique is being proposed which is employed to process heterogeneous data items. Few issues that are being targeted in the existing paper include associating climatologically and meteorological information with large variety of farming decisions. Using the well-known MapReduce framework the above issues and challenges can be resolved. The existing paper proposes empirical techniques of climate classification and prediction by adopting Co-EANFS (Co-Effective and Adaptive Neuro-Fuzzy System) approach for data handling. Furthermore, the paper examines association rule mining too, which is being implemented for examining the best crop production by relying upon the soil and weather condition. Lastly, a technique is proposed for managing various levels such as preprocessing, clustering, classification and prediction. First, the weather dataset is being collected which undergoes processing; thereafter the proposed model is implemented which results in formation of cluster data sets linked to each season. For evaluating the performance, accuracy predictions generated by Co-EANFS is used which being formulated with varying no: of inputs and variables. The proposed framework acquires least execution time. pubtype: Academic Journal doctype: computer program equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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