MERAL Myanmar Education Research and Learning Portal
Item
{"_buckets": {"deposit": "b0e1fe5c-7cdf-4810-a191-f8154acc9b0b"}, "_deposit": {"id": "4779", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4779"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4779", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Big Data Analytics for Rainfall Prediction using MapReduce-Based Regression Model", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "The most significant climatic element whichimpacts on agriculture sector is rainfall and rainfallprediction becomes an important issue in agriculturecountry like Myanmar. Collecting, storing andprocessing of huge amount of climatic data (BigData) require high-performance analytical systemsrunning on distributed environments for accurateprediction of weather. Traditional standard dataanalytics algorithms need to be adapted to takeadvantage of cloud computing models which providescalability and flexibility. In this paper, MultipleLinear Regression which is an empirical, statisticaland mathematically mature method in data analysisis applied in Rainfall Prediction. To proveconventional Multiple Linear Regression workefficiently in distributed environments, we propose aparallel processing of Regression Model calledMapReduce-based Multiple Linear Regression (MRMLR).Weekly Rainfall Prediction with the proposedregression model using large scale weather data willbase on the QR Decomposition and Ordinary LeastSquares method adapted to MapReduce Framework.Correlation-based Filter Approach by usingSymmetrical Uncertainty (SU) will be applied inselecting correlated and relevant features forimproving the proposed regression model’sprediction accuracy."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Big data"}, {"interim": "Rainfall Prediction"}, {"interim": "Multiple Linear Regression"}, {"interim": "QR Decomposition"}, {"interim": "Ordinary Least Squares"}, {"interim": "Symmetrical Uncertainty"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value": []}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Fifteenth International Conference on Computer Applications(ICCA 2017)", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Khine, Kyi Lai Lai"}, {"subitem_authors_fullname": "Nyunt, Thi Thi Soe"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2017-02-16"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/669"}, "item_title": "Big Data Analytics for Rainfall Prediction using MapReduce-Based Regression Model", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004779", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-11"}, "publish_date": "2019-07-11", "publish_status": "0", "recid": "4779", "relation": {}, "relation_version_is_last": true, "title": ["Big Data Analytics for Rainfall Prediction using MapReduce-Based Regression Model"], "weko_shared_id": -1}
Big Data Analytics for Rainfall Prediction using MapReduce-Based Regression Model
http://hdl.handle.net/20.500.12678/0000004779
http://hdl.handle.net/20.500.12678/000000477997af947f-6959-480d-af1b-e7fd5bcce87b
b0e1fe5c-7cdf-4810-a191-f8154acc9b0b
Publication type | ||||||
---|---|---|---|---|---|---|
Article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Big Data Analytics for Rainfall Prediction using MapReduce-Based Regression Model | |||||
Language | en | |||||
Publication date | 2017-02-16 | |||||
Authors | ||||||
Khine, Kyi Lai Lai | ||||||
Nyunt, Thi Thi Soe | ||||||
Description | ||||||
The most significant climatic element whichimpacts on agriculture sector is rainfall and rainfallprediction becomes an important issue in agriculturecountry like Myanmar. Collecting, storing andprocessing of huge amount of climatic data (BigData) require high-performance analytical systemsrunning on distributed environments for accurateprediction of weather. Traditional standard dataanalytics algorithms need to be adapted to takeadvantage of cloud computing models which providescalability and flexibility. In this paper, MultipleLinear Regression which is an empirical, statisticaland mathematically mature method in data analysisis applied in Rainfall Prediction. To proveconventional Multiple Linear Regression workefficiently in distributed environments, we propose aparallel processing of Regression Model calledMapReduce-based Multiple Linear Regression (MRMLR).Weekly Rainfall Prediction with the proposedregression model using large scale weather data willbase on the QR Decomposition and Ordinary LeastSquares method adapted to MapReduce Framework.Correlation-based Filter Approach by usingSymmetrical Uncertainty (SU) will be applied inselecting correlated and relevant features forimproving the proposed regression model’sprediction accuracy. | ||||||
Keywords | ||||||
Big data, Rainfall Prediction, Multiple Linear Regression, QR Decomposition, Ordinary Least Squares, Symmetrical Uncertainty | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/669 | |||||
Journal articles | ||||||
Fifteenth International Conference on Computer Applications(ICCA 2017) | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |