{"_buckets": {"deposit": "df008068-325b-444b-9bf5-1bd8e33d1527"}, "_deposit": {"id": "4857", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4857"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4857", "sets": ["1597824273898", "user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Social Trends Analysis using Efficient Burst Detection in Adaptive Time Windows", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Real time monitoring of twitter tweet streamsfor trends has popularity in the last decade. Thisprovide effective information for government,business and other organization to know whathappening right now. The task comprise manychallenges including the processing of large volumeof data in real time and high levels of noise. Themain objective of this work is timely detection ofbursty trends which have happened recently anddiscovery of their evolutionary patterns along thetimeline. We present burst detection in adaptive timewindows. It is the task of finding unexpected changein some quantity in real time tweet stream. Burst ishighly depend on the sampled time window size andthreshold values. So in this work, we describe how toadjust time windows sizes and threshold values inreal time. Our experimental results show bothprocessing time is efficient and effectiveness of ourapproach."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value": []}, "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": "Khaing, Phyu Phyu"}, {"subitem_authors_fullname": "Nwe, Nwe"}]}]}, "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/123456789/754"}, "item_title": "Social Trends Analysis using Efficient Burst Detection in Adaptive Time Windows", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004857", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-11"}, "publish_date": "2019-07-11", "publish_status": "0", "recid": "4857", "relation": {}, "relation_version_is_last": true, "title": ["Social Trends Analysis using Efficient Burst Detection in Adaptive Time Windows"], "weko_shared_id": -1}