Microscopic modelling circadian and bursty pattern of human activities
Recent studies for a wide range of human activities such as email communication, Web browsing, and library visiting, have revealed the bursty nature of human activities. The distribution of inter-event times (IETs) between two consecutive human activities exhibits a heavy-tailed decay behavior and t...
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| description | Recent studies for a wide range of human activities such as email communication, Web browsing, and library visiting, have revealed the bursty nature of human activities. The distribution of inter-event times (IETs) between two consecutive human activities exhibits a heavy-tailed decay behavior and the oscillating pattern with a one-day period, reflective of the circadian pattern of human life. Even though a priority-based queueing model was successful as a basic model for understanding the heavy-tailed behavior, it ignored important ingredients, such as the diversity of individual activities and the circadian pattern of human life. Here, we collect a large scale of dataset which contains individuals' time stamps when articles are posted on blog posts, and based on which we construct a theoretical model which can take into account of both ignored ingredients. Once we identify active and inactive time intervals of individuals and remove the inactive time interval, thereby constructing an ad hoc continuous time domain. Therein, the priority-based queueing model is applied by adjusting the arrival and the execution rates of tasks by comparing them with the activity data of individuals. Then, the obtained results are transferred back to the real-time domain, which produces the oscillating and heavy-tailed IET distribution. This microscopic model enables us to develop theoretical understanding towards more empirical results. |
| doi_str_mv | 10.1371/journal.pone.0058292 |
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The distribution of inter-event times (IETs) between two consecutive human activities exhibits a heavy-tailed decay behavior and the oscillating pattern with a one-day period, reflective of the circadian pattern of human life. Even though a priority-based queueing model was successful as a basic model for understanding the heavy-tailed behavior, it ignored important ingredients, such as the diversity of individual activities and the circadian pattern of human life. Here, we collect a large scale of dataset which contains individuals' time stamps when articles are posted on blog posts, and based on which we construct a theoretical model which can take into account of both ignored ingredients. Once we identify active and inactive time intervals of individuals and remove the inactive time interval, thereby constructing an ad hoc continuous time domain. Therein, the priority-based queueing model is applied by adjusting the arrival and the execution rates of tasks by comparing them with the activity data of individuals. Then, the obtained results are transferred back to the real-time domain, which produces the oscillating and heavy-tailed IET distribution. This microscopic model enables us to develop theoretical understanding towards more empirical results.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0058292</identifier><identifier>PMID: 23505479</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Behavior ; Browsing ; Circadian Rhythm - physiology ; Circadian rhythms ; Communication ; Datasets ; Fourier transforms ; Human Activities ; Human behavior ; Humans ; Ingredients ; Medicine ; Models, Theoretical ; Physics ; Queues ; Queuing theory ; Social and Behavioral Sciences ; Time domain analysis</subject><ispartof>PloS one, 2013-03, Vol.8 (3), p.e58292-e58292</ispartof><rights>COPYRIGHT 2013 Public Library of Science</rights><rights>2013 Kim et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Jinhong</au><au>Lee, Deokjae</au><au>Kahng, Byungnam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Microscopic modelling circadian and bursty pattern of human activities</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2013-03-11</date><risdate>2013</risdate><volume>8</volume><issue>3</issue><spage>e58292</spage><epage>e58292</epage><pages>e58292-e58292</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Recent studies for a wide range of human activities such as email communication, Web browsing, and library visiting, have revealed the bursty nature of human activities. The distribution of inter-event times (IETs) between two consecutive human activities exhibits a heavy-tailed decay behavior and the oscillating pattern with a one-day period, reflective of the circadian pattern of human life. Even though a priority-based queueing model was successful as a basic model for understanding the heavy-tailed behavior, it ignored important ingredients, such as the diversity of individual activities and the circadian pattern of human life. Here, we collect a large scale of dataset which contains individuals' time stamps when articles are posted on blog posts, and based on which we construct a theoretical model which can take into account of both ignored ingredients. Once we identify active and inactive time intervals of individuals and remove the inactive time interval, thereby constructing an ad hoc continuous time domain. Therein, the priority-based queueing model is applied by adjusting the arrival and the execution rates of tasks by comparing them with the activity data of individuals. Then, the obtained results are transferred back to the real-time domain, which produces the oscillating and heavy-tailed IET distribution. This microscopic model enables us to develop theoretical understanding towards more empirical results.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>23505479</pmid><doi>10.1371/journal.pone.0058292</doi><tpages>e58292</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Behavior Browsing Circadian Rhythm - physiology Circadian rhythms Communication Datasets Fourier transforms Human Activities Human behavior Humans Ingredients Medicine Models, Theoretical Physics Queues Queuing theory Social and Behavioral Sciences Time domain analysis |
| title | Microscopic modelling circadian and bursty pattern of human activities |
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