发现时空事件序列

Berkay Aydin, R. Angryk
{"title":"发现时空事件序列","authors":"Berkay Aydin, R. Angryk","doi":"10.1145/3004725.3004735","DOIUrl":null,"url":null,"abstract":"Spatiotemporal event sequences represent the sequences of event types whose spatiotemporal instances frequently follow each other in spatiotemporal context. In this work, we present spatiotemporal event sequence mining from spatio-temporal event datasets that contains evolving region trajectories. We propose two algorithms for discovering spatio-temporal event sequences. We formally define a flexible spatiotemporal follow relationship, introduce various data models for capturing the sequence forming behavior. Lastly, we present an extended experimental evaluation that demonstrates the computational efficiency of our algorithms.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Discovering spatiotemporal event sequences\",\"authors\":\"Berkay Aydin, R. Angryk\",\"doi\":\"10.1145/3004725.3004735\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Spatiotemporal event sequences represent the sequences of event types whose spatiotemporal instances frequently follow each other in spatiotemporal context. In this work, we present spatiotemporal event sequence mining from spatio-temporal event datasets that contains evolving region trajectories. We propose two algorithms for discovering spatio-temporal event sequences. We formally define a flexible spatiotemporal follow relationship, introduce various data models for capturing the sequence forming behavior. Lastly, we present an extended experimental evaluation that demonstrates the computational efficiency of our algorithms.\",\"PeriodicalId\":154980,\"journal\":{\"name\":\"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems\",\"volume\":\"72 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3004725.3004735\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3004725.3004735","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

时空事件序列是指事件类型的序列,这些事件的时空实例在时空背景下频繁地相互关联。在这项工作中,我们提出了从包含不断发展的区域轨迹的时空事件数据集中挖掘时空事件序列的方法。我们提出了两种发现时空事件序列的算法。我们正式定义了一个灵活的时空跟随关系,引入了各种数据模型来捕捉序列形成行为。最后,我们提出了一个扩展的实验评估,证明了我们的算法的计算效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discovering spatiotemporal event sequences
Spatiotemporal event sequences represent the sequences of event types whose spatiotemporal instances frequently follow each other in spatiotemporal context. In this work, we present spatiotemporal event sequence mining from spatio-temporal event datasets that contains evolving region trajectories. We propose two algorithms for discovering spatio-temporal event sequences. We formally define a flexible spatiotemporal follow relationship, introduce various data models for capturing the sequence forming behavior. Lastly, we present an extended experimental evaluation that demonstrates the computational efficiency of our algorithms.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信