{"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}
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.