{"title":"泛在人工智能和动态数据流","authors":"A. Bifet, J. Read","doi":"10.1145/3210284.3214345","DOIUrl":null,"url":null,"abstract":"Artificial Intelligence is leading to ubiquitous sources of Big Data arriving at high-velocity and in real-time. To effectively deal with it, we need to be able to adapt to changes in the distribution of the data being produced, and we need to do it using a minimum amount of time and memory. In this paper, we detail modern applications falling into this context, and discuss some state-of-the-art methodologies in mining data streams in real-time, and the open source tools that are available to do machine learning/data mining in real-time for this challenging setting.","PeriodicalId":412438,"journal":{"name":"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Ubiquitous Artificial Intelligence and Dynamic Data Streams\",\"authors\":\"A. Bifet, J. Read\",\"doi\":\"10.1145/3210284.3214345\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial Intelligence is leading to ubiquitous sources of Big Data arriving at high-velocity and in real-time. To effectively deal with it, we need to be able to adapt to changes in the distribution of the data being produced, and we need to do it using a minimum amount of time and memory. In this paper, we detail modern applications falling into this context, and discuss some state-of-the-art methodologies in mining data streams in real-time, and the open source tools that are available to do machine learning/data mining in real-time for this challenging setting.\",\"PeriodicalId\":412438,\"journal\":{\"name\":\"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3210284.3214345\",\"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 12th ACM International Conference on Distributed and Event-based Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3210284.3214345","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ubiquitous Artificial Intelligence and Dynamic Data Streams
Artificial Intelligence is leading to ubiquitous sources of Big Data arriving at high-velocity and in real-time. To effectively deal with it, we need to be able to adapt to changes in the distribution of the data being produced, and we need to do it using a minimum amount of time and memory. In this paper, we detail modern applications falling into this context, and discuss some state-of-the-art methodologies in mining data streams in real-time, and the open source tools that are available to do machine learning/data mining in real-time for this challenging setting.