{"title":"webSPADE:用于分析web日志数据的并行序列挖掘算法","authors":"A. Demiriz","doi":"10.1109/ICDM.2002.1184046","DOIUrl":null,"url":null,"abstract":"Enterprise-class web sites receive a large amount of traffic, from both registered and anonymous users. Data warehouses are built to store and help analyze the click streams within this traffic to provide companies with valuable insights into the behavior of their customers. This article proposes a parallel sequence mining algorithm, webSPADE, to analyze the click streams found in site web logs. In this process, raw web logs are first cleaned and inserted into a data warehouse. The click streams are then mined by webSPADE. An innovative web-based front-end is used to visualize and query the sequence mining results. The webSPADE algorithm is currently used by Verizon to analyze the daily traffic of the Verizon.com web site.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"webSPADE: a parallel sequence mining algorithm to analyze web log data\",\"authors\":\"A. Demiriz\",\"doi\":\"10.1109/ICDM.2002.1184046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Enterprise-class web sites receive a large amount of traffic, from both registered and anonymous users. Data warehouses are built to store and help analyze the click streams within this traffic to provide companies with valuable insights into the behavior of their customers. This article proposes a parallel sequence mining algorithm, webSPADE, to analyze the click streams found in site web logs. In this process, raw web logs are first cleaned and inserted into a data warehouse. The click streams are then mined by webSPADE. An innovative web-based front-end is used to visualize and query the sequence mining results. The webSPADE algorithm is currently used by Verizon to analyze the daily traffic of the Verizon.com web site.\",\"PeriodicalId\":405340,\"journal\":{\"name\":\"2002 IEEE International Conference on Data Mining, 2002. Proceedings.\",\"volume\":\"64 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2002 IEEE International Conference on Data Mining, 2002. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDM.2002.1184046\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDM.2002.1184046","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
webSPADE: a parallel sequence mining algorithm to analyze web log data
Enterprise-class web sites receive a large amount of traffic, from both registered and anonymous users. Data warehouses are built to store and help analyze the click streams within this traffic to provide companies with valuable insights into the behavior of their customers. This article proposes a parallel sequence mining algorithm, webSPADE, to analyze the click streams found in site web logs. In this process, raw web logs are first cleaned and inserted into a data warehouse. The click streams are then mined by webSPADE. An innovative web-based front-end is used to visualize and query the sequence mining results. The webSPADE algorithm is currently used by Verizon to analyze the daily traffic of the Verizon.com web site.
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