{"title":"通过相关领域分析发现应用中的异常和根本原因","authors":"Yuchen Zhao, Arjun Iyer, Ariel Smoliar","doi":"10.1109/ICDMW.2015.68","DOIUrl":null,"url":null,"abstract":"In this paper, we present a powerful end-to-end data mining system that collects application related data and provides insightful relevant fields analysis in addition to search and filtering. We present details on field extraction, indexing, relevant field processing and dynamic baseline derivation. We also propose to demonstrate the effectiveness of various scoring algorithms. Two real-world use cases show relevant fields analysis is effective to detect application anomalies and discover root causes of application incidents.","PeriodicalId":192888,"journal":{"name":"2015 IEEE International Conference on Data Mining Workshop (ICDMW)","volume":"18 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Discovering Anomalies and Root Causes in Applications via Relevant Fields Analysis\",\"authors\":\"Yuchen Zhao, Arjun Iyer, Ariel Smoliar\",\"doi\":\"10.1109/ICDMW.2015.68\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a powerful end-to-end data mining system that collects application related data and provides insightful relevant fields analysis in addition to search and filtering. We present details on field extraction, indexing, relevant field processing and dynamic baseline derivation. We also propose to demonstrate the effectiveness of various scoring algorithms. Two real-world use cases show relevant fields analysis is effective to detect application anomalies and discover root causes of application incidents.\",\"PeriodicalId\":192888,\"journal\":{\"name\":\"2015 IEEE International Conference on Data Mining Workshop (ICDMW)\",\"volume\":\"18 2\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Data Mining Workshop (ICDMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW.2015.68\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Data Mining Workshop (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2015.68","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Discovering Anomalies and Root Causes in Applications via Relevant Fields Analysis
In this paper, we present a powerful end-to-end data mining system that collects application related data and provides insightful relevant fields analysis in addition to search and filtering. We present details on field extraction, indexing, relevant field processing and dynamic baseline derivation. We also propose to demonstrate the effectiveness of various scoring algorithms. Two real-world use cases show relevant fields analysis is effective to detect application anomalies and discover root causes of application incidents.