{"title":"Hive、Spark-Sql、Flink-Sql在IVR数据分析中的性能比较","authors":"R. Kaur, Raman Chadha","doi":"10.9790/0661-1903040611","DOIUrl":null,"url":null,"abstract":"Companies that utilize automated IVR systems have a veritable treasure trove of data that can be analyzed to improve the quality of the customer experience.After all, many customers who are greeted by linear thinking IVR systems instead of human voices already assume that their self-service experience is going to be less than favorable.Analyse the call Centre Performance includes various parameters like Cross-Team Visibility, Monitor Interactions in Real Time, Simplify reporting, Evaluate and streamline journeys etc. This paper focus on an approach in which IVR data is analysed and comparison is done based on HIVE, SPARK and FLINK frameworks.","PeriodicalId":91890,"journal":{"name":"IOSR journal of computer engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performance Comparison between Hive, Spark-Sql & Flink-Sql through IVR Data Analysis\",\"authors\":\"R. Kaur, Raman Chadha\",\"doi\":\"10.9790/0661-1903040611\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Companies that utilize automated IVR systems have a veritable treasure trove of data that can be analyzed to improve the quality of the customer experience.After all, many customers who are greeted by linear thinking IVR systems instead of human voices already assume that their self-service experience is going to be less than favorable.Analyse the call Centre Performance includes various parameters like Cross-Team Visibility, Monitor Interactions in Real Time, Simplify reporting, Evaluate and streamline journeys etc. This paper focus on an approach in which IVR data is analysed and comparison is done based on HIVE, SPARK and FLINK frameworks.\",\"PeriodicalId\":91890,\"journal\":{\"name\":\"IOSR journal of computer engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IOSR journal of computer engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.9790/0661-1903040611\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IOSR journal of computer engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9790/0661-1903040611","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance Comparison between Hive, Spark-Sql & Flink-Sql through IVR Data Analysis
Companies that utilize automated IVR systems have a veritable treasure trove of data that can be analyzed to improve the quality of the customer experience.After all, many customers who are greeted by linear thinking IVR systems instead of human voices already assume that their self-service experience is going to be less than favorable.Analyse the call Centre Performance includes various parameters like Cross-Team Visibility, Monitor Interactions in Real Time, Simplify reporting, Evaluate and streamline journeys etc. This paper focus on an approach in which IVR data is analysed and comparison is done based on HIVE, SPARK and FLINK frameworks.
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