{"title":"数据浪潮:数据管理和挖掘","authors":"Mohand Tahar Kechadi","doi":"10.1109/WETICE.2010.56","DOIUrl":null,"url":null,"abstract":"Nowadays, massive amounts of data that are often geographically distributed and owned by different organisations are being mined. As consequence, a large mount of knowledge is being produced. This causes the problem of efficient knowledge management and mining. The main aim is to develop DM infrastructures to fully exploit the benefit of the knowledge contained in these very large data repositories. To this end, we introduced ”knowledge map” approach to represent easily and efficiently the knowledge mined in a large-scale platform such as Grid. This also facilitates the integration and coordination of local mining processes along with existing knowledge to increase the accuracy of the final models. In this paper, we discuss its advantages and its design issues.","PeriodicalId":426248,"journal":{"name":"2010 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The Data Wave: Data Management and Mining\",\"authors\":\"Mohand Tahar Kechadi\",\"doi\":\"10.1109/WETICE.2010.56\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, massive amounts of data that are often geographically distributed and owned by different organisations are being mined. As consequence, a large mount of knowledge is being produced. This causes the problem of efficient knowledge management and mining. The main aim is to develop DM infrastructures to fully exploit the benefit of the knowledge contained in these very large data repositories. To this end, we introduced ”knowledge map” approach to represent easily and efficiently the knowledge mined in a large-scale platform such as Grid. This also facilitates the integration and coordination of local mining processes along with existing knowledge to increase the accuracy of the final models. In this paper, we discuss its advantages and its design issues.\",\"PeriodicalId\":426248,\"journal\":{\"name\":\"2010 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WETICE.2010.56\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WETICE.2010.56","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nowadays, massive amounts of data that are often geographically distributed and owned by different organisations are being mined. As consequence, a large mount of knowledge is being produced. This causes the problem of efficient knowledge management and mining. The main aim is to develop DM infrastructures to fully exploit the benefit of the knowledge contained in these very large data repositories. To this end, we introduced ”knowledge map” approach to represent easily and efficiently the knowledge mined in a large-scale platform such as Grid. This also facilitates the integration and coordination of local mining processes along with existing knowledge to increase the accuracy of the final models. In this paper, we discuss its advantages and its design issues.