Rahma Djiroun, Meriem Amel Guessoum, K. Boukhalfa, E. Benkhelifa
{"title":"Towards an OLAP Cubes Recommendation Approach in Cloud Computing Environment","authors":"Rahma Djiroun, Meriem Amel Guessoum, K. Boukhalfa, E. Benkhelifa","doi":"10.1109/SNAMS53716.2021.9732105","DOIUrl":null,"url":null,"abstract":"The Cloud Computing technology is constantly evolving in terms of service provision. Recently, many companies that use OLAP systems for decision-making are deploying their OLAP cubes as services in a cloud environment. The potential of the exploited cubes is growing significantly. Therefore, consumers find difficulties in selecting relevant cubes especially when their needs are dispersed across multiple cubes. Hence, we propose in this paper an approach that allows relevant cubes recommen-dation among the deployed cubes in the cloud as well as the construction of new cubes if the need cannot be met by a single cube. In order to validate our approach, a tool called “Cube-RS” is developed. An experimental study that evaluates our proposal in terms of relevance and performance is presented.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNAMS53716.2021.9732105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Cloud Computing technology is constantly evolving in terms of service provision. Recently, many companies that use OLAP systems for decision-making are deploying their OLAP cubes as services in a cloud environment. The potential of the exploited cubes is growing significantly. Therefore, consumers find difficulties in selecting relevant cubes especially when their needs are dispersed across multiple cubes. Hence, we propose in this paper an approach that allows relevant cubes recommen-dation among the deployed cubes in the cloud as well as the construction of new cubes if the need cannot be met by a single cube. In order to validate our approach, a tool called “Cube-RS” is developed. An experimental study that evaluates our proposal in terms of relevance and performance is presented.