{"title":"面向列的数据模型,用于数据密集型系统","authors":"Simeon Emanuilov, A. Dimov","doi":"10.1109/COMSCI55378.2022.9912610","DOIUrl":null,"url":null,"abstract":"Traditional relational row-oriented databases have some performance issues in the case of data-intensive systems that process large amounts of data. On the other hand, a column-oriented approach to data presentation has shown promising results when applied in analytical processing. This paper shows a design for a webhook (event notifications) software system to facilitate the usage of the columnar approach in data-intensive software systems. It will be an example of using the column store where the analytical requirement is not the primary one. The approach can be applied to any system in the data-intensive context, which has many data points and requirements to store, fetch, and aggregate based on a few specific from many fields.","PeriodicalId":399680,"journal":{"name":"2022 10th International Scientific Conference on Computer Science (COMSCI)","volume":"SE-7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Column-oriented data model for data-intensive systems\",\"authors\":\"Simeon Emanuilov, A. Dimov\",\"doi\":\"10.1109/COMSCI55378.2022.9912610\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional relational row-oriented databases have some performance issues in the case of data-intensive systems that process large amounts of data. On the other hand, a column-oriented approach to data presentation has shown promising results when applied in analytical processing. This paper shows a design for a webhook (event notifications) software system to facilitate the usage of the columnar approach in data-intensive software systems. It will be an example of using the column store where the analytical requirement is not the primary one. The approach can be applied to any system in the data-intensive context, which has many data points and requirements to store, fetch, and aggregate based on a few specific from many fields.\",\"PeriodicalId\":399680,\"journal\":{\"name\":\"2022 10th International Scientific Conference on Computer Science (COMSCI)\",\"volume\":\"SE-7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 10th International Scientific Conference on Computer Science (COMSCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMSCI55378.2022.9912610\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 10th International Scientific Conference on Computer Science (COMSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMSCI55378.2022.9912610","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Column-oriented data model for data-intensive systems
Traditional relational row-oriented databases have some performance issues in the case of data-intensive systems that process large amounts of data. On the other hand, a column-oriented approach to data presentation has shown promising results when applied in analytical processing. This paper shows a design for a webhook (event notifications) software system to facilitate the usage of the columnar approach in data-intensive software systems. It will be an example of using the column store where the analytical requirement is not the primary one. The approach can be applied to any system in the data-intensive context, which has many data points and requirements to store, fetch, and aggregate based on a few specific from many fields.