{"title":"Interactive Data Mashups for User-Centric Data Analysis","authors":"M. Behringer, Pascal Hirmer","doi":"10.1145/3603719.3603742","DOIUrl":null,"url":null,"abstract":"Nowadays, the amount of data is growing rapidly. Through data mining and analysis, information and knowledge can be derived based on this growing volume of data. Different tools have been introduced in the past to specify data analysis scenarios in a graphical manner, for instance, PowerBI, Knime, or RapidMiner. However, when it comes to specifying complex data analysis scenarios, e.g., in larger companies, domain experts can easily become overwhelmed by the extensive functionality and configuration possibilities of these tools. In addition, the tools vary significantly regarding their powerfulness and functionality, which could lead to the need to use different tools for the same scenario. In this demo paper, we introduce our novel user-centric interactive data mashup tool that supports domain experts in interactively creating their analysis scenarios and introduces essential functionalities that are lacking in similar tools, such as direct feedback of data quality issues or recommendation of suitable data sources not yet considered.","PeriodicalId":314512,"journal":{"name":"Proceedings of the 35th International Conference on Scientific and Statistical Database Management","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 35th International Conference on Scientific and Statistical Database Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3603719.3603742","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
Nowadays, the amount of data is growing rapidly. Through data mining and analysis, information and knowledge can be derived based on this growing volume of data. Different tools have been introduced in the past to specify data analysis scenarios in a graphical manner, for instance, PowerBI, Knime, or RapidMiner. However, when it comes to specifying complex data analysis scenarios, e.g., in larger companies, domain experts can easily become overwhelmed by the extensive functionality and configuration possibilities of these tools. In addition, the tools vary significantly regarding their powerfulness and functionality, which could lead to the need to use different tools for the same scenario. In this demo paper, we introduce our novel user-centric interactive data mashup tool that supports domain experts in interactively creating their analysis scenarios and introduces essential functionalities that are lacking in similar tools, such as direct feedback of data quality issues or recommendation of suitable data sources not yet considered.