{"title":"一个用于异构数据的个人数字图书馆","authors":"Su Inn Park, F. Shipman","doi":"10.1109/JCDL.2014.6970155","DOIUrl":null,"url":null,"abstract":"Systems are needed to support access to and analysis of large heterogeneous scientific datasets. We developed PerCon, a data management and analysis environment, to support such activities. PerCon processes and integrates data gathered via queries to existing data providers to create a personal digital library of data. Users may then search, browse, visualize and annotate the data as they proceed with analysis and interpretation. Interpretation in PerCon takes place in a visual workspace in which multiple data visualizations and annotations are placed into spatial arrangements based on the current task. The system watches for patterns in the user's data selection and organization and through mixed-initiative interaction assists users by suggesting potentially relevant data from unexplored data sources. PerCon's data location and analysis capabilities were evaluated in a controlled study with 24 users. Study participants had to locate and analyze heterogeneous weather and river data with and without the visual workspace and mixed-initiative interaction, respectively. Results indicate that the visual workspace facilitated information representation and aided in the identification of relationships between datasets. The system's suggestions encouraged data exploration, leading participants to identify more evidence of correlation among data streams and more potential interactions among weather and river data.","PeriodicalId":92278,"journal":{"name":"Proceedings of the ... ACM/IEEE Joint Conference on Digital Libraries. ACM/IEEE Joint Conference on Digital Libraries","volume":"5 1","pages":"97-106"},"PeriodicalIF":0.0000,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"PerCon: A personal digital library for heterogeneous data\",\"authors\":\"Su Inn Park, F. Shipman\",\"doi\":\"10.1109/JCDL.2014.6970155\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Systems are needed to support access to and analysis of large heterogeneous scientific datasets. We developed PerCon, a data management and analysis environment, to support such activities. PerCon processes and integrates data gathered via queries to existing data providers to create a personal digital library of data. Users may then search, browse, visualize and annotate the data as they proceed with analysis and interpretation. Interpretation in PerCon takes place in a visual workspace in which multiple data visualizations and annotations are placed into spatial arrangements based on the current task. The system watches for patterns in the user's data selection and organization and through mixed-initiative interaction assists users by suggesting potentially relevant data from unexplored data sources. PerCon's data location and analysis capabilities were evaluated in a controlled study with 24 users. Study participants had to locate and analyze heterogeneous weather and river data with and without the visual workspace and mixed-initiative interaction, respectively. Results indicate that the visual workspace facilitated information representation and aided in the identification of relationships between datasets. The system's suggestions encouraged data exploration, leading participants to identify more evidence of correlation among data streams and more potential interactions among weather and river data.\",\"PeriodicalId\":92278,\"journal\":{\"name\":\"Proceedings of the ... ACM/IEEE Joint Conference on Digital Libraries. ACM/IEEE Joint Conference on Digital Libraries\",\"volume\":\"5 1\",\"pages\":\"97-106\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ... ACM/IEEE Joint Conference on Digital Libraries. ACM/IEEE Joint Conference on Digital Libraries\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/JCDL.2014.6970155\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... ACM/IEEE Joint Conference on Digital Libraries. ACM/IEEE Joint Conference on Digital Libraries","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/JCDL.2014.6970155","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PerCon: A personal digital library for heterogeneous data
Systems are needed to support access to and analysis of large heterogeneous scientific datasets. We developed PerCon, a data management and analysis environment, to support such activities. PerCon processes and integrates data gathered via queries to existing data providers to create a personal digital library of data. Users may then search, browse, visualize and annotate the data as they proceed with analysis and interpretation. Interpretation in PerCon takes place in a visual workspace in which multiple data visualizations and annotations are placed into spatial arrangements based on the current task. The system watches for patterns in the user's data selection and organization and through mixed-initiative interaction assists users by suggesting potentially relevant data from unexplored data sources. PerCon's data location and analysis capabilities were evaluated in a controlled study with 24 users. Study participants had to locate and analyze heterogeneous weather and river data with and without the visual workspace and mixed-initiative interaction, respectively. Results indicate that the visual workspace facilitated information representation and aided in the identification of relationships between datasets. The system's suggestions encouraged data exploration, leading participants to identify more evidence of correlation among data streams and more potential interactions among weather and river data.