{"title":"洞察样本的共性:探索异常子集与数据集关系的可视化框架","authors":"Nikolas Stege , Michael H. Breitner","doi":"10.1016/j.datak.2024.102299","DOIUrl":null,"url":null,"abstract":"<div><p>Domain experts are driven by business needs, while data analysts develop and use various algorithms, methods, and tools, but often without domain knowledge. A major challenge for companies and organizations is to integrate data analytics in business processes and workflows. We deduce an interactive process and visualization framework to enable value creating collaboration in inter- and cross-disciplinary teams. Domain experts and data analysts are both empowered to analyze and discuss results and come to well-founded insights and implications. Inspired by a typical auditing problem, we develop and apply a visualization framework to single out unusual data in general subsets for potential further investigation. Our framework is applicable to both unusual data detected manually by domain experts or by algorithms applied by data analysts. Application examples show typical interaction, collaboration, visualization, and decision support.</p></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"151 ","pages":"Article 102299"},"PeriodicalIF":2.7000,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169023X24000235/pdfft?md5=5865a6d1aaccbc08965569d170abf88f&pid=1-s2.0-S0169023X24000235-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Insights into commonalities of a sample: A visualization framework to explore unusual subset-dataset relationships\",\"authors\":\"Nikolas Stege , Michael H. Breitner\",\"doi\":\"10.1016/j.datak.2024.102299\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Domain experts are driven by business needs, while data analysts develop and use various algorithms, methods, and tools, but often without domain knowledge. A major challenge for companies and organizations is to integrate data analytics in business processes and workflows. We deduce an interactive process and visualization framework to enable value creating collaboration in inter- and cross-disciplinary teams. Domain experts and data analysts are both empowered to analyze and discuss results and come to well-founded insights and implications. Inspired by a typical auditing problem, we develop and apply a visualization framework to single out unusual data in general subsets for potential further investigation. Our framework is applicable to both unusual data detected manually by domain experts or by algorithms applied by data analysts. Application examples show typical interaction, collaboration, visualization, and decision support.</p></div>\",\"PeriodicalId\":55184,\"journal\":{\"name\":\"Data & Knowledge Engineering\",\"volume\":\"151 \",\"pages\":\"Article 102299\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0169023X24000235/pdfft?md5=5865a6d1aaccbc08965569d170abf88f&pid=1-s2.0-S0169023X24000235-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data & Knowledge Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0169023X24000235\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data & Knowledge Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169023X24000235","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Insights into commonalities of a sample: A visualization framework to explore unusual subset-dataset relationships
Domain experts are driven by business needs, while data analysts develop and use various algorithms, methods, and tools, but often without domain knowledge. A major challenge for companies and organizations is to integrate data analytics in business processes and workflows. We deduce an interactive process and visualization framework to enable value creating collaboration in inter- and cross-disciplinary teams. Domain experts and data analysts are both empowered to analyze and discuss results and come to well-founded insights and implications. Inspired by a typical auditing problem, we develop and apply a visualization framework to single out unusual data in general subsets for potential further investigation. Our framework is applicable to both unusual data detected manually by domain experts or by algorithms applied by data analysts. Application examples show typical interaction, collaboration, visualization, and decision support.
期刊介绍:
Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.