Data quality management,especially data cleansing,has been extensively studied for many years in the areas of data management and visual analytics.In the paper,we first review and explore the relevant work from the re...Data quality management,especially data cleansing,has been extensively studied for many years in the areas of data management and visual analytics.In the paper,we first review and explore the relevant work from the research areas of data management,visual analytics and human-computer interaction.Then for different types of data such as multimedia data,textual data,trajectory data,and graph data,we summarize the common methods for improving data quality by leveraging data cleansing techniques at different analysis stages.Based on a thorough analysis,we propose a general visual analytics framework for interactively cleansing data.Finally,the challenges and opportunities are analyzed and discussed in the context of data and humans.展开更多
基金This research was funded by National Key R&D Program of China(No.SQ2018YFB100002)the National Natural Science Foundation of China(No.s 61761136020,61672308)+5 种基金Microsoft Research Asia,Fraunhofer Cluster of Excellence on"Cognitive Internet Technologies",EU through project Track&Know(grant agreement 780754)NSFC(61761136020)NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(U1609217)Zhejiang Provincial Natural Science Foundation(LR18F020001)NSFC Grants 61602306Fundamental Research Funds for the Central Universities。
文摘Data quality management,especially data cleansing,has been extensively studied for many years in the areas of data management and visual analytics.In the paper,we first review and explore the relevant work from the research areas of data management,visual analytics and human-computer interaction.Then for different types of data such as multimedia data,textual data,trajectory data,and graph data,we summarize the common methods for improving data quality by leveraging data cleansing techniques at different analysis stages.Based on a thorough analysis,we propose a general visual analytics framework for interactively cleansing data.Finally,the challenges and opportunities are analyzed and discussed in the context of data and humans.
