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• W3126869922 abstract "With the growing focus on business analytics and data-driven decision-making, there is a greater need for humans to interact effectively with data. We propose that presenting data to human users in terms of instances and attributes provides a more flexible and usable structure for querying, exploring, and analysing data. Compared to a traditional representation, an instance-based representation does not impose any predefined classification schema over the data when it is presented to users. This paper examines the potential utility of instance-based data through two laboratory experiments – the first focusing on exploration of data for pattern discovery (open-ended tasks) and the second on retrieval of information (closed-ended tasks). In both cases, participants were able to achieve better results in tasks using instance-based data than using class-based representations. Given the growing need for self-service analytics, as well as using information for purposes not anticipated when it was collected, we show that instance-based representations can be an effective way to satisfy the emerging needs of information users." @default.
• W3126869922 created "2021-02-15" @default.
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• W3126869922 date "2021-01-29" @default.
• W3126869922 modified "2023-09-27" @default.
• W3126869922 title "Skipping class: improving human-driven data exploration and querying through instances" @default.
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• W3126869922 doi "https://doi.org/10.1080/0960085x.2020.1869507" @default.
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