Skipping class: Improving human-driven data exploration and querying through instances
Saghafi,Arash Wand,Yair Parsons,Jeffrey
Saghafi,Arash
Wand,Yair
Parsons,Jeffrey
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.
Description
Date
2022-08
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Research Projects
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Keywords
Database Design, attributes, classification, database queries, human-in-the-loop data analytics, instance-based data model, knowledge discovery, non-classified data, open information, pattern detection
Citation
Saghafi, A, Wand, Y & Parsons, J 2022, 'Skipping class: Improving human-driven data exploration and querying through instances', European Journal of Information Systems, vol. 31, no. 4, pp. 463-491. https://doi.org/10.1080/0960085X.2020.1869507