Abstract
We present a new visual exploration concept-Progressive Visual Analytics with Safeguards-that helps people manage the uncertainty arising from progressive data exploration. Despite its potential benefits, intermediate knowledge from progressive analytics can be incorrect due to various machine and human factors, such as a sampling bias or misinterpretation of uncertainty. To alleviate this problem, we introduce PVA-Guards, safeguards people can leave on uncertain intermediate knowledge that needs to be verified, and derive seven PVA-Guards based on previous visualization task taxonomies. PVA-Guards provide a means of ensuring the correctness of the conclusion and understanding the reason when intermediate knowledge becomes invalid. We also present ProReveal, a proof-of-concept system designed and developed to integrate the seven safeguards into progressive data exploration. Finally, we report a user study with 14 participants, which shows people voluntarily employed PVA-Guards to safeguard their findings and ProReveal's PVA-Guard view provides an overview of uncertain intermediate knowledge. We believe our new concept can also offer better consistency in progressive data exploration, alleviating people's heterogeneous interpretation of uncertainty.
Highlights
W E propose Progressive Visual Analytics with Safeguards, a novel visual exploration concept that helps people manage the uncertainty arising from progressive data exploration
Progressive visual analytics (PVA) allows people to access the partial results of visualization queries in the middle of computation, helping them make data-driven decisions faster even with large-scale data
Despite the benefits of progressive visual analytics (PVA), managing the trustworthiness of intermediate outcomes has been regarded as a core concern when applying PVA to a wider range of scenarios
Summary
W E propose Progressive Visual Analytics with Safeguards, a novel visual exploration concept that helps people manage the uncertainty arising from progressive data exploration. Progressive visual analytics (PVA) allows people to access the partial results of visualization queries in the middle of computation, helping them make data-driven decisions faster even with large-scale data. Such intermediate knowledge can be incorrect due to various machine and human factors. Many PVA systems build and use samples of raw data to estimate results, which leaves a discrepancy between the precise results and the results based on the samples Another reason can be a human factor such as misinterpreting the uncertainty of intermediate knowledge and making a hasty decision.
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