Understanding the human in the design of cyber-human discovery systems for data-driven astronomy

Fluke, Christopher J.; Hegarty, Sarah; MacMahon, Clare

doi:10.1016/j.ascom.2020.100423

Cited by 4 publications

(2 citation statements)

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“…They should be employed during those periods that are particularly labour-intensive, while assisting in the identification of additional processes that can be fully or partly automated. Finding the appropriate balance between human inspection and automated detection may help to maximise the overall discovery potential of a workflow (Fluke et al 2017(Fluke et al , 2020.…”

Section: Discussionmentioning

confidence: 99%

“…A limitation to these distributed processes is one of consistency in decision-making between team members with diverse skill levels (see, for example, Fluke et al 2017, Fluke, Hegarty, & MacMahon 2020). An investment in training may be required, or a complex task must be abstracted to one of group-consensus classification.…”

Section: Single Instruction Multiple Data Work Patternsmentioning

confidence: 99%

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Survey-scale discovery-based research processes: Evaluating a bespoke visualisation environment for astronomical survey data

Fluke¹,

Vohl²,

Kilborn³

et al. 2023

Publ. Astron. Soc. Aust.

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Next generation astronomical surveys naturally pose challenges for human-centred visualisation and analysis workflows that currently rely on the use of standard desktop display environments. While a significant fraction of the data preparation and analysis will be taken care of by automated pipelines, crucial steps of knowledge discovery can still only be achieved through various level of human interpretation. As the number of sources in a survey grows, there is need to both modify and simplify repetitive visualisation processes that need to be completed for each source. As tasks such as per-source quality control, candidate rejection, and morphological classification all share a single instruction, multiple data (SIMD) work pattern, they are amenable to a parallel solution. Selecting extragalactic neutral hydrogen (HI) surveys as a representative example, we use system performance benchmarking and the visual data and reasoning (VDAR) methodology from the field of information visualisation to evaluate a bespoke comparative visualisation environment: the encube visual analytics framework deployed on the 83 Megapixel Swinburne DiscoveryWall. Through benchmarking using spectral cube data from existing HI surveys, we are able to perform interactive comparative visualisation via texture-based volume rendering of 180 three-dimensional (3D) data cubes at a time. The time to load a configuration of spectral cubes scale linearly with the number of voxels, with independent samples of 180 cubes (8.4 Gigavoxels or 34 Gigabytes) each loading in under 5 minutes. We show that parallel comparative inspection is a productive and time-saving technique which can reduce the time taken to complete SIMD-style visual tasks currently performed at the desktop by at least two orders of magnitude, potentially rendering some labour-intensive desktop-based workflows obsolete.

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Section: Discussionmentioning

confidence: 99%

Section: Single Instruction Multiple Data Work Patternsmentioning

confidence: 99%