Visual Analysis of Multidimensional Big Data: A Scalable Lightweight Bundling Method for Parallel Coordinates

Cui, Wenqiang; Strazdiņš, Ģirts; Wang, Hao

doi:10.1109/tbdata.2021.3123982

Cited by 4 publications

(1 citation statement)

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“…The reason can be found in analytical conversations, where this type of query makes it easier for the user to request successive data transformations beyond the initial or current visualisation. In this context, we suggest that V-NLI systems allow users to ask for more complex data transformations, such as visual binning or the extraction of subsets for the analysis of specific parts of the data [30,109,110] To do so, we think that combining Natural Language with other interaction styles may help the user to express the context of the visualisation, in line with the proposal of Beck et al [63], where the user's NL-based queries refer to the part of the visualisation selected with the mouse. For example, suppose there is a 3D scene that shows a hierarchical graph.…”

Section: Discussionmentioning

confidence: 99%

Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review

2023

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Rapid growth in the generation of data from various sources has made data visualisation a valuable tool for analysing data. However, visual analysis can be a challenging task, not only due to intricate dashboards but also when dealing with complex and multidimensional data. In this context, advances in Natural Language Processing technologies have led to the development of Visualisation-oriented Natural Language Interfaces (V-NLIs). In this paper, we carry out a scoping review that analyses synergies between the fields of Data Visualisation and Natural Language Interaction. Specifically, we focus on chatbot-based V-NLI approaches and explore and discuss three research questions. The first two research questions focus on studying how chatbot-based V-NLIs contribute to interactions with the Data and Visual Spaces of the visualisation pipeline, while the third seeks to know how chatbot-based V-NLIs enhance users’ interaction with visualisations. Our findings show that the works in the literature put a strong focus on exploring tabular data with basic visualisations, with visual mapping primarily reliant on fixed layouts. Moreover, V-NLIs provide users with restricted guidance strategies, and few of them support high-level and follow-up queries. We identify challenges and possible research opportunities for the V-NLI community such as supporting high-level queries with complex data, integrating V-NLIs with more advanced systems such as Augmented Reality (AR) or Virtual Reality (VR), particularly for advanced visualisations, expanding guidance strategies beyond current limitations, adopting intelligent visual mapping techniques, and incorporating more sophisticated interaction methods.

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