Steerable Self-Driving Data Visualization

Luo, Yuling; Qin, Xuedi; Chai, Chunlei; Tang, Nan; Li, Guoliang; Li, Wenbo

doi:10.1109/tkde.2020.2981464

Cited by 26 publications

(13 citation statements)

References 27 publications

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“…Qin et al [75] explore the possibility to automate the process of matching data with appropriate diagrammatic visu a liza tio n types. Luo et al [76] go on to further develop this appro a ch, achieving a system that make a rage of visua lization recommendations when presented with data; utilizing a learning technique that makes use of existing examples, users can also enter keywords to further influence the systems choice of visualization. With the aim of further exploring t h e automated generation of graphs from data the field of Gra p h Grammars may lend itself to such context-based display of industrial data.…”

Section: Visualization and Interactionmentioning

confidence: 99%

Human in the Loop: Industry 4.0 Technologies and Scenarios for Worker Mediation of Automated Manufacturing

Turner

Chen

et al. 2021

IEEE Access

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Industry 4.0 derived technologies have the potential to enable a new wave of digital manufacturing solutions for semi and fully automated production. In addition, this paradigm enco m p a s ses the use of communication technologies to transmit data to processing stations as well as the utilization of cloud based computational resources for data mining. Despite the rise in automation, future manufactu rin g systems will initially still require humans in the loop to provide supervisory level mediation for even the most autonomous production scenarios. Through a structured review, this paper details a number of key technologies that are most likely to shape this future and describes a range of scenarios for their use in delivering human mediated automated and autonomous production. This paper argues that in all cases of future manufacturing management it is key that the human has oversight of critical information flows and remains an active participant in the delivery of the next generation of production systems.

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Section: Visualization and Interactionmentioning

confidence: 99%

Human in the Loop: Industry 4.0 Technologies and Scenarios for Worker Mediation of Automated Manufacturing

Turner

Chen

et al. 2021

IEEE Access

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“…Thus, we discuss feature engineering and feature learning approaches used to extract visualization features. [11], [47], [54], [66] element positions or regions [44], [46], [53], [54], [67] element styles [46] parameters [1], [2] communicative signals [68] design rules [30], [36] statistical models [3], [66], [69], [70] statistics [31], [32], [64], [71], [72] one-hot vector [73] Feature Learning convolutional neural network [12], [13], [40], [44], [50], [53], [69], [74]- [77], [77]- [87] autoencoder [6], [77] autoencoder [9], [88] embedding models [3], [73] autoencoder [89]…”

Section: Representationmentioning

confidence: 99%

“…Literature from data mining and databases [31], [34], [35], [51], [70], [72], [91]- [94] usually uses relational programming to form visualizations as queries into database (Figure 5 B ). Those relational queries facilitate operations on collections of Fig.…”

Section: Internal Representationmentioning

confidence: 99%

“…However, the sequence-to-sequence structure might not well capture the characteristics of the underlying data such as the data type. DeepEye [31], [70], [72] and VizDeck [71] perform feature engineering to consider data statistics such as the number of unique values in a column. VizML [32] further extends this approach to 841 features including single-and pairwise-column features of the input dataset and significantly outperforms Data2Vis and DeepEye.…”

Section: Feature Engineering and Feature Learningmentioning

confidence: 99%

“…Machine Learning Hybrid Rankings effectiveness rankings [29] learning-to-rank [31], [40], [70], [72] Scores convert rankings to scores [30], [96], [97] hand-crafted metrics [2], [5], [6], [39], [91], [92], [108]- [110] learning-to-rank with scores [1] predictive regression [64], [71], [77] learning-to-weight hand-crafted metrics [30] [56], [103] use the node hierarchy information to group shape nodes under the same ancestor. Nevertheless, those approaches are only applicable to vector graphics.…”

Section: Rule-basedmentioning

confidence: 99%

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AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Wang

Shu

et al. 2021

Preprint

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Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data. We define visualization data as the digital representations of visualizations in computers and focus on visualizations in information visualization and visual analytic. We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at ai4vis.github.io.

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