BigIN4

Lin, Qingwei; Ke, Weichen; Lou, Jian–Guang; Zhang, Hongyu; Sui, Kaixin; Xu, Ye; Zhou, Ziyi; Qiao, Bo; Zhang, Dongmei

doi:10.1145/3219819.3219867

Cited by 13 publications

(3 citation statements)

References 20 publications

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“…There are a number of systems that apply statistical measures to find interesting data points and recommend them for visualization (e.g. [THY*17, LKL*18, CBYE19]). One of the most prominent examples is Voyager [WMA*16], a mixed‐initiative system that supports faceted browsing of recommended charts.…”

Section: Related Workmentioning

confidence: 99%

Guided Exploration of Industrial Sensor Data

Langer,

Meyes,

Meisen

2024

Computer Graphics Forum

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In recent years, digitization in the industrial sector has increased steadily. Digital data not only allows us to monitor the underlying production process using machine learning methods (anomaly detection, behaviour analysis) but also to understand the underlying production process. Insights from Exploratory Data Analysis (EDA) play an important role in building data‐driven processes because data scientists learn essential characteristics of the data in the context of the domain. Due to the complexity of production processes, it is usually difficult for data scientists to acquire this knowledge by themselves. Hence, they have to rely on continuous close collaboration with domain experts and their acquired domain expertise. However, direct communication does not promote documentation of the knowledge transfer from domain experts to data scientists. In this respect, changing team constellations, for example due to a change in personnel, result in a renewed high level of effort despite the same knowledge transfer problem. As a result, EDA is a cost‐intensive iterative process. We, therefore, investigate a system to extract information from the interactions that domain experts perform during EDA. Our approach relies on recording interactions and system states of an exploration tool and generating guided exploration sessions for domain novices. We implement our approach in a software tool and demonstrate its capabilities using two real‐world use cases from the manufacturing industry. We evaluate its feasibility in a user study to investigate whether domain novices can reproduce the most important insights from domain experts about the datasets of the use cases based on generated EDA sessions. From the results of this study, we conclude the feasibility of our system as participants are able to reproduce on average 86.5% of insights from domain experts.

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Section: Related Workmentioning

confidence: 99%

Guided Exploration of Industrial Sensor Data

Langer,

Meyes,

Meisen

2024

Computer Graphics Forum

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“…Furthermore, MetaInsight [26] proposes a novel scoring function to quantify the usefulness of insights. BigIn4 [27] combines data cubes and the Approximate Query Processing technique to provide interactive insight recommendations for a large-scale dataset. Additionally, DataSite [34] is a web-based platform that enables automatic analysis and calculation while users visualize and analyze data.…”

Section: A Automatic Insight Discovery Of Tabular Datamentioning

confidence: 99%

“…Previous research on the visual exploration and analysis of tabular data can be categorized into two distinct groups. The first category pertains to defining insights within the tabular data [24]- [27], such as the identification of outliers and trends. The second category revolves around recommending visualizations based on these insights, encompassing both single visualizations [28], [29] and sets of related visualizations [30]- [33].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Fluctuation Shapes a Dynamic Flow Linked to States of Consciousness

Liu

Lei

et al. 2022

Preprint

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Consciousness emerges from the spatiotemporal dynamics of neural activity. However, how such an extraordinary phenomenon is supported by neural flexibility and regional specialization across the cerebral cortex remains elusive. Here, using several functional magnetic resonance imaging (fMRI) paradigms (e.g., anesthesia, sleep, and drowsiness), we show a consciousness-related signature characterized by shifting spontaneous fluctuations along a unimodal-transmodal cortical organizational axis. The signature is simple and sensitive to altered states of consciousness in single individuals, exhibiting abnormal elevation under psychedelics as well as in individuals with psychosis. Under task-free conditions, this hierarchical dynamic reflects ongoing brain state changes in global integration and connectome diversity. Applying quasi-periodic pattern detection during different vigilance states, we found that hierarchical heterogeneity also manifested as spatiotemporally propagation waves related to arousal. A similar pattern was observed in neural activity measured with cortex-wide electrocorticography (ECoG) in macaques. Last, we observed that the spatial distribution of the principle cortical gradient not only preferentially recapitulated the genetic transcription levels of the histaminergic system but also that of the functional connectome mapping of the tuberomammillary nucleus (TMN), which promotes wakefulness. Combining behavioral, neuroimaging, electrophysiological, and transcriptomic evidence, we suggest that global state of consciousness is supported by efficient hierarchical processing that can be constrained along a low-dimensional macroscale gradient.

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SmartInsight: Learning-Based Automatic Insight Discovery for Exploratory Data Analysis

Zhang,

Jing

et al. 2024

Lecture Notes in Computer Science

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BigIN4

Cited by 13 publications

References 20 publications

Guided Exploration of Industrial Sensor Data

Guided Exploration of Industrial Sensor Data

Hierarchical Fluctuation Shapes a Dynamic Flow Linked to States of Consciousness

SmartInsight: Learning-Based Automatic Insight Discovery for Exploratory Data Analysis

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