Visualizing Uncertainty and Alternatives in Event Sequence Predictions

Guo, Shunan; Du, Fan; Malik, Sana; Koh, Eunyee; Kim, Sung‐Chul; Liu, Zhicheng; Kim, Donghyun; Zha, Hongyuan; Cao, Nan

doi:10.1145/3290605.3300803

Cited by 45 publications

(40 citation statements)

References 55 publications

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“…Wu et al [ 96 ] developed an AI-powered tool to assist people with dyslexia to write posts on social media. Guo et al [ 97 ] developed an application to visualize event sequence predictions of multiple records. They used inputs from machine learning practitioners before development and used eighteen participants to evaluate the system.…”

Section: Classifying Hcml Researchmentioning

confidence: 99%

“…Considering the works that focused on the user side, some researchers catered to general end-users or consumers [83,101,200,210], while others on specific end-users. Examples for these include people who need assistance [2,80,[86][87][88][89]96,117,147], medical professionals [57,67,110,192,193], international travelers [50], Amazon Mechanical Turk [60,99], drivers [161,162], musicians [102], teachers [124], students [128], children [72,125], UX designers [65,115,206,209], UI designers [103,111,173], data analysts [97], video creators [84], and game designers [70,165,174,211]. Apart from focusing on a specific user group, some have tried to understand multiple user-perspectives from ML engineers to the end-user [48].…”

Section: The 'Human' In Hcmlmentioning

confidence: 99%

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A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

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After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

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Section: Classifying Hcml Researchmentioning

confidence: 99%

Section: The 'Human' In Hcmlmentioning

confidence: 99%

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

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“…Users are allowed to modify these events (e.g., by removing, moving, adding, or adjusting the event's duration) to get different outcomes from the prediction model. Moreover, [34] is a visual analytics system designed for prediction analysis. It employs Recurrent Neural Networks (RNNs) to predict future activities, and review the most probable predictions and possible alternatives in a circular glyph design ( Fig.…”

Section: Visual Event Prediction and Recommendationmentioning

confidence: 99%

“…This uncertainty can inhibit analysts from making optimal decisions if information about uncertainty is not properly communicated in the visual analytics process [87]. Although some previous studies [19], [34] have incorporated uncertainty information in visual analytics of event sequence data, they focused on only one type of uncertainty information -the probabilistic uncertainty under an event prediction scenario. Therefore, more research is required to study the best ways of incorporating and visualizing other types of uncertainty information, such as bounded uncertainty, during the process of event sequence data analysis.…”

Section: Data Qualitymentioning

confidence: 99%

Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.

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“…Uncertainty data can be hierarchical or sequential ("lineage" in Thomson et al) [52]. For example, a Bayesian mixed linear model can produce parameter estimates that can be organized in a hierarchy (see Figure 16.3 in Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan [33]); in machine learning, an event sequence prediction model can generate probabilities of state transitions [20]. These structures in data (or even in statistical models) can be captured with formal specifications, such as probabilistic programs.…”

Section: Leveraging Uncertainty Lineage and Model Structuresmentioning

confidence: 99%

A Probabilistic Grammar of Graphics

Pu¹,

Kay²

2020

Preprint

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Visualizations depicting probabilities and uncertainty are used everywhere from medical risk communication to machine learning, yet these probabilistic visualizations are difficult to specify, prone to error, and their designs are cumbersome to explore. We propose a Probabilistic Grammar of Graphics (PGoG), an extension to Wilkinson’s original framework. Inspired by the success of probabilistic programming languages, PGoG makes probability expressions, such as P(A|B), a first-class citizen in the language. PGoG abstractions also reflect the distinction between probability and frequency framing, a concept from the uncertainty communication literature. It is expressive, encompassing product plots, density plots, icon arrays, and dotplots, among other visualizations. Its coherent syntax ensures correctness (that the proportions of visual elements and their spatial placement reflect the underlying probability distribution) and reduces edit distance between probabilistic visualization specifications, potentially support- ing more design exploration. We provide a proof-of-concept implementation of PGoG in R.

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Visualizing Uncertainty and Alternatives in Event Sequence Predictions

Cited by 45 publications

References 55 publications

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Visual Progression Analysis of Event Sequence Data

A Probabilistic Grammar of Graphics

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