EventAction: Visual analytics for temporal event sequence recommendation

Du, Fan; Plaisant, Catherine; Spring, Neil; Shneiderman, Ben

doi:10.1109/vast.2016.7883512

Cited by 68 publications

(49 citation statements)

References 37 publications

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“…Moreover, we can use our methods for other applications, such as career decision support, financial analysis, and cybersecurity systems. For instance, as similar to the existing works [7][8][9], in a career decision support system, we can suggest similar students or workers to help the user select the next career.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

Comparative visual analytics for assessing medical records with sequence embedding

Guo

Fujiwara

et al. 2020

Visual Informatics

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Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforward due to the characteristics of medical records: high dimensionality, irregularity in time, and sparsity. To address this challenge, we introduce a method for similarity calculation of medical records. Our method employs event and sequence embeddings. While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding. Moreover, in order to better handle the irregularity of data, we enhance the self-attention mechanism with consideration of different time intervals. We have developed a visual analytics system to support comparative studies of patient records. To make a comparison of sequences with different lengths easier, our system incorporates a sequence alignment method. Through its interactive interface, the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records. We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis.

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Section: Discussion and Limitationsmentioning

confidence: 99%

Comparative visual analytics for assessing medical records with sequence embedding

Guo

Fujiwara

et al. 2020

Visual Informatics

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“…In this way, the cohort P = {P i } returned in response to a user query includes a set of patients, P i = { a i , e i , v i } with attributes a i , a temporal event sequence e i , and an outcome v i . This representation is similar to outcome-labeled temporal event sequences used widely in prior work (e.g., [9,15,30,53,59]).…”

Section: Symbolmentioning

confidence: 99%

Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation

Gotz

Zhang

Wang

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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a b d e c Fig. 1. The Cadence system for temporal event sequence visualization. (a) Interactive bar charts and histograms summarize nontemporal attributes and a variety of temporal event statistics. (b) An interactive flow-based timeline allows users to dynamically define and explore pathways within the temporal event data. Selections within the timeline link to (c) a Kaplan-Meier chart to summarize outcomes-over-time. Timeline selections also link to (d and e) a novel scatter-and-focus visualization which leverages dynamic hierarchical aggregation, scenting, and optimization-based layout to support navigation of high-dimensional hierarchical event data.Abstract-Temporal event data are collected across a broad range of domains, and a variety of visual analytics techniques have been developed to empower analysts working with this form of data. These techniques generally display aggregate statistics computed over sets of event sequences that share common patterns. Such techniques are often hindered, however, by the high-dimensionality of many real-world event sequence datasets which can prevent effective aggregation. A common coping strategy for this challenge is to group event types together prior to visualization, as a pre-process, so that each group can be represented within an analysis as a single event type. However, computing these event groupings as a pre-process also places significant constraints on the analysis. This paper presents a new visual analytics approach for dynamic hierarchical dimension aggregation. The approach leverages a predefined hierarchy of dimensions to computationally quantify the informativeness, with respect to a measure of interest, of alternative levels of grouping within the hierarchy at runtime. This information is then interactively visualized, enabling users to dynamically explore the hierarchy to select the most appropriate level of grouping to use at any individual step within an analysis. Key contributions include an algorithm for interactively determining the most informative set of event groupings for a specific analysis context, and a scented scatter-plus-focus visualization design with an optimization-based layout algorithm that supports interactive hierarchical exploration of alternative event type groupings. We apply these techniques to high-dimensional event sequence data from the medical domain and report findings from domain expert interviews.

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“…MatrixWave [49] applied superposition with an explicit encoding of sequence differences when comparing two event sequences. EventAction [14] used a calendar view to show several temporal event sequences and placed them in a ranked list to compare different sequences via juxtaposition. Most of the previous work focuses on the visual comparison of single sequences (one-to-one), or of event sequence groups (many-to-many).…”

Section: Visual Comparisonmentioning

confidence: 99%

Visual Anomaly Detection in Event Sequence Data

Guo

Jin

Chen

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Fig. 1. The anomaly detection result of the Agavue dataset using ET 3 system. Two anomalous pattern were identified: (1) tool-tips failing to show when users interact with toolbars; (2) users encountering an error after requesting data binding.Abstract-Anomaly detection is a common analytical task that aims to identify rare cases that differ from the typical cases that make up the majority of a dataset. Anomaly detection takes many forms, but is frequently applied to the analysis of event sequence data in addressing real-world problems such as error diagnosis, fraud detection, and vital sign monitoring. With general event sequence data, however, the task of anomaly detection can be complex because the sequential and temporal nature of such data results in diverse definitions and flexible forms of anomalies: a specific event type, a sub-sequence or the entire sequence. This, in turn, increases the difficulty in interpreting detected anomalies, a critical element in raising human confidence with the analysis results. Most prior work in event sequence anomaly detection focuses on detecting only one type of anomaly with a specific application domain. Such approaches do not provide flexible ways of identifying diverse types of anomalies, and provide only limited support for result interpretation. In this paper, we propose an unsupervised anomaly detection algorithm based on Variational AutoEncoders (VAE). The model learns latent representations for all sequences in the dataset and detects anomalies that deviate from the overall distribution. Moreover, the model can estimate an underlying normal progression for each given sequence represented as occurrence probabilities of events along the sequence progression. Events in violation of their occurrence probability (i.e., event occurrences with small occurrence probability, and absent events with large occurrence probability) are identified as abnormal. We also introduce a visualization system, EventThread3, to support interactive exploration of the analysis result. The system facilitates interpretations of anomalies within the context of normal sequence progressions in the dataset through comprehensive one-to-many sequence comparison. Finally, we quantitatively evaluate the performance of our anomaly detection algorithm and demonstrate the effectiveness of our system through case studies in three different application domains and report feedback collected from study participants and expert users.

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EventAction: Visual analytics for temporal event sequence recommendation

Cited by 68 publications

References 37 publications

Comparative visual analytics for assessing medical records with sequence embedding

Comparative visual analytics for assessing medical records with sequence embedding

Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation

Visual Anomaly Detection in Event Sequence Data

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