(s|qu)eries

Zgraggen, Emanuel; Drucker, Steven M.; Fisher, Danyel; DeLine, Robert

doi:10.1145/2702123.2702262

Cited by 42 publications

(7 citation statements)

References 26 publications

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“…A follow-up version, Lifelines2 [42], utilizes a set of dynamic aggregations to highlight the prevalence of events occurring in multiple sequences. Zgraggen et al [47] propose a touch-based system featuring a visual query language for time series built via regular expressions. WireVis [5] connects temporal events by monitoring a set of userdefined keywords and visualizing the detected relations as a network.…”

Section: Visual Analytics Of Multivariate Temporal Datamentioning

confidence: 99%

DOMINO: Visual Causal Reasoning With Time-Dependent Phenomena

Wang

Mueller

2023

IEEE Trans. Visual. Comput. Graphics

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Current work on using visual analytics to determine causal relations among variables has mostly been based on the concept of counterfactuals. As such the derived static causal networks do not take into account the effect of time as an indicator. However, knowing the time delay of a causal relation can be crucial as it instructs how and when actions should be taken. Yet, similar to static causality, deriving causal relations from observational time-series data, as opposed to designed experiments, is not a straightforward process. It can greatly benefit from human insight to break ties and resolve errors. We hence propose a set of visual analytics methods that allow humans to participate in the discovery of causal relations associated with windows of time delay. Specifically, we leverage a well-established method, logic-based causality, to enable analysts to test the significance of potential causes and measure their influences toward a certain effect. Furthermore, since an effect can be a cause of other effects, we allow users to aggregate different temporal cause-effect relations found with our method into a visual flow diagram to enable the discovery of temporal causal networks. To demonstrate the effectiveness of our methods we constructed a prototype system named DOMINO and showcase it via a number of case studies using real-world datasets. Finally, we also used DOMINO to conduct several evaluations with human analysts from different science domains in order to gain feedback on the utility of our system in practical scenarios.

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Section: Visual Analytics Of Multivariate Temporal Datamentioning

confidence: 99%

DOMINO: Visual Causal Reasoning With Time-Dependent Phenomena

Wang

Mueller

2023

IEEE Trans. Visual. Comput. Graphics

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“…Several authors [CvW17, ZDFD15,FKSS06,KPS15] use the multivariate data as a visual filter and form sequences based on users' filter selections. Events can be filtered using regular expressions [ZDFD15, CvW17] or nodelink diagrams [KPS15]. To support additional attribute exploration and analysis, data distributions of the attributes are shown as small charts on the side [CvW17, XSZX22].…”

Section: Related Workmentioning

confidence: 99%

“…However, this requires programming skills, is time and labor intensive, loses the relation to other perspectives, and does not enable users to explore complex multivariate relations. Several authors [CvW17, ZDFD15,FKSS06,KPS15] use the multivariate data as a visual filter and form sequences based on users' filter selections. Events can be filtered using regular expressions [ZDFD15, CvW17] or nodelink diagrams [KPS15].…”

Section: Related Workmentioning

confidence: 99%

FlexEvent: going beyond Case‐Centric Exploration and Analysis of Multivariate Event Sequences

Linden

Wulterkens

Gilst

et al. 2023

Computer Graphics Forum

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In many domains, multivariate event sequence data is collected focused around an entity (the case). Typically, each event has multiple attributes, for example, in healthcare a patient has events such as hospitalization, medication, and surgery. In addition to the multivariate events, also the case (a specific attribute, e.g., patient) has associated multivariate data (e.g., age, gender, weight). Current work typically only visualizes one attribute per event (label) in the event sequences. As a consequence, events can only be explored from a predefined case‐centric perspective. However, to find complex relations from multiple perspectives (e.g., from different case definitions, such as doctor), users also need an event‐ and attribute‐centric perspective. In addition, support is needed to effortlessly switch between and within perspectives. To support such a rich exploration, we present FlexEvent: an exploration and analysis method that enables investigation beyond a fixed case‐centric perspective. Based on an adaptation of existing visualization techniques, such as scatterplots and juxtaposed small multiples, we enable flexible switching between different perspectives to explore the multivariate event sequence data needed to answer multi‐perspective hypotheses. We evaluated FlexEvent with three domain experts in two use cases with sleep disorder and neonatal ICU data that show our method facilitates experts in exploring and analyzing real‐world multivariate sequence data from different perspectives.

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“…To facilitate the process of creating intrachart and inter‐chart animations, CAST [GLW21] and Data Animator [TLS21] presented two authoring tools that enable users to build chart animations without programming. Built on regular expressions, (s|qu)eries [ZDFD15] proposed an expressive visual query language for building queries on sequences in an approachable way. Users can visually describe high‐level patterns of interest by directly manipulating the constraint blocks in 2D canvas and interactively explore the result visualizations.…”

Section: Related Workmentioning

confidence: 99%

Exploring Multivariate Event Sequences with an Interactive Similarity Builder

Sun

Zhang

et al. 2022

Computer Graphics Forum

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Similarity‐based exploration is an effective method in knowledge discovery. Faced with multivariate event sequence data (MVES), developing a satisfactory similarity measurement for a specific question is challenging because of the heterogeneity introduced by numerous attributes with different data formats, coupled with their associations. Additionally, the absence of effective validation feedback makes judging the goodness of a measurement scheme a time‐consuming and error‐prone procedure. To free analysts from tedious programming to concentrate on the exploration of MVES data, this paper introduces an interactive similarity builder, where analysts can use visual building blocks for assembling similarity measurements in a drag‐and‐drop and incremental fashion. Based on the builder, we further propose a visual analytics framework that provides multi‐granularity visual validations for measurement schemes and supports a recursive workflow for refining the focus set. We illustrate the power of our prototype through a case study and a user study with real‐world datasets. Results suggest that the system improves the efficiency of developing similarity measurements and the usefulness of exploring MVES data.

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(s|qu)eries

Cited by 42 publications

References 26 publications

DOMINO: Visual Causal Reasoning With Time-Dependent Phenomena

DOMINO: Visual Causal Reasoning With Time-Dependent Phenomena

FlexEvent: going beyond Case‐Centric Exploration and Analysis of Multivariate Event Sequences

Exploring Multivariate Event Sequences with an Interactive Similarity Builder

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