Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations

Wongsuphasawat, Kanit; Moritz, Dominik; Anand, Anushka; Mackinlay, Jock D.; Howe, Bill; Heer, Jeffrey

doi:10.1109/tvcg.2015.2467191

Cited by 412 publications

(458 citation statements)

References 34 publications

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“…Data visualization management systems that automate and recommend visualizations to users have recently become a topic of active interest in the database and humancomputer interaction community [69]. Recent systems including SeeDB [50], Voyager [67], and ZenVisage [59] focus on recommending visualizations for large-scale data sets, particularly for exploratory analysis of relational data [47]. In this paper, we focus on the visualization of deviations within time series.…”

Section: Related Workmentioning

confidence: 99%

Asap

Rong¹,

Bailis²

2017

Proc. VLDB Endow.

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Time series visualization of streaming telemetry (i.e., charting of key metrics such as server load over time) is increasingly prevalent in modern data platforms and applications. However, many existing systems simply plot the raw data streams as they arrive, often obscuring large-scale trends due to small-scale noise. We propose an alternative: to better prioritize end users' attention, smooth time series visualizations as much as possible to remove noise, while retaining large-scale structure to highlight significant deviations. We develop a new analytics operator called ASAP that automatically smooths streaming time series by adaptively optimizing the trade-off between noise reduction (i.e., variance) and trend retention (i.e., kurtosis). We introduce metrics to quantitatively assess the quality of smoothed plots and provide an efficient search strategy for optimizing these metrics that combines techniques from stream processing, user interface design, and signal processing via autocorrelation-based pruning, pixel-aware preaggregation, and ondemand refresh. We demonstrate that ASAP can improve users' accuracy in identifying long-term deviations in time series by up to 38.4% while reducing response times by up to 44.3%. Moreover, ASAP delivers these results several orders of magnitude faster than alternative search strategies.

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Section: Related Workmentioning

confidence: 99%

Asap

Rong¹,

Bailis²

2017

Proc. VLDB Endow.

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“…There has been work such as [19,20] on recommending visual encodings for different types of data. Tools like VISO [25] use semantic ontologies to recommend visualizations, while those like Voyager [39] focus on the interface design aspects for exploratory data analysis. Another type of visualization recommendation is performed by systems like Profiler and Scorpion [15,40] which detect specific patterns (e.g.…”

Section: Related Workmentioning

confidence: 99%

S ee DB

et al. 2015

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Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose SEEDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SEEDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most "useful" or "interesting". The two major obstacles in recommending interesting visualizations are (a) scale: evaluating a large number of candidate visualizations while responding within interactive time scales, and (b) utility: identifying an appropriate metric for assessing interestingness of visualizations. For the former, SEEDB introduces pruning optimizations to quickly identify high-utility visualizations and sharing optimizations to maximize sharing of computation across visualizations. For the latter, as a first step, we adopt a deviationbased metric for visualization utility, while indicating how we may be able to generalize it to other factors influencing utility. We implement SEEDB as a middleware layer that can run on top of any DBMS. Our experiments show that our framework can identify interesting visualizations with high accuracy. Our optimizations lead to multiple orders of magnitude speedup on relational row and column stores and provide recommendations at interactive time scales. Finally, we demonstrate via a user study the effectiveness of our deviation-based utility metric and the value of recommendations in supporting visual analytics.

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“…We discovered that VisUnit cannot be used to replicate the controlled user studies reported by 16% of the 101 surveyed papers that have appeared in IEEE Information Visualization (InfoVis) between 1995 and 2015 such as these papers [117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132].…”

Section: Discussion Of the Limitations Of Visunitmentioning

confidence: 99%

“…For example, studies that involve eye-tracking or studies that use think-aloud protocols cannot currently be supported by VisUnit [121,129,130,131]. Similarly, user studies that measure other variables apart from accuracy and completion time cannot be supported effectively by VisUnit.…”

Section: User Studies Involving Complex Visualization Tools or Non-wementioning

Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations

Cited by 412 publications

References 34 publications

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