4,334 research outputs found
VizPut: Insight-Aware Imputation of Incomplete Data for Visualization Recommendation
In insight recommendation systems, obtaining timely and high-quality
recommended visual analytics over incomplete data is challenging due to the
difficulties in cleaning and processing such data. Failing to address data
incompleteness results in diminished recommendation quality, compelling users
to impute the incomplete data to a cleaned version through a costly imputation
strategy. This paper introduces VizPut scheme, an insight-aware selective
imputation technique capable of determining which missing values should be
imputed in incomplete data to optimize the effectiveness of recommended
visualizations within a specified imputation budget. The VizPut scheme
determines the optimal allocation of imputation operations with the objective
of achieving maximal effectiveness in recommended visual analytics. We evaluate
this approach using real-world datasets, and our experimental results
demonstrate that VizPut effectively maximizes the efficacy of recommended
visualizations within the user-defined imputation budget.Comment: This is part of my thesis chapter
https://espace.library.uq.edu.au/view/UQ:812c68
Visual Analytics for Understanding Draco's Knowledge Base
Draco has been developed as an automated visualization recommendation system
formalizing design knowledge as logical constraints in ASP (Answer-Set
Programming). With an increasing set of constraints and incorporated design
knowledge, even visualization experts lose overview in Draco and struggle to
retrace the automated recommendation decisions made by the system. Our paper
proposes an Visual Analytics (VA) approach to visualize and analyze Draco's
constraints. Our VA approach is supposed to enable visualization experts to
accomplish identified tasks regarding the knowledge base and support them in
better understanding Draco. We extend the existing data extraction strategy of
Draco with a data processing architecture capable of extracting features of
interest from the knowledge base. A revised version of the ASP grammar provides
the basis for this data processing strategy. The resulting incorporated and
shared features of the constraints are then visualized using a hypergraph
structure inside the radial-arranged constraints of the elaborated
visualization. The hierarchical categories of the constraints are indicated by
arcs surrounding the constraints. Our approach is supposed to enable
visualization experts to interactively explore the design rules' violations
based on highlighting respective constraints or recommendations. A qualitative
and quantitative evaluation of the prototype confirms the prototype's
effectiveness and value in acquiring insights into Draco's recommendation
process and design constraints.Comment: To be presented at VIS 202
You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems
Visual query systems (VQSs) empower users to interactively search for line
charts with desired visual patterns, typically specified using intuitive
sketch-based interfaces. Despite decades of past work on VQSs, these efforts
have not translated to adoption in practice, possibly because VQSs are largely
evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we
collaborated with experts from three diverse domains---astronomy, genetics, and
material science---via a year-long user-centered design process to develop a
VQS that supports their workflow and analytical needs, and evaluate how VQSs
can be used in practice. Our study results reveal that ad-hoc sketch-only
querying is not as commonly used as prior work suggests, since analysts are
often unable to precisely express their patterns of interest. In addition, we
characterize three essential sensemaking processes supported by our enhanced
VQS. We discover that participants employ all three processes, but in different
proportions, depending on the analytical needs in each domain. Our findings
suggest that all three sensemaking processes must be integrated in order to
make future VQSs useful for a wide range of analytical inquiries.Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25
in Vancouver, Canada. Paper will also be published in a special issue of IEEE
Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS
(InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing,
Visualization, Visualization design and evaluation method
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