8,841 research outputs found
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
Visualizing Historical Book Trade Data: An Iterative Design Study with Close Collaboration with Domain Experts
The circulation of historical books has always been an area of interest for
historians. However, the data used to represent the journey of a book across
different places and times can be difficult for domain experts to digest due to
buried geographical and chronological features within text-based presentations.
This situation provides an opportunity for collaboration between visualization
researchers and historians. This paper describes a design study where a variant
of the Nine-Stage Framework was employed to develop a Visual Analytics (VA)
tool called DanteExploreVis. This tool was designed to aid domain experts in
exploring, explaining, and presenting book trade data from multiple
perspectives. We discuss the design choices made and how each panel in the
interface meets the domain requirements. We also present the results of a
qualitative evaluation conducted with domain experts. The main contributions of
this paper include: 1) the development of a VA tool to support domain experts
in exploring, explaining, and presenting book trade data; 2) a comprehensive
documentation of the iterative design, development, and evaluation process
following the variant Nine-Stage Framework; 3) a summary of the insights gained
and lessons learned from this design study in the context of the humanities
field; and 4) reflections on how our approach could be applied in a more
generalizable way
Visual exploration of semantic-web-based knowledge structures
Humans have a curious nature and seek a better understanding of the world. Data, in-
formation, and knowledge became assets of our modern society through the information
technology revolution in the form of the internet. However, with the growing size of
accumulated data, new challenges emerge, such as searching and navigating in these large
collections of data, information, and knowledge. The current developments in academic
and industrial contexts target the corresponding challenges using Semantic Web techno-
logies. The Semantic Web is an extension of the Web and provides machine-readable
representations of knowledge for various domains. These machine-readable representations
allow intelligent machine agents to understand the meaning of the data and information;
and enable additional inference of new knowledge.
Generally, the Semantic Web is designed for information exchange and its processing
and does not focus on presenting such semantically enriched data to humans. Visualizations
support exploration, navigation, and understanding of data by exploiting humans’ ability
to comprehend complex data through visual representations. In the context of Semantic-
Web-Based knowledge structures, various visualization methods and tools are available,
and new ones are being developed every year. However, suitable visualizations are highly
dependent on individual use cases and targeted user groups.
In this thesis, we investigate visual exploration techniques for Semantic-Web-Based
knowledge structures by addressing the following challenges: i) how to engage various user
groups in modeling such semantic representations; ii) how to facilitate understanding using
customizable visual representations; and iii) how to ease the creation of visualizations
for various data sources and different use cases. The achieved results indicate that visual
modeling techniques facilitate the engagement of various user groups in ontology modeling.
Customizable visualizations enable users to adjust visualizations to the current needs and
provide different views on the data. Additionally, customizable visualization pipelines
enable rapid visualization generation for various use cases, data sources, and user group
Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research
Visualization for machine learning (VIS4ML) research aims to help experts
apply their prior knowledge to develop, understand, and improve the performance
of machine learning models. In conceiving VIS4ML systems, researchers
characterize the nature of human knowledge to support human-in-the-loop tasks,
design interactive visualizations to make ML components interpretable and
elicit knowledge, and evaluate the effectiveness of human-model interchange. We
survey recent VIS4ML papers to assess the generalizability of research
contributions and claims in enabling human-in-the-loop ML. Our results show
potential gaps between the current scope of VIS4ML research and aspirations for
its use in practice. We find that while papers motivate that VIS4ML systems are
applicable beyond the specific conditions studied, conclusions are often
overfitted to non-representative scenarios, are based on interactions with a
small set of ML experts and well-understood datasets, fail to acknowledge
crucial dependencies, and hinge on decisions that lack justification. We
discuss approaches to close the gap between aspirations and research claims and
suggest documentation practices to report generality constraints that better
acknowledge the exploratory nature of VIS4ML research
Towards more effective visualisations in climate services: good practices and recommendations
Visualisations are often the entry point to information that supports stakeholders’ decision- and policy-making processes. Visual displays can employ either static, dynamic or interactive formats as well as various types of representations and visual encodings, which differently affect the attention, recognition and working memory of users. Despite being well-suited for expert audiences, current climate data visualisations need to be further improved to make communication of climate information more inclusive for broader audiences, including people with disabilities. However, the lack of evidence-based guidelines and tools makes the creation of accessible visualisations challenging, potentially leading to misunderstanding and misuse of climate information by users. Taking stock of visualisation challenges identified in a workshop by climate service providers, we review good practices commonly applied by other visualisation-related disciplines strongly based on users’ needs that could be applied to the climate services context. We show how lessons learned in the fields of user experience, data visualisation, graphic design and psychology make useful recommendations for the development of more effective climate service visualisations. This includes applying a user-centred design approach, using interaction in a suitable way in visualisations, paying attention to information architecture or selecting the right type of representation and visual encoding. The recommendations proposed here can help climate service providers reduce users’ cognitive load and improve their overall experience when using a service. These recommendations can be useful for the development of the next generation of climate services, increasing their usability while ensuring that their visual components are inclusive and do not leave anyone behind.The research leading to these results received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements no. 689029 (Climateurope), 776787 (S2S4E), 776467 (MED-GOLD) and 869565 (VitiGEOSS).Peer ReviewedPostprint (published version
- …