Predicting intent behind selections in scatterplot visualizations

Cite This

Files in this item

Checksum: MD5:385fc01f6c7d537f58b5547c0e93007a

GADHAVE, Kiran, Jochen GÖRTLER, Zach CUTLER, Carolina NOBRE, Oliver DEUSSEN, Miriah MEYER, Jeff M. PHILLIPS, Alexander LEX, 2021. Predicting intent behind selections in scatterplot visualizations. In: Information Visualization. Sage Publications. 20(4), pp. 207-228. ISSN 1473-8716. eISSN 1473-8724. Available under: doi: 10.1177/14738716211038604

@article{Gadhave2021Predi-54767, title={Predicting intent behind selections in scatterplot visualizations}, year={2021}, doi={10.1177/14738716211038604}, number={4}, volume={20}, issn={1473-8716}, journal={Information Visualization}, pages={207--228}, author={Gadhave, Kiran and Görtler, Jochen and Cutler, Zach and Nobre, Carolina and Deussen, Oliver and Meyer, Miriah and Phillips, Jeff M. and Lex, Alexander} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dspace:hasBitstream rdf:resource=""/> <dc:creator>Cutler, Zach</dc:creator> <dc:language>eng</dc:language> <dc:creator>Phillips, Jeff M.</dc:creator> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:creator>Deussen, Oliver</dc:creator> <dc:creator>Nobre, Carolina</dc:creator> <dspace:isPartOfCollection rdf:resource=""/> <dc:date rdf:datatype="">2021-09-01T13:46:43Z</dc:date> <dcterms:abstract xml:lang="eng">Predicting and capturing an analyst’s intent behind a selection in a data visualization is valuable in two scenarios: First, a successful prediction of a pattern an analyst intended to select can be used to auto-complete a partial selection which, in turn, can improve the correctness of the selection. Second, knowing the intent behind a selection can be used to improve recall and reproducibility. In this paper, we introduce methods to infer analyst’s intents behind selections in data visualizations, such as scatterplots. We describe intents based on patterns in the data, and identify algorithms that can capture these patterns. Upon an interactive selection, we compare the selected items with the results of a large set of computed patterns, and use various ranking approaches to identify the best pattern for an analyst’s selection. We store annotations and the metadata to reconstruct a selection, such as the type of algorithm and its parameterization, in a provenance graph. We present a prototype system that implements these methods for tabular data and scatterplots. Analysts can select a prediction to auto-complete partial selections and to seamlessly log their intents. We discuss implications of our approach for reproducibility and reuse of analysis workflows. We evaluate our approach in a crowd-sourced study, where we show that auto-completing selection improves accuracy, and that we can accurately capture pattern-based intent.</dcterms:abstract> <dc:contributor>Deussen, Oliver</dc:contributor> <dc:contributor>Meyer, Miriah</dc:contributor> <dcterms:available rdf:datatype="">2021-09-01T13:46:43Z</dcterms:available> <dc:creator>Meyer, Miriah</dc:creator> <dcterms:hasPart rdf:resource=""/> <dc:contributor>Gadhave, Kiran</dc:contributor> <dc:contributor>Phillips, Jeff M.</dc:contributor> <dc:creator>Lex, Alexander</dc:creator> <dc:contributor>Lex, Alexander</dc:contributor> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:issued>2021</dcterms:issued> <dc:rights>terms-of-use</dc:rights> <dcterms:title>Predicting intent behind selections in scatterplot visualizations</dcterms:title> <dc:creator>Görtler, Jochen</dc:creator> <dc:creator>Gadhave, Kiran</dc:creator> <dcterms:rights rdf:resource=""/> <dcterms:isPartOf rdf:resource=""/> <bibo:uri rdf:resource=""/> <dc:contributor>Nobre, Carolina</dc:contributor> <dc:contributor>Görtler, Jochen</dc:contributor> <dc:contributor>Cutler, Zach</dc:contributor> </rdf:Description> </rdf:RDF>

Downloads since Sep 1, 2021 (Information about access statistics)

Gadhave_2-lu8hwkr6ntaj2.pdf 55

This item appears in the following Collection(s)

Search KOPS


My Account