StorySets: Ordering Curves and Dimensions for Visualizing Uncertain Sets and Multi-Dimensional Discrete Data Article Swipe
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2504.12776
We propose a method for visualizing uncertain set systems, which differs from previous set visualization approaches that are based on certainty (an element either belongs to a set or not). Our method is inspired by storyline visualizations and parallel coordinate plots: (a) each element is represented by a vertical glyph, subdivided into bins that represent different levels of uncertainty; (b) each set is represented by an x-monotone curve that traverses element glyphs through the bins representing the level of uncertainty of their membership. Our implementation also includes optimizations to reduce visual complexity captured by the number of turns for the set curves and the number of crossings. Although several of the natural underlying optimization problems are NP-hard in theory (e.g., optimal element order, optimal set order), in practice, we can compute near-optimal solutions with respect to curve crossings with the help of a new exact algorithm for optimally ordering set curves within each element's bins. With these optimizations, the proposed method makes it easy to see set containment (the smaller set's curve is strictly below the larger set's curve). A brief design-space exploration using uncertain set-membership data, as well as multi-dimensional discrete data, shows the flexibility of the proposed approach.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2504.12776
- https://arxiv.org/pdf/2504.12776
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4417090402
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4417090402Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2504.12776Digital Object Identifier
- Title
-
StorySets: Ordering Curves and Dimensions for Visualizing Uncertain Sets and Multi-Dimensional Discrete DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-17Full publication date if available
- Authors
-
Annika Bonerath, Wouter Meulemans, Martin Nöllenburg, Alexander WolffList of authors in order
- Landing page
-
https://arxiv.org/abs/2504.12776Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2504.12776Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2504.12776Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
Full payload
| id | https://openalex.org/W4417090402 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2504.12776 |
| ids.doi | https://doi.org/10.48550/arxiv.2504.12776 |
| ids.openalex | https://openalex.org/W4417090402 |
| fwci | |
| type | preprint |
| title | StorySets: Ordering Curves and Dimensions for Visualizing Uncertain Sets and Multi-Dimensional Discrete Data |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2504.12776 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://arxiv.org/pdf/2504.12776 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2504.12776 |
| locations[1].id | doi:10.48550/arxiv.2504.12776 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2504.12776 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5061113615 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-8427-3246 |
| authorships[0].author.display_name | Annika Bonerath |
| authorships[0].author_position | middle |
| authorships[0].raw_author_name | Bonerath, Annika |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5062614232 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4978-3400 |
| authorships[1].author.display_name | Wouter Meulemans |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Meulemans, Wouter |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5071390048 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0454-3937 |
| authorships[2].author.display_name | Martin Nöllenburg |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Nöllenburg, Martin |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5014138072 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-5872-718X |
| authorships[3].author.display_name | Alexander Wolff |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Wolff, Alexander |
| authorships[3].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2504.12776 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | StorySets: Ordering Curves and Dimensions for Visualizing Uncertain Sets and Multi-Dimensional Discrete Data |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-12-07T11:11:36.759477 |
| primary_topic | |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2504.12776 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2504.12776 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2504.12776 |
| primary_location.id | pmh:oai:arXiv.org:2504.12776 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://arxiv.org/pdf/2504.12776 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2504.12776 |
| publication_date | 2025-04-17 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 179 |
| abstract_inverted_index.a | 2, 26, 47, 142 |
| abstract_inverted_index.We | 0 |
| abstract_inverted_index.an | 65 |
| abstract_inverted_index.as | 187, 189 |
| abstract_inverted_index.by | 34, 46, 64, 93 |
| abstract_inverted_index.in | 117, 126 |
| abstract_inverted_index.is | 32, 44, 62, 172 |
| abstract_inverted_index.it | 162 |
| abstract_inverted_index.of | 57, 78, 80, 96, 105, 109, 141, 196 |
| abstract_inverted_index.on | 19 |
| abstract_inverted_index.or | 28 |
| abstract_inverted_index.to | 25, 88, 135, 164 |
| abstract_inverted_index.we | 128 |
| abstract_inverted_index.(a) | 41 |
| abstract_inverted_index.(an | 21 |
| abstract_inverted_index.(b) | 59 |
| abstract_inverted_index.Our | 30, 83 |
| abstract_inverted_index.and | 37, 102 |
| abstract_inverted_index.are | 17, 115 |
| abstract_inverted_index.can | 129 |
| abstract_inverted_index.for | 4, 98, 146 |
| abstract_inverted_index.new | 143 |
| abstract_inverted_index.see | 165 |
| abstract_inverted_index.set | 7, 13, 27, 61, 100, 124, 149, 166 |
| abstract_inverted_index.the | 73, 76, 94, 99, 103, 110, 139, 158, 175, 194, 197 |
| abstract_inverted_index.(the | 168 |
| abstract_inverted_index.With | 155 |
| abstract_inverted_index.also | 85 |
| abstract_inverted_index.bins | 52, 74 |
| abstract_inverted_index.each | 42, 60, 152 |
| abstract_inverted_index.easy | 163 |
| abstract_inverted_index.from | 11 |
| abstract_inverted_index.help | 140 |
| abstract_inverted_index.into | 51 |
| abstract_inverted_index.that | 16, 53, 68 |
| abstract_inverted_index.well | 188 |
| abstract_inverted_index.with | 133, 138 |
| abstract_inverted_index.based | 18 |
| abstract_inverted_index.below | 174 |
| abstract_inverted_index.bins. | 154 |
| abstract_inverted_index.brief | 180 |
| abstract_inverted_index.curve | 67, 136, 171 |
| abstract_inverted_index.data, | 186, 192 |
| abstract_inverted_index.exact | 144 |
| abstract_inverted_index.level | 77 |
| abstract_inverted_index.makes | 161 |
| abstract_inverted_index.not). | 29 |
| abstract_inverted_index.set's | 170, 177 |
| abstract_inverted_index.shows | 193 |
| abstract_inverted_index.their | 81 |
| abstract_inverted_index.these | 156 |
| abstract_inverted_index.turns | 97 |
| abstract_inverted_index.using | 183 |
| abstract_inverted_index.which | 9 |
| abstract_inverted_index.(e.g., | 119 |
| abstract_inverted_index.curves | 101, 150 |
| abstract_inverted_index.either | 23 |
| abstract_inverted_index.glyph, | 49 |
| abstract_inverted_index.glyphs | 71 |
| abstract_inverted_index.larger | 176 |
| abstract_inverted_index.levels | 56 |
| abstract_inverted_index.method | 3, 31, 160 |
| abstract_inverted_index.number | 95, 104 |
| abstract_inverted_index.order, | 122 |
| abstract_inverted_index.plots: | 40 |
| abstract_inverted_index.reduce | 89 |
| abstract_inverted_index.theory | 118 |
| abstract_inverted_index.visual | 90 |
| abstract_inverted_index.within | 151 |
| abstract_inverted_index.NP-hard | 116 |
| abstract_inverted_index.belongs | 24 |
| abstract_inverted_index.compute | 130 |
| abstract_inverted_index.curve). | 178 |
| abstract_inverted_index.differs | 10 |
| abstract_inverted_index.element | 22, 43, 70, 121 |
| abstract_inverted_index.natural | 111 |
| abstract_inverted_index.optimal | 120, 123 |
| abstract_inverted_index.order), | 125 |
| abstract_inverted_index.propose | 1 |
| abstract_inverted_index.respect | 134 |
| abstract_inverted_index.several | 108 |
| abstract_inverted_index.smaller | 169 |
| abstract_inverted_index.through | 72 |
| abstract_inverted_index.Although | 107 |
| abstract_inverted_index.captured | 92 |
| abstract_inverted_index.discrete | 191 |
| abstract_inverted_index.includes | 86 |
| abstract_inverted_index.inspired | 33 |
| abstract_inverted_index.ordering | 148 |
| abstract_inverted_index.parallel | 38 |
| abstract_inverted_index.previous | 12 |
| abstract_inverted_index.problems | 114 |
| abstract_inverted_index.proposed | 159, 198 |
| abstract_inverted_index.strictly | 173 |
| abstract_inverted_index.systems, | 8 |
| abstract_inverted_index.vertical | 48 |
| abstract_inverted_index.algorithm | 145 |
| abstract_inverted_index.approach. | 199 |
| abstract_inverted_index.certainty | 20 |
| abstract_inverted_index.crossings | 137 |
| abstract_inverted_index.different | 55 |
| abstract_inverted_index.element's | 153 |
| abstract_inverted_index.optimally | 147 |
| abstract_inverted_index.practice, | 127 |
| abstract_inverted_index.represent | 54 |
| abstract_inverted_index.solutions | 132 |
| abstract_inverted_index.storyline | 35 |
| abstract_inverted_index.traverses | 69 |
| abstract_inverted_index.uncertain | 6, 184 |
| abstract_inverted_index.approaches | 15 |
| abstract_inverted_index.complexity | 91 |
| abstract_inverted_index.coordinate | 39 |
| abstract_inverted_index.crossings. | 106 |
| abstract_inverted_index.subdivided | 50 |
| abstract_inverted_index.underlying | 112 |
| abstract_inverted_index.x-monotone | 66 |
| abstract_inverted_index.containment | 167 |
| abstract_inverted_index.exploration | 182 |
| abstract_inverted_index.flexibility | 195 |
| abstract_inverted_index.membership. | 82 |
| abstract_inverted_index.represented | 45, 63 |
| abstract_inverted_index.uncertainty | 79 |
| abstract_inverted_index.visualizing | 5 |
| abstract_inverted_index.design-space | 181 |
| abstract_inverted_index.near-optimal | 131 |
| abstract_inverted_index.optimization | 113 |
| abstract_inverted_index.representing | 75 |
| abstract_inverted_index.uncertainty; | 58 |
| abstract_inverted_index.optimizations | 87 |
| abstract_inverted_index.visualization | 14 |
| abstract_inverted_index.implementation | 84 |
| abstract_inverted_index.optimizations, | 157 |
| abstract_inverted_index.set-membership | 185 |
| abstract_inverted_index.visualizations | 36 |
| abstract_inverted_index.multi-dimensional | 190 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |