{"id":"https://openalex.org/W7160623766","doi":"https://doi.org/10.48550/arxiv.2605.06065","title":"EventColumn: Integrating Event Sequences into Tabular Visualizations","display_name":"EventColumn: Integrating Event Sequences into Tabular Visualizations","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160623766","doi":"https://doi.org/10.48550/arxiv.2605.06065"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.06065","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06065","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.06065","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135709241","display_name":"Jakob Zethofer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zethofer, Jakob","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135728716","display_name":"Andreas Hinterreiter","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hinterreiter, Andreas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088272038","display_name":"Lukas Schieferm\u00fcller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schieferm\u00fcller, Lukas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062199290","display_name":"Belgin Mutlu","orcid":"https://orcid.org/0000-0002-6910-2780"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mutlu, Belgin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135669428","display_name":"Marc Streit","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Streit, Marc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.5974000096321106,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.5974000096321106,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.11460000276565552,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.10409999638795853,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.7457000017166138},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5734000205993652},{"id":"https://openalex.org/keywords/event-data","display_name":"Event data","score":0.5649999976158142},{"id":"https://openalex.org/keywords/data-warehouse","display_name":"Data warehouse","score":0.505299985408783},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.4749000072479248},{"id":"https://openalex.org/keywords/column","display_name":"Column (typography)","score":0.4068000018596649},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.37869998812675476},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.376800000667572}],"concepts":[{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.7457000017166138},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7105000019073486},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5734000205993652},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.5649999976158142},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.513700008392334},{"id":"https://openalex.org/C135572916","wikidata":"https://www.wikidata.org/wiki/Q193351","display_name":"Data warehouse","level":2,"score":0.505299985408783},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.4749000072479248},{"id":"https://openalex.org/C2780551164","wikidata":"https://www.wikidata.org/wiki/Q2306599","display_name":"Column (typography)","level":3,"score":0.4068000018596649},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.37869998812675476},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.31949999928474426},{"id":"https://openalex.org/C2776235265","wikidata":"https://www.wikidata.org/wiki/Q18392052","display_name":"Fragment (logic)","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2874999940395355},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C195818886","wikidata":"https://www.wikidata.org/wiki/Q5421724","display_name":"Expressive power","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2662000060081482},{"id":"https://openalex.org/C123606473","wikidata":"https://www.wikidata.org/wiki/Q907918","display_name":"Complex event processing","level":3,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.06065","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06065","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.06065","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06065","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.593545138835907}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,55,108],"introduce":[1],"EventColumn,":[2],"a":[3,16,39,80,126],"new":[4],"column":[5],"type":[6],"that":[7,95],"integrates":[8],"event-sequence":[9],"data":[10,119],"with":[11,59,87,112],"heterogeneous":[12],"tabular":[13,89],"attributes":[14,31],"into":[15],"single":[17],"unified":[18],"table.":[19],"EventColumn":[20,57,102],"lets":[21],"analysts":[22],"compare":[23,96],"event":[24,47,84,98],"sequences":[25,99],"alongside":[26],"numerical,":[27],"categorical,":[28],"and":[29,35,49,72,114,124],"temporal":[30],"at":[32],"both":[33],"instance":[34],"group":[36,43],"levels,":[37],"offering":[38],"compressed":[40],"overview,":[41],"heatmap":[42],"summaries,":[44],"alignment":[45],"by":[46],"types,":[48],"boxplots":[50],"of":[51,69,83,106],"similar":[52],"historical":[53],"items.":[54],"developed":[56],"together":[58],"collaborators":[60],"from":[61,120],"the":[62,67,76],"steel":[63,121],"industry":[64],"to":[65,79],"facilitate":[66],"analysis":[68],"production":[70,122],"events":[71],"warehouse":[73],"logistics,":[74],"but":[75],"solution":[77],"generalizes":[78],"wide":[81],"range":[82],"sequence":[85],"datasets":[86],"additional":[88],"attributes.":[90],"Unlike":[91],"most":[92],"existing":[93],"approaches":[94],"either":[97],"or":[100],"tables,":[101],"supports":[103],"simultaneous":[104],"comparison":[105],"both.":[107],"demonstrate":[109],"its":[110],"integration":[111],"Taggle":[113],"Microsoft":[115],"Power":[116],"BI":[117],"on":[118,125],"logistics":[123],"public":[127],"e-commerce":[128],"dataset.":[129]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}
