Show simple item record

dc.contributor.advisorBroder, Alan
dc.contributor.authorPolonetsky, Lily
dc.identifier.citationPolonetsky, L. (2022, April 28). Finding All Co-occurrences from Spatiotemporal Data in an Efficient and Scalable Manner. Undergraduate honors thesis, Yeshiva University.en_US
dc.descriptionUndergraduate honors thesis / Open Accessen_US
dc.description.abstractPeople are constantly moving around, coming and going from one place to another. People interact with friends, coworkers, and strangers on a daily basis. This data - that is a person’s location at a given time - has the potential to tell much about the person’s social connections. This has broad-ranging applications: it has applications in fields such as epidemiology, criminology, and marketing. Previous work on computing co-occurrences from spatiotemporal data to infer social ties has been mostly mathematical and has not focused on achieving computationally efficient results. In addition, many of the approaches have relinquished finding every co-occurrence in order to compute things in an efficient and scalable manner. This work sets out to discuss a scalable and computationally efficient approach to compute all co-occurrences from a spatiotemporal dataset for the purpose of inferring social ties.en_US
dc.description.sponsorshipThe S. Daniel Abraham Honors Programen_US
dc.publisherYeshiva Universityen_US
dc.relation.ispartofseriesS. Daniel Abraham Honors Student Theses;April 28, 2022
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.subjectspatiotemporal dataen_US
dc.subjectscalable approachen_US
dc.titleFinding All Co-occurrences from Spatiotemporal Data in an Efficient and Scalable Manneren_US

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States