Stochastic submodular data forgetting
| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Velegrakis, Ioannis | |
| dc.contributor.author | Rico Cuevas, Ramón | |
| dc.date.accessioned | 2023-07-22T00:02:27Z | |
| dc.date.available | 2023-07-22T00:02:27Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/44274 | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | Our ability to collect data is rapidly surpassing our ability to store it. As a result, organizations are faced with difficult decisions about what data to retain, and in what form, to meet their business goals while complying with storage restrictions. We address this retention issue in the context of relational data by exploiting continuous stochastic submodular maximization technology. | |
| dc.title | Stochastic submodular data forgetting | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.courseuu | Computing Science | |
| dc.thesis.id | 19874 |
