Regression-Based Recovery Time Predictions in Business Continuity Management: A Public College Case Study

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dc.contributor.authorPodaras Athanasioscs
dc.contributor.authorMoirogiorgou Konstantiacs
dc.contributor.authorZervakis Michaliscs
dc.date.accessioned2021-01-14T18:04:14Z
dc.date.available20-29-2020en
dc.date.available2021-01-14T18:04:14Z
dc.date.issued2020cs
dc.date.updated04-25-2020en
dc.format.extent29cs
dc.identifier.doiDOI: 10.4018/978-1-7998-4978-0.ch020
dc.identifier.isbn978-1-79984-978-0cs
dc.identifier.urihttps://dspace.tul.cz/handle/15240/158519
dc.language.isoengcs
dc.publisherIGI GLOBALcs
dc.publisher.cityPennsylvania, USAcs
dc.relation.ispartofHandbook of Research on Global Challenges for Improving Public Services and Government Operationsen
dc.relation.ispartofseries0cs
dc.relation.urihttps://www.igi-global.com/chapter/regression-based-recovery-time-predictions-in-business-continuity-management/266112cs
dc.subjectbusiness continuity pointscs
dc.subjectbusiness functionscs
dc.subjectcritical processescs
dc.subjectcriticality rankingcs
dc.subjectmachine learningcs
dc.subjectresumption time framescs
dc.subjectrecovery complexitycs
dc.subjectavailabilitycs
dc.subjectpublic universitiescs
dc.titleRegression-Based Recovery Time Predictions in Business Continuity Management: A Public College Case Studyen
dc.titleRegression-Based Recovery Time Predictions in Business Continuity Management: A Public College Case Studycs
local.citation.epage380-408cs
local.citation.spage380-408cs
local.identifier.publikace7846
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