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dc.contributor.authorKougka, Georgia-
dc.contributor.authorVarvoutas, Konstantinos-
dc.contributor.authorGounaris, Anastasios-
dc.contributor.authorTsakalidis, George-
dc.contributor.authorVergidis, Kostas-
dc.description.abstractThis work deals with redesigning business process models, e.g., in BPMN, based on cost-based optimization techniques that were initially proposed for data analytics workflows. More specifically, it discusses execution cost and cycle time improvements through treating business processes in the same way as data-centric workflows. The presented solutions are cost-based, i.e., they employ quantitative metadata and cost models. The advantage of this approach is that business processes can benefit from recent advances in data-intensive workflow optimization similarly to the manner they nowadays benefit from additional data analytics areas, e.g., in the area of process mining. Concrete use cases are presented that are capable of demonstrating that even in small, more conservative cases, the benefits are significant. The contribution of this work is to show how to automatically optimize the model structure of a given process in terms of the ordering of tasks and how to perform resource allocation under contradicting objectives. Finally, the work identifies open issues in developing end-to-end business process redesign solutions with regards to the case studies considered.en_US
dc.relation.ispartofseriesLecture Notes in Computer Scienceen_US
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.titleOn Knowledge Transfer from Cost-Based Optimization of Data-Centric Workflows to Business Process Redesignen_US
dc.typeConference Paperen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.volumetitleTransactions on Large-Scale Data- and Knowledge-Centered Systems XLIIIen_US
Appears in Collections:Department of Applied Informatics

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