Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCesare Guariniello, Prajwal Balasubramani-
dc.contributor.authorDaniel A. DeLaurentis-
dc.identifier.citationPublished--Unlimited Distributionen_US
dc.descriptionExcerpt from the Proceedings of the Nineteenth Annual Acquisition Research Symposiumen_US
dc.description.abstractSystem-of-Systems (SoS) capability emerges from the collaboration of multiple systems, which are acquired from independent organizations. Even though the systems contribute to and benefit from the larger SoS, the data analytics and decision-making about the independent system is rarely shared across the SoS stakeholders. The objective of the research presented in this paper is to identify how the sharing of datasets and the corresponding analytics among SoS stakeholders can lead to an improved SoS capability. Our objective is to characterize how appropriate use of data sets may lead to deployment of different predictive (predicting an outcome from data) and prescriptive (determining a preferred strategy) analytics and lead to better decision outcomes at the SoS level. We build and demonstrate a framework for this objective, based on extensive literature review, which utilizes appropriate predictive and prescriptive methodologies for SoS analysis. Additionally, we propose to utilize machine learning techniques to predict the achievable SoS capability and identify sources of uncertainty derived by sharing partial datasets. A case study demonstrates the use of the framework and prospects for future improvements.en_US
dc.description.sponsorshipAcquisition Research Programen_US
dc.publisherAcquisition Research Programen_US
dc.relation.ispartofseriesAcquisition Management;SYM-AM-22-073-
dc.subjectArtificial Intelligence (AI)en_US
dc.subjectMachine Learning (ML)en_US
dc.subjectSystem-of-Systems (SoS)en_US
dc.subjectCollaboration (JCA)en_US
dc.titleA System-of-Systems Approach to Enterprise Analytics Design: Acquisition Support in the Age of Machine Learning and Artificial Intelligenceen_US
dc.typeTechnical Reporten_US
Appears in Collections:Annual Acquisition Research Symposium Proceedings & Presentations

Files in This Item:
File Description SizeFormat 
SYM-AM-22-073.pdfProceedings906.13 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.