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Adaptive spatiotemporal data integration using distributed query relaxation over heterogeneous observational datasets

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dc.contributor.authorMitra, Saptashwa, author
dc.contributor.authorPallickara, Sangmi Lee, advisor
dc.contributor.authorPallickara, Shrideep, committee member
dc.contributor.authorLi, Kaigang, committee member
dc.date.accessioned2018-09-10T20:04:53Z
dc.date.available2018-09-10T20:04:53Z
dc.date.issued2018
dc.description.abstractCombining data from disparate sources enhances the opportunity to explore different aspects of the phenomena under consideration. However, there are several challenges in doing so effectively that include inter alia, the heterogeneity in data representation and format, collection patterns, and integration of foreign data attributes in a ready-to-use condition. In this study, we propose a scalable query-oriented data integration framework that provides estimations for spatiotemporally aligned data points. We have designed Confluence, a distributed data integration framework that dynamically generates accurate interpolations for the targeted spatiotemporal scopes along with an estimate of the uncertainty involved with such estimation. Confluence orchestrates computations to evaluate spatial and temporal query joins and to interpolate values. Our methodology facilitates distributed query evaluations with a dynamic relaxation of query constraints. Query evaluations are locality-aware and we leverage model-based dynamic parameter selection to provide accurate estimation for data points. We have included empirical benchmarks that profile the suitability of our approach in terms of accuracy, latency, and throughput at scale.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierMitra_colostate_0053N_14969.pdf
dc.identifier.urihttps://hdl.handle.net/10217/191379
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectdata integration
dc.subjectreal time queries
dc.subjectvector data
dc.subjectraster data
dc.subjectdata fusion
dc.subjectspatiotemporal
dc.titleAdaptive spatiotemporal data integration using distributed query relaxation over heterogeneous observational datasets
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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