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Near real-time processing of voluminous, high-velocity data streams for continuous sensing environments

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dc.contributor.authorHewa Raga Munige, Thilina, author
dc.contributor.authorPallickara, Shrideep, advisor
dc.contributor.authorChandrasekar, V., committee member
dc.contributor.authorGhosh, Sudipto, committee member
dc.contributor.authorPallickara, Sangmi, committee member
dc.date.accessioned2020-08-31T10:11:52Z
dc.date.available2020-08-31T10:11:52Z
dc.date.issued2020
dc.description.abstractRecent advancements in miniaturization, falling costs, networking enhancements, and battery technologies have contributed to a proliferation of networked sensing devices. Arrays of coordinated sensing devices are deployed in continuous sensing environments (CSEs) where the phenomena of interest are monitored. Observations sensed by devices in a CSE setting are encapsulated as multidimensional data streams that must subsequently be processed. The vast number of sensing devices, the high rates at which data are generated, and the high-resolutions at which these measurements are performed contribute to the voluminous, high-velocity data streams that are now increasingly pervasive. These data streams must be processed in near real-time to power user-facing applications such as visualization dashboards and monitoring systems, as well as various stages of data ingestion pipelines such as ETL pipelines. This dissertation focuses on facilitating efficient ingestion and near real-time processing of voluminous, high-velocity data streams originating in CSEs. Challenges in ingesting and processing such streams include energy and bandwidth constraints at the data sources, data transfer and processing costs, underutilized resources, and preserving the performance of stream processing applications in the presence of variable workloads and system conditions. Toward this end, we explore design principles to build a high-performant and adaptive stream processing engine to address processing challenges that are unique to CSE data streams. Further, we demonstrate how our holistic methodology based on space-efficient representations of data streams through a controlled trade-off of accuracy, can substantially alleviate stream ingestion challenges while improving the stream processing performance. We evaluate the efficacy of our methodology using real-world streaming datasets in a large-scale setup and contrast against the state-of-the-art developments in the field.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierHewaRagaMunige_colostate_0053A_16117.pdf
dc.identifier.urihttps://hdl.handle.net/10217/211777
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
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.subjectdistributed computing
dc.subjectedge computing
dc.subjectdata sketching
dc.subjectInternet of Things
dc.subjectdistributed stream processing
dc.titleNear real-time processing of voluminous, high-velocity data streams for continuous sensing environments
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.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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