Big Data decision support system
| dc.contributor.author | Ma, Tian J., author | |
| dc.contributor.author | Chong, Edwin, advisor | |
| dc.contributor.author | Simske, Steve, committee member | |
| dc.contributor.author | Herber, Daniel, committee member | |
| dc.contributor.author | Pezeshki, Ali, committee member | |
| dc.date.accessioned | 2023-01-21T01:24:52Z | |
| dc.date.available | 2023-01-21T01:24:52Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Each day, the amount of data produced by sensors, social and digital media, and Internet of Things is rapidly increasing. The volume of digital data is expected to be doubled within the next three years. At some point, it might not be financially feasible to store all the data that is received. Hence, if data is not analyzed as it is received, the information collected could be lost forever. Actionable Intelligence is the next level of Big Data analysis where data is being used for decision making. This thesis document describes my scientific contribution to Big Data Actionable Intelligence generations. Chapter 1 consists of my colleagues and I's contribution in Big Data Actionable Intelligence Architecture. The proven architecture has demonstrated to support real-time actionable intelligence generation using disparate data sources (e.g., social media, satellite, newsfeeds). This work has been published in the Journal of Big Data. Chapter 2 shows my original method to perform real-time detection of moving targets using Remote Sensing Big Data. This work has also been published in the Journal of Big Data and it has received an issuance of a U.S. patent. As the Field-of-View (FOV) in remote sensing continues to expand, the number of targets observed by each sensor continues to increase. The ability to track large quantities of targets in real-time poses a significant challenge. Chapter 3 describes my colleague and I's contribution to the multi-target tracking domain. We have demonstrated that we can overcome real-time tracking challenges when there are large number of targets. Our work was published in the Journal of Sensors. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Ma_colostate_0053A_17351.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/236001 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright 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.title | Big Data decision support system | |
| dc.type | Text | |
| dc.type | Image | |
| dcterms.rights.dpla | This 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.discipline | Systems Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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