abstract: Intelligence analysts’ work has become progressively complex due to increasing security threats and data availability. In order to study “big” data exploration within the intelligence domain the intelligence analyst task was abstracted and replicated in a laboratory (controlled environment). Participants used a computer interface and movie database to determine the opening weekend gross movie earnings of three pre-selected movies. Data consisted of Twitter tweets and predictive models. These data were displayed in various formats such as graphs, charts, and text. Participants used these data to make their predictions. It was expected that teams (a team is a group with members who have different specialties and who work interdependently) would outperform individuals and groups. That is, teams would be significantly better at predicting “Opening Weekend Gross” than individuals or groups. Results indicated that teams outperformed individuals and groups in the first prediction, under performed in the second prediction, and performed better than individuals in the third prediction (but not better than groups). Insights and future directions are discussed. / Dissertation/Thesis / Masters Thesis Engineering 2016
| Identifer | oai:union.ndltd.org:asu.edu/item:40353 |
| Date | January 2016 |
| Contributors | Buchanan, Verica (Author), Cooke, Nancy J. (Advisor), Maciejewski, Ross (Committee member), Craig, Scotty D. (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 132 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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