Examining the Relationship between Decision Lead Time and the Predictive Performance of Proactive Complex Event Processing Rules Developed with Weather Data Decision Trees
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| Publicat a: | ProQuest Dissertations and Theses (2019) |
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ProQuest Dissertations & Theses
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| 001 | 2307397375 | ||
| 003 | UK-CbPIL | ||
| 020 | |a 9781687940186 | ||
| 035 | |a 2307397375 | ||
| 045 | 2 | |b d20190101 |b d20191231 | |
| 084 | |a 66569 |2 nlm | ||
| 100 | 1 | |a Gilson, Brian James | |
| 245 | 1 | |a Examining the Relationship between Decision Lead Time and the Predictive Performance of Proactive Complex Event Processing Rules Developed with Weather Data Decision Trees | |
| 260 | |b ProQuest Dissertations & Theses |c 2019 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a The problem addressed in this study is that the relationship between decision lead time and the predictive performance of proactive Complex Event Processing (CEP) rules developed with weather data decision trees has not been established. The purpose of the study was to examine the relationship between decision lead time and the predictive performance of proactive CEP rules developed with weather data decision trees. Data from three weather stations were used to develop decision-tree models to predict weather events at 12 lead times. Predictive performance statistics were collected, and the relationship between lead time and predictive performance of the decision-trees rules was assessed. The study identified a significant negative correlation between decision lead time and predictive performance. The study also identified non-linear patterns and changepoints in the plots of lead time versus predictive performance. Changes in the decision tree structures across the lead times and locations were also observed. Stakeholders can use the methods and results of this study to help develop proactive CEP rules using decision trees which incorporate knowledge of the relationship between decision lead time and predictive performance for their application | |
| 653 | |a Computer science | ||
| 653 | |a Applied mathematics | ||
| 653 | |a Systems science | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2019) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2307397375/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2307397375/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |