Name
Title | ||
|---|---|---|
Predictive analytics using statistical, learning, and ensemble methods to support real-time exploration of discrete event simulations |
Abstract | ||
|---|---|---|
Discrete event simulations (DES) provide a powerful means for modeling complex systems and analyzing their behavior. DES capture all possible interactions between the entities they manage, which makes them highly expressive but also compute-intensive. These computational requirements often impose limitations on the breadth and/or depth of research that can be conducted with a discrete event simulation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.future.2015.06.013 | Future Generation Computer Systems |
Keywords | Field | DocType |
Discrete event simulation,Latin Hypercube Sampling,Distributed execution,Cloud infrastructure | Data mining,Dimensionality reduction,Predictive analytics,Computer science,Real-time computing,Curse of dimensionality,User interface,Ensemble learning,Latin hypercube sampling,Cloud computing,Distributed computing,Discrete event simulation | Journal |
Volume | ISSN | Citations |
56 | 0167-739X | 6 |
PageRank | References | Authors |
0.48 | 18 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Walid Budgaga | 1 | 11 | 2.26 |
| Matthew Malensek | 2 | 93 | 10.44 |
| Sangmi Lee Pallickara | 3 | 170 | 24.46 |
| Neil Harvey | 4 | 19 | 2.86 |
| F. Jay Breidt | 5 | 8 | 1.20 |
| Shrideep Pallickara | 6 | 837 | 92.72 |