Name
Abstract | ||
|---|---|---|
Paper presents the Shape Movement Pattern (ShaMP) algorithm, an algorithm for extracting Movement Patterns (MPs) from network data, and a prediction mechanism whereby the identified MPs can be used to predict the nature of movement in a previously unseen network. The principal advantage offered by ShaMP is that it lends itself to parallelisation. The reported evaluation was conducted using both Massage Pass Interface (MPI) and Hadoop/MapReduce; and artificially generated and real life networks. The later extracted from the UK Cattle tracking Systems (CTS) in operation in Great Britain (GB). The evaluation indicates that very successful results can be produced, average precision, recall and F1 values of 0.965, 0.919 and 0.941 were recorded respectively. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BIGCOMP.2017.7881729 | 2017 IEEE International Conference on Big Data and Smart Computing (BigComp) |
Keywords | Field | DocType |
Big Network Data,Pattern Mining,Movement Patterns,Prediction,Hadoop,MPJ Express | Data mining,Computer science,Tracking system,Prediction algorithms,Artificial intelligence,Network data,Recall,Machine learning | Conference |
ISSN | ISBN | Citations |
2375-933X | 978-1-5090-3016-3 | 1 |
PageRank | References | Authors |
0.35 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammed Al-Zeyadi | 1 | 1 | 0.69 |
| Frans Coenen | 2 | 1283 | 131.80 |
| Alexei Lisitsa | 3 | 272 | 45.94 |