Name
Abstract | ||
|---|---|---|
The increasing pervasiveness of location-acquisition tech- nologies (GPS, GSM networks, etc.) is leading to the collec- tion of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we move towards this direction and develop an extension of the sequential pattern mining paradigm that analyzes the trajectories of moving objects. We introduce trajectory patterns as concise descriptions of frequent behaviours, in terms of both space (i.e., the regions of space visited during movements) and time (i.e., the duration of movements). In this setting, we provide a general formal statement of the novel mining problem and then study several different in- stantiations of different complexity. The various approaches are then empirically evaluated over real data and synthetic benchmarks, comparing their strengths and weaknesses. |
Year | Venue | Keywords |
|---|---|---|
2007 | Encyclopedia of GIS | sequential pattern mining paradigm,trajectory pattern mining,different complexity,novel mining problem,frequent behaviour,large spatio-temporal datasets,gsm network,trajectory patterns,different instantiations,concise description,novel application,spatio-temporal data mining,general formal statement,sequential pattern mining |
Field | DocType | Citations |
USable,Data mining,GSM,Computer science,Global Positioning System,Artificial intelligence,Strengths and weaknesses,Sequential Pattern Mining,Machine learning,Trajectory | Conference | 224 |
PageRank | References | Authors |
13.22 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fosca Giannotti | 1 | 2948 | 253.39 |
| Mirco Nanni | 2 | 1412 | 84.47 |
| Fabio Pinelli | 3 | 972 | 50.96 |
| Dino Pedreschi | 4 | 3083 | 244.47 |