Name
Abstract | ||
|---|---|---|
The constant increase of moving object data imposes the need for modeling, processing, and mining trajectories, in order to find and understand the patterns behind these data. Existing works have mainly focused on the geometric properties of trajectories, while the semantics and the background geographic information has rarely been addressed. We claim that meaningful patterns can only be extracted from trajectories if the geographic space where trajectories are located is considered. In this paper we propose a reverse engineering framework for mining and modeling semantic trajectory patterns. Since trajectory patterns are data dependent, they may not be modeled in conceptual geographic database schemas before they are known. Therefore, we apply data mining to extract general trajectory patterns, and through a new kind of relationships, we model these patterns in the geographic database schema. A case study shows the power of the framework for modeling semantic trajectory patterns in the geographic space. |
Year | Venue | Keywords |
|---|---|---|
2007 | ER (Tutorials, Posters, Panels & Industrial Contributions) | general trajectory pattern,data mining,semantic trajectory pattern,trajectory pattern,dynamic modeling,object data,background geographic information,geographic database schema,geographic space,reverse engineering framework,mining trajectory,conceptual geographic database schema,data model,pattern,patterns,reverse engineering |
Field | DocType | Citations |
Data mining,Trajectory data mining,Computer science,Data dependent,Reverse engineering,Geographic database,System dynamics,Schema (psychology),Semantics,Trajectory,Database | Conference | 21 |
PageRank | References | Authors |
1.81 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Otávio Alvares | 1 | 595 | 35.18 |
| Vania Bogorny | 2 | 828 | 46.56 |
| Jose Antonio Fernandes de Macedo | 3 | 200 | 9.94 |
| Bart Moelans | 4 | 225 | 12.48 |
| Stefano Spaccapietra | 5 | 2603 | 565.28 |