Name
Abstract | ||
|---|---|---|
Temporal abstraction methods produce high level descriptions of a parameter evolution from collections of temporal data. As the level of abstraction of the data is increased, it becomes easier to use them in a reasoning process based on high-level explicit knowledge. Furthermore, the volume of data to be treated is reduced and, subsequently, the reasoning becomes more efficient. Besides, there exist domains, such as medicine, in which there is some imprecision when describing the temporal location of data, especially when they are based on subjective observations. In this work, we describe how the use of fuzzy temporal constraint networks enables temporal imprecision to be considered in temporal abstraction. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73053-8_61 | IWINAC (1) |
Keywords | Field | DocType |
fuzzy temporal constraint networks,high-level explicit knowledge,temporal abstraction method,temporal abstraction,temporal location,fuzzy temporal constraint network,high level description,temporal imprecision,temporal data,reasoning process,parameter evolution,explicit knowledge | Abstraction,Computer science,Explicit knowledge,Fuzzy logic,Theoretical computer science,Temporal database,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
4527 | 0302-9743 | 1 |
PageRank | References | Authors |
0.34 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel Campos | 1 | 189 | 20.38 |
| J. M. Juárez | 2 | 3 | 1.06 |
| Jose Salort | 3 | 6 | 1.60 |
| José Palma | 4 | 120 | 14.28 |
| R. Marín | 5 | 71 | 6.23 |