Name
Abstract | ||
|---|---|---|
In this paper, we describe a new paradigm for information retrieval in which the retrieval target is based on a model. Three types of models - linear, finite state, and knowledge models are discussed. These information retrieval scenarios often arise from applications such as environmental epidemiology, oil/gas production and exploration, and precision agriculture/forestry. Traditional model-based data and information processing usually requires the processing of each and every data points. The proposed new framework, in contrast, will process the data progressively using a set of progressive models and utilize indexing techniques specialized for the model to facilitate retrieval, thus achieving a dramatic speedup. |
Year | Venue | Keywords |
|---|---|---|
2000 | ICDCS Workshop of Knowledge Discovery and Data Mining in the World-Wide Web | precision agriculture,information retrieval,environmental epidemiology,indexation,information processing |
Field | DocType | Citations |
Data mining,Human–computer information retrieval,Information retrieval,Computer science,Relevance (information retrieval),Modal | Conference | 2 |
PageRank | References | Authors |
0.37 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chung-sheng Li | 1 | 1372 | 222.33 |
| Yuan-chi Chang | 2 | 377 | 38.05 |
| Lawrence D. Bergman | 3 | 660 | 61.49 |
| John R. Smith | 4 | 4939 | 487.88 |