Name
Abstract | ||
|---|---|---|
To meet the intelligence community's need for link analysis tools that work together, researchers are currently investigat- ing ways of building workflows of these tools using an in- telligent system architecture. A key challenge in building a dynamic link analysis workflow environment is representing the behavior of the individual link analysis algorithms being composed. In this paper, we outline techniques for modeling algorithms that allow a system architecture to reason about their behavior and performance, individually and in combi- nation. The algorithm characterization model we propose is based on a layered approach, where the layers range from high-level qualitative descriptions of algorithms to detailed statistical descriptions of their effect on the data. Recent research and development in technology for in- telligence analysis has produced a large number of tools, each of which addresses some aspect of the link analysis |
Year | Venue | Field |
|---|---|---|
2006 | AAAI Fall Symposium: Capturing and Using Patterns for Evidence Detection | Computer science,Link analysis,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Wolverton | 1 | 264 | 28.16 |
| Ian Harrison | 2 | 1 | 1.04 |
| David Martin | 3 | 174 | 23.08 |
| Sri International | 4 | 295 | 36.03 |
| Ravenswood Ave | 5 | 0 | 0.34 |