Name
Abstract | ||
|---|---|---|
In recent years, there has been an increased need for the use of active systems - sys- tems required to act automatically based on events, or changes in the environment. Such systems span many areas, from active data- bases, through applications that drive the core business processes of today's enterprises. However, in many cases, the events to which the system must respond are not generated by monitoring tools, but must be inferred from other events based on complex tempo- ral predicates. In addition, in many practi- cal applications, such inference is inherently uncertain. In this paper, we introduce a for- mal framework for knowledge representation and reasoning enabling such event inference. Based on probability theory, we de…ne the representation of the associated uncertainty. In addition, we formally de…ne the probabil- ity space, and show how the relevant proba- bilities can be calculated by dynamically con- structing a Bayesian network. To the best of our knowledge, this is the …rst work that enables taking such uncertainty into account in the context of active systems. Therefore, our contribution is twofold: We formally de- …ne a probabilistic representational model for event composition, and show how to apply this model to the quanti…cation of the occur- rence probability of events. This results in a framework enabling any active system to handle such uncertainty. |
Year | Venue | Keywords |
|---|---|---|
2005 | Uncertainty in Artificial Intelligence | probability theory,knowledge representation and reasoning,business process,bayesian network |
DocType | Volume | Citations |
Conference | abs/1207.1427 | 13 |
PageRank | References | Authors |
0.89 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Segev Wasserkrug | 1 | 140 | 12.25 |
| Avigdor Gal | 2 | 1128 | 116.45 |
| Opher Etzion | 3 | 798 | 148.62 |