Name
Abstract | ||
|---|---|---|
Stream reasoning is the task of continuously deriving conclusions on streaming data. To get results instantly one evaluates a query repeatedly on recent data chunks selected by window operators. However, simply recomputing results from scratch is impractical for rule-based reasoning with semantics similar to Answer Set Programming, due to the trade-off between complexity and data throughput. To address this problem, we present a method to efficiently update models of a rule set. In particular, we show how an answer stream (model) of a LARS program can be incrementally adjusted to new or outdated input by extending truth maintenance techniques. We obtain in this way a means towards practical rule-based stream reasoning with nonmonotonic negation, various window operators and different forms of temporal reference. |
Year | Venue | Field |
|---|---|---|
2015 | IJCAI | Rule-based system,Negation,Stream reasoning,Computer science,Streaming data,Operator (computer programming),Artificial intelligence,Throughput,Answer set programming,Semantics |
DocType | Citations | PageRank |
Conference | 4 | 0.40 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harald Beck | 1 | 49 | 5.61 |
| Minh Dao-Tran | 2 | 395 | 20.39 |
| Thomas Eiter | 3 | 7238 | 532.10 |