Name
Abstract | ||
|---|---|---|
Traffic monitoring or crowd management systems produce large amounts of data in the form of events that need to be processed to detect relevant incidents. Rule-based pattern recognition is a promising approach for these applications, however, increasing amounts of data as well as large and complex rule sets demand for more and more processing power and memory. In order to scale such applications, a rule-based pattern detection system needs to be distributable over multiple machines. Today's approaches are however focused on static distribution of rules or do not support reasoning over the full set of events. We propose Cloud PARTE, a complex event detection system that implements the Rete algorithm on top of mobile actors. These actors can migrate between machines to respond to changes in the work load distribution. Cloud PARTE is an extension of PARTE and offers the first rule engine specifically tailored for continuous complex event detection that is able to benefit from elastic systems as provided by cloud computing platforms. It supports fully automatic load balancing and supports online rules with access to the entire event pool. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2541329.2541338 | AGERE!@SPLASH |
Keywords | Field | DocType |
elastic system,rule-based pattern detection system,entire event pool,automatic load balancing,rule-based pattern recognition,mobile actor,crowd management system,complex event detection system,elastic complex event processing,complex rule,cloud parte,continuous complex event detection,rete,complex event processing,load balancing | Crowd management,Load balancing (computing),Computer science,Complex event processing,Rule engine,Real-time computing,Rete algorithm,Pattern detection,Distributed computing,Cloud computing | Conference |
Citations | PageRank | References |
1 | 0.37 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Janwillem Swalens | 1 | 2 | 2.42 |
| Thierry Renaux | 2 | 4 | 2.79 |
| Lode Hoste | 3 | 102 | 7.16 |
| Stefan Marr | 4 | 124 | 21.54 |
| Wolfgang De Meuter | 5 | 817 | 90.11 |