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Online spatial reasoning for complex event recognition
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AI
DOI：10.1007/s10707-026-00569-z.png)
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Abstract
En 中文
Complex Event Recognition (CER) systems have the ability to process streams of events by detecting event patterns with minimal latency. Typically, these patterns have a temporal structure, often resembling the sequential structure of regular expressions. A pattern advances to the next state by checking various conditions on the current and possibly previous events of the stream. CER systems are very efficient in tracking all the possible paths that a pattern may follow and report when a path is complete and a complex event must be reported. In some cases, the conditions that need to be checked may be spatial. For example, in maritime situational awareness, a condition may need to check whether a vessel is close to any other vessel. Such conditions are not easily expressed directly as regular expressions. For such spatio-temporal tasks, there exist dedicated modules which can evaluate this type of conditions efficiently. Thus, we can integrate such a spatio-temporal module within a CER system in order to take advantage of both worlds: the CER engine can accommodate and process complex regular expressions and delegate the evaluation of expensive spatio-temporal tasks to a dedicated module whenever it needs to. We present an approach towards such an integration. We describe how a CER engine, based on symbolic automata, can cooperate with a spatio-temporal link discovery (stLD) module such that the former can leverage the spatio-temporal capabilities of the latter. This cooperation can take place in an online manner rendering the whole system suitable for real-time processing of event streams. We discuss two different communication schemes between the CER engine and the spatio-temporal module and explore when each one should be preferred. We provide a theoretical estimation of the predicted performance of the system under each communication scheme. Our extensive experimental evaluation confirms most of our theoretical predictions.
Keywords:
Finite automata
Complex event recognition
Complex event processing
Spatial reasoning
Geospatial interlinking
Spatiotemporal link discovery
Journal
G
IF:
2.6
Papers:
20
Citations:
813