Characterizing diagnoses and systems
Artificial Intelligence
A tight analysis of the greedy algorithm for set cover
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
A method for team intention inference
International Journal of Human-Computer Studies
Computational Linguistics
Abduction in well-founded semantics and generalized stable models via tabled dual programs
Theory and Practice of Logic Programming
On detecting space-time clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An event detection algebra for reactive systems
Proceedings of the 4th ACM international conference on Embedded software
A lightweight LTL runtime verification tool for java
OOPSLA '04 Companion to the 19th annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Fast Pattern Detection in Stream Data
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 1
Detection of emerging space-time clusters
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Algorithmic construction of sets for k-restrictions
ACM Transactions on Algorithms (TALG)
Verifiable agent interaction in abductive logic programming: The SCIFF framework
ACM Transactions on Computational Logic (TOCL)
The temporal logic of programs
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
A Multi-layered General Agent Model
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
AMOEBA-RT: Run-Time Verification of Adaptive Software
Models in Software Engineering
On Preferring and Inspecting Abductive Models
PADL '09 Proceedings of the 11th International Symposium on Practical Aspects of Declarative Languages
Event-based applications and enabling technologies
Proceedings of the Third ACM International Conference on Distributed Event-Based Systems
Diagnosing multiple persistent and intermittent faults
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Intention Recognition via Causal Bayes Networks Plus Plan Generation
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Modelling decision making with probabilistic causation
Intelligent Decision Technologies
Comparing LTL Semantics for Runtime Verification
Journal of Logic and Computation
Runtime Verification for LTL and TLTL
ACM Transactions on Software Engineering and Methodology (TOSEM)
Annals of Mathematics and Artificial Intelligence
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Many applications (such as system and user monitoring, runtime verification, diagnosis, observation-based decision making, intention recognition) all require to detect the occurrence of an event in a system, which entails the ability to observe the system. Observation can be costly, so it makes sense to try and reduce the number of observations, without losing full certainty about the event's actual occurrence. In this paper, we propose a formalization of this problem. We formally show that, whenever the event to be detected follows a discrete spatial or temporal pattern, then it is possible to reduce the number of observations. We discuss exact and approximate algorithms to solve the problem, and provide an experimental evaluation of them. We apply the resulting algorithms to verification of linear temporal logics formulæ. Finally, we discuss possible generalizations and extensions, and, in particular, how event detection can benefit from logic programming techniques.