4 repositorios
Engines specialized in detecting temporal patterns and sequences within streams to trigger actions.
Distinct from Stream Processing Engines: Focuses on temporal pattern detection specifically, whereas Stream Processing Engines are general-purpose.
Explore 4 awesome GitHub repositories matching data & databases · Complex Event Processing Engines. Refine with filters or upvote what's useful.
Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite batch workloads. It functions as a stateful stream processor and a SQL stream processing engine, providing a unified runtime to execute relational queries and event-based transformations. The system is distinguished by its ability to manage persistent operator state to ensure exactly-once processing guarantees and consistency during failures. It features specialized capabilities for complex event processing to detect temporal patterns and handles out-of-order events using eve
Detects temporal patterns and sequences within data streams to trigger real-time actions.
Este proyecto es un sistema de gestión de reglas de negocio y motor de reglas diseñado para definir, ejecutar y gestionar lógica de negocio compleja desacoplada del código fuente de la aplicación. Proporciona un compilador de lógica de negocio que transforma definiciones de reglas legibles por humanos en modelos ejecutables para una evaluación de alto rendimiento en tiempo de ejecución. El sistema incluye un motor de procesamiento de eventos complejos para analizar flujos de datos en tiempo real e identificar patrones temporales, y un ejecutor de modelos y notación de decisiones que procesa lógica estructurada basada en estándares de la industria para resultados deterministas. Utiliza un motor de inferencia que soporta encadenamiento hacia adelante y hacia atrás para automatizar decisiones evaluando dependencias lógicas complejas. La plataforma cubre un amplio rango de capacidades, incluyendo gestión de sesiones de reglas, análisis de dependencias de reglas y visualización de lógica de ejecución. También cuenta con herramientas para monitoreo del rendimiento de reglas, traducción de formatos de reglas y recuperación del estado de sesión mediante una capa de persistencia conectable.
Provides a specialized engine for detecting temporal patterns and sequences within real-time data streams to trigger actions.
Grule is a business rule engine for Go that decouples complex decision-making logic from core application code. It provides a framework for defining, versioning, and executing business rules through a domain-specific language, allowing logic to be managed independently of the underlying software implementation. The engine distinguishes itself by utilizing a formal grammar-based parser and a Rete-inspired pattern matching algorithm to evaluate logic against data facts efficiently. It supports dynamic system modeling by enabling runtime updates to policies and providing thread-safe knowledge ba
Evaluates incoming data streams against predefined logic patterns to trigger automated actions.
This project is a business rules and complex event processing engine designed to manage logical decision-making and stateful workflows. It functions as a computational framework that evaluates incoming data streams and facts against conditional logic to derive new conclusions and trigger automated actions. The engine distinguishes itself through a combination of forward-chaining inference and deterministic state machine orchestration. It uses salience-based conflict resolution to prioritize rule execution and supports persistent contextual state tracking to manage long-running business proces
Correlates event sequences and patterns to trigger automated actions based on logical and time-based constraints.