4 dépôts
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.
Ce projet est un système de gestion de règles métier et un moteur de règles conçu pour définir, exécuter et gérer une logique métier complexe découplée du code source de l'application. Il fournit un compilateur de logique métier qui transforme des définitions de règles lisibles par l'humain en modèles exécutables pour une évaluation haute performance au runtime. Le système inclut un moteur de traitement d'événements complexes pour analyser les flux de données en temps réel afin d'identifier des modèles temporels, ainsi qu'un exécuteur de modèles et notations de décision qui traite une logique structurée basée sur des standards industriels pour des résultats déterministes. Il utilise un moteur d'inférence prenant en charge le chaînage avant et arrière pour automatiser les décisions en évaluant des dépendances logiques complexes. La plateforme couvre un large éventail de capacités, incluant la gestion de sessions de règles, l'analyse de dépendances de règles et la visualisation de la logique d'exécution. Elle propose également des outils pour le monitoring des performances des règles, la traduction de formats de règles et la récupération de l'état de session via une couche de persistance enfichable.
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.