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Table of Contents

The Learning Agent provides means for self-managed machine learning, in other words, orchestrates the learning process. To orchestrate the process, it must be thoroughly described. The description defines all phases of the Complex-Event Machine Learning framework; therefore, the request it's called  Complex-Event Machine Learning Request (CEMLR) and execution of a CEMLR it's known as CEML Process (CEMLP). Summarizing the CEML framework, the CEMLR should describe the following parts: Feature Space Description and, Pre-Processing and Feature Extraction Rules (CEML: Data Pre-Processing Phase): Learning Description (CEML: Learning Phase), Evaluation Description (CEML: Continuous Validation Phase), and Actuation Rules (CEML: Deployment Phase). The Pre-Processing Rules describes how the fragmented raw input data or data streams are processed and aggregated. The Feature Extraction Rules define how features are extracted from pre-processed data. The Learning Description defines the selection of an Algorithm, Parameters, and the Feature Space for construction of a model. The Evaluation Description is used to construct an Evaluator. The Evaluator is attached to the model and is responsible for providing real-time performance metrics about the model and deciding if it reaches the expected scores. Finally, the Actuation Rules describe actuation of the system whenever the model reaches the expected performance scores. All steps are performed in an Execution Pipeline Environment (EPE) or distributed in a set of interconnected EPE over the network. The EPE it is usually a CEP engine which most commonly is Esper CEP engine. Finally, the CEML: Data Collection Propagation phase can be split in two: Incoming data and outgoing data. The incoming data isn't managed in the CEMLR; this is done using additional APIs of the agent (SEE HERE). The outgoing data can be partially controlled using same features of the Statement API (see statement-rest and Statement Native).