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Cristal-ise is a description-driven software platform originally developed to track the construction of the CMS ECAL detector of the LHC at CERN. This is its core library, known as the kernel, which manages business objects called Items. Items are entirely configured from data, called descriptions, held in other Items. Every change of a state in an Item is a consequence of an execution of an activity in that Item's lifecycle, meaning that Cristal-ise applications are completely traceable, even in their design. It also supports extensive versioning of Item description data, giving the system a high level of flexibility.

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "evidence-driven state merging" (EDSM) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

In brewing, mashing passes raw ingredients through hot water to activate, hydrate, and convert them for fermentation. Mash, the open source project, takes raw data payloads and provides a generic mediation process to translate the payloads into a new output. This allows configuration-driven mediation to be plugged into your application, which is especially important when dealing with ad-hoc tweaks that need to occur to payloads to add, remove, or alter them to conform to changing service payloads to/from other systems.

In brewing, a fermenter is a vessel in which unfinished ingredients become nearly finished beer. In Model Driven Architecture, Fermenter is a project that converts functional concepts into nearly finished applications. This approach allows for the quick definition and assembly of applications with the focus on functional concepts rather than technical underpinnings. Fermenter is an MDA engine that allows common concepts to be modeled and spawn any number of generated source files. In turn, this allows developers to focus more time on business logic, less time on technical underpinnings, and achieve extremely high levels of architectural conformity.