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Our first work is to create a set of tools to help in the adoption drools in our local companies. Those tools are here to make drools "enterprise ready "by adopting an industrial approach : we want to put in common a standard library that we shall all use in our drools project. This tools are of course open source and we use a friendly apache licence. Indeed, drools like any other jboss framework is a swiss knife that offers all possibilities. Our framework implements the best practices that we implemented in real projects that goes from how to monitor, report and supervise the use of drools. We hope you will enjoy our work and invite you to contact us in case of trouble. But do not forget we are a community !

ToroDB is an open source project that turns your RDBMS into a MongoDB-compatible server, supporting the MongoDB query API and MongoDB's replication, but storing your data into a reliable and trusted ACID database. ToroDB currently supports PostgreSQL as a backend, but others will be added in the future. ToroDB natively implements the MongoDB protocol, so you can use it with MongoDB tools and drivers, and features a document-to-relational mapping algorithm that transforms the JSON documents into relational tables. ToroDB also offers a native SQL layer and automatic data normalization and partitioning based on JSON documents' implicit schema.

The HTTP server module (ks-server-http) implements the Web API of the KnowledgeStore, which includes the two CRUD and SPARQL endpoints. The CRUD Endpoint supports the retrieval and manipulation of semi-structured data about resource, mention, entity and axiom records (encoded in RDF, possibly using JSONLD), and the upload / download of resource representation. The SPARQL Endpoint supports SPARQL SELECT, CONSTRUCT, DESCRIBE and ASK queries according to the W3C SPARQL protocol. The two endpoints are implemented on top of a component implementing the KnowledgeStore Java API (the Store interface), which can be either the the KnowledgeStore frontend (ks-frontend) or the Java Client. The implementation of the module is based on the Jetty Web sever (run in embedded mode) and the Jersey JAX-RS implementation. Reference documentation of the Web API is automatically generated using the Enunciate tool.

Skeletal implementation of interface javax.lang.model.util.Types, plus concrete realization backed by core Java Reflection API, akin to JEP 119. This module implements those methods in interface javax.lang.model.util.Types that pertain to the Java type system, plus it provides a method for resolving formal type parameters to actual type arguments. These methods have no equivalent in the Java Reflection API. The abstract skeletal implementation in this module is meant to be specialized for a particular javax.lang.model implementation. The abstraction makes this module well-suited to be used in projects that contain javax.lang.model as part of their domain model (for instance, implementations of alternative languages on top of the JVM).

RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many parameters, and to use normalized basis functions instead of unnormalized ones.

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents. For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule. For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule. In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.