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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.

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.

The main module contains the GeoTools public interfaces that are used by other GeoTools modules (and GeoTools applications). Where possible we make use industry standard terms as provided by OGC and ISO standards. The formal GeoTools public api consists of gt-metadata, jts and the gt-main module. The main module contains the default implementations that are available provided to other GeoTools modules using our factory system. Factories are obtained from an appropriate FactoryFinder, giving applications a chance configure the factory used using the Factory Hints facilities. FilterFactory ff = CommonFactoryFinder.getFilterFactory(); Expression expr = ff.add( expression1, expression2 ); If you find yourself using implementation specific classes chances are you doing it wrong: Expression expr = new AddImpl( expression1, expressiom2 );

Little Nanny is new type of Application to keep parents and their young children in touch. The idea is to reuse any old Smartphone (Android or iOS) as a high precision tracking device. After speaking with other parents, it was clear that there was a need for a really reliable App to help give children the freedom they need to explore and discover, while staying in touch with those who matter most. Little Nanny has a simple mission – to help kids be kids again, while giving parents an amazing new window into their children’s lives.

This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999. This classifier should produce similar results to one created by passing the base learner to Bagging, which is in turn passed to a CostSensitiveClassifier operating on minimum expected cost. The difference is that MetaCost produces a single cost-sensitive classifier of the base learner, giving the benefits of fast classification and interpretable output (if the base learner itself is interpretable). This implementation uses all bagging iterations when reclassifying training data (the MetaCost paper reports a marginal improvement when only those iterations containing each training instance are used in reclassifying that instance).