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Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems. This artifact enables you to use MOA from within WEKA.

AuthzForce PDP extensions for JSON value processing, in particular JSONPath evaluation; it provides the XACML datatype 'urn:ow2:authzforce:feature:pdp:datatype:json' for JSON object/array values, and XACML functions that evaluate a JSON path (second parameter of standard string datatype) against an input JSON object/array (first parameter of datatype 'urn:ow2:authzforce:feature:pdp:datatype:json') and return the result of this evaluation. The return datatype depends on the actual function used: 'urn:ow2:authzforce:feature:pdp:function:string-from-json-path' returns a bag of strings, 'urn:ow2:authzforce:feature:pdp:function:integer-from-json-path' returns a bag of integers, 'urn:ow2:authzforce:feature:pdp:function:double-from-json-path' returns a bag of doubles, 'urn:ow2:authzforce:feature:pdp:function:boolean-from-json-path' returns a bag of booleans.

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems. This artifact enables you to stream data to MOA from Kafka.

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems. This artifact enables you to use MOA from within WEKA.

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems. ADAMS fork.

Generic Dao is a Java package which allows a developer to skip writing DAOs for their persistence objects when they are using Spring and/or Hibernate. The package was originally created by Aaron Zeckoski for the Evaluation System project but was repackaged to make it distributable by request. It is used in the RSF framework (http://www2.caret.cam.ac.uk/rsfwiki/). Note about the BeanUtils provided dependency: BeanUtils is not required if you are not using it in your project. Note about the Hibernate provided dependency: Hibernate is not required if you are not using it in your project.

Stochastice Performance Logic is a formalism for capturing performance assumptions. It is, for example, possible to capture assumption that newer version of a function bar is faster than the previous version or that library foobar is faster than library barfoo when rendering antialiased text. The purpose of this framework is to allow evaluation of SPL formulas inside Java applications.

This package provides two meta attribute selection evaluators - one for performing cost-sensitive attribute evaluation (CostSensitiveAttributeEval) and a second for performing cost-sensitive subset evaluation (CostSensitiveSubsetEval). Both methods take a cost matrix and a base evaluator. If the base evaluator can handle instance weights, then the training data is weighted according to the cost matrix, otherwise the training data is sampled according to the cost matrix.

Perfidix comes as a light-weight Java Benchmarking Tool which allows to make simple measurements of source code without installing heavy profilers or using special IDEs. Perfidix comes as a simple jar which has to be included in the classpath. Methods which should be benched are annotated similar to JUnit testcases. With these simple modifications fast evaluation and real-time measurements can be made with existing source code without adapting the program.