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Provides support for the sKWRL rule-based reasoning language. Rule programs can be uploaded and are evaluated in a forward chaining algorithm whenever a transaction commits. Since the reasoner evaluates rules directly on the database, reasoning is quite efficient.

Provides support for the sKWRL rule-based reasoning language. Rule programs can be uploaded and are evaluated in a forward chaining algorithm whenever a transaction commits. Since the reasoner evaluates rules directly on the database, reasoning is quite efficient.

The dependency-versions-check plugin verifies that all resolved versions of artifacts are at least the versions specified by the project dependencies. The Maven dependency resolution process will substitute versions for the different artifacts in a dependency tree and sometimes chooses incompatible versions which leads to difficult to detect problems. This plugin resolves all dependencies and collects any requested version. It evaluates whether the resolved versions are compatible to the requested versions and reports possible conflicts.

ENERKOs Report Engine is an Apache-POI based reporting engine that runs inside an Oracle Database. It has two main purposes: First, it creates reports as Excel worksheets from either SQL-Statements or pipelined table functions. Second, it evaluates existing Excel worksheets or new reports on the fly and presents them as a pipelined function that can be queried like any physical table.

Evaluates the worth of an attribute by using an SVM classifier. Attributes are ranked by the square of the weight assigned by the SVM. Attribute selection for multiclass problems is handled by ranking attributes for each class seperately using a one-vs-all method and then "dealing" from the top of each pile to give a final ranking. For more information see: I. Guyon, J. Weston, S. Barnhill, V. Vapnik (2002). Gene selection for cancer classification using support vector machines. Machine Learning. 46:389-422.

This Tool provides the means to monitor the performance of Spring Batch applications without the need to manipulate the respective code basis. Through Listeners and AOP it evaluates the throughput of a monitored Job and writes the result to a simble database-schema (a file-based H2 by default). It measures the performance of Job, Step, Chunk, Reader/Processor/Writer/Tasklet down to indivdual Items. Installation/Usage: 1. Add the springBatchPerformanceMonitoring-dependency to your POM 2. Add the de.viadee.spring.batch.infrastructure.Configurator.class to your Spring-Application-Context (via @Import on your Configuration) 3. Run the Job 4. By default the monitoring result is written to project-folder/target/database/monitoringDB.mv.db

Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes. The consistency of any subset can never be lower than that of the full set of attributes, hence the usual practice is to use this subset evaluator in conjunction with a Random or Exhaustive search which looks for the smallest subset with consistency equal to that of the full set of attributes. See: H. Liu, R. Setiono: A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327, 1996.

Class for building and using a decision table/naive bayes hybrid classifier. At each point in the search, the algorithm evaluates the merit of dividing the attributes into two disjoint subsets: one for the decision table, the other for naive Bayes. A forward selection search is used, where at each step, selected attributes are modeled by naive Bayes and the remainder by the decision table, and all attributes are modelled by the decision table initially. At each step, the algorithm also considers dropping an attribute entirely from the model. For more information, see: Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. In: Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS), 318-319, 2008.