Download JAR files tagged by produce with all dependencies

Cacher provides a simple interface (Cache) that is designed to wrap a 3rd party caching client/library (eg. spymemcached). This interface is then consumed by the FetchManager which houses logic to standarize how cached values are stored and retrieved. If a value is already cached then it is simply returned, otherwise a Fetcher (either FetchSingle or FetchMultiple) is asked how to produce the value, which is then cached for subsequent requests.

Metro is a high-performance, extensible, easy-to-use web service stack. It is a one-stop shop for all your web service needs, from the simplest hello world web service to reliable, secured, and transacted web service that involves .NET services. This module provides source files in order to produce Javadoc for Java EE. The set of files contained herein does not match what is in webservices-api.

DataVision is an Open Source reporting tool similar to Crystal Reports. Reports can be designed using a drag-and-drop GUI. They may be run, viewed, and printed from the application or exported as HTML, XML, PDF, Excel, LaTeX2e, DocBook, or tab- or comma-delimited text files. The output files produced by LaTeX2e and DocBook can in turn be used to produce PDF, text, HTML, PostScript, and more.

The JAX-SB Generator is a comprehensive framework to facilitate the use of XML in software languages such as Java. This framework intends to provide tools that will allow a developer to easily integrate XML interoperability, bind XML Schemas, and analyze XML Schemas for use with any software. Currently there is only one language and one application that is supported: XML Binding in Java. Next on the agenda is the development of analysis tools for XML Schema Documents. For instance, imagine a tool that will allow you to determine whether an XSD complies to best-practices. This tool will: 1) produce warnings for structures that do not comply to best-practices, 2) tell the developer whether the logical structure can be transformed linearly to a form that complies to best-practices, and 3) to automatically produce an XSD that maintains the logical structure of the original document but now fully complies to best-practices. This is just one of many tools that will be available shortly. In the meantime, check out the binding application.

Apache Rat is a release audit tool. It improves accuracy and efficiency when checking releases. It is heuristic in nature: making guesses about possible problems. It will produce false positives and cannot find every possible issue with a release. Its reports require interpretation. In response to demands from project quality tool developers, Rat is available as a library suitable for inclusion in tools. This POM describes that library. Note that binary compatibility is not guaranteed between 0.x releases. Apache Rat is developed by the Apache Creadur project, a language and build agnostic home for software distribution comprehension and audit tools.

The Data Highway is a service that allows data to be easily landed and consumed via REST interfaces which accept and produce JSON messages. The data is first defined using a schema and a "road" is created which will accept data messages that conform to this schema. Producers of data sets thus only need to define the structure of their data and are then able to send their data to a REST endpoint and not be too concerned with what happens next. The Data Highway will ensure that this data is made available for downstream streaming consumption and also stored reliably in a "data lake" in the cloud for access by various end users.

JAXB 2.1.x XJC Boolean-Getter Plugin This plugin causes getter methods for Boolean Objects to be called "getXX" instead of "isXX". Downloaded from http://fisheye5.atlassian.com/browse/~raw,r=1.1/jaxb2-commons/www/boolean-getter/index.html on 18/03/10 Yiannis Paschalidis Sanity4J was created to simplify running multiple static code analysis tools on the Java projects. It provides a single entry point to run all the selected tools and produce a consolidated report, which presents all findings in an easily accessible manner.

JAXB 2.1.x XJC Boolean-Getter Plugin This plugin causes getter methods for Boolean Objects to be called "getXX" instead of "isXX". Downloaded from http://fisheye5.atlassian.com/browse/~raw,r=1.1/jaxb2-commons/www/boolean-getter/index.html on 18/03/10 Yiannis Paschalidis Sanity4J was created to simplify running multiple static code analysis tools on the Java projects. It provides a single entry point to run all the selected tools and produce a consolidated report, which presents all findings in an easily accessible manner.

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).

Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution. For more information, see Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).