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Burningwave core is an advanced, free and open source Java library that contains THE MOST POWERFUL CLASSPATH SCANNER for criteria based classes search. It’s possible to search classes by every criteria that your immagination can made by using lambda expressions. Burningwave core scan engine is highly optimized using direct allocated ByteBuffers to avoid heap saturation; searches are executed in multithreading context and are not affected by “the issue of the same class loaded by different classloaders” (normally if you try to execute "isAssignableFrom" method on a same class loaded from different classloader it return false). This library is useful also for creating classes during runtime, facilitate the use of reflection and much more...

The Infinispan Spring Integration project provides Spring integration for Infinispan, a high performance distributed cache. Its primary features are * An implementation of org.springframework.cache.CacheManager, Spring's central caching abstraction, backed by Infinispan's EmbeddedCacheManager. To be used if your Spring-powered application and Infinispan are colocated, i.e. running within the same VM. * An implementation of org.springframework.cache.CacheManager backed by Infinispan's RemoteCacheManager. To bes used if your Spring-powered application accesses Infinispan remotely, i.e. over the network. * An implementation of org.springframework.cache.CacheManager backed by a CacheContainer reference. To be used if your Spring- powered application needs access to a CacheContainer defined outside the application (e.g. retrieved from JNDI) * Spring namespace support allowing shortcut definitions for all the components above In addition, Infinispan Spring Integration offers various FactoryBeans for facilitating creation of Infinispan core classes - Cache, CacheManager, ... - within a Spring context.

A plugin helping with installing ImageJ 1.x plugins and dependencies. In ImageJ 1.x, .jar files containing an underscore are automatically scanned for classes whose names contain underscores. Every such class is considered to be a plugin. That is not true, however, for jai_codec.jar's classes. Therefore, Fiji introduced the split between plugins/ and jars/, where third-party libraries are expected to live in jars/. ImageJ2 uses the same structure since it is backwards compatible.

This is a plugin to Madura Objects. It provides a rules/constraint engine to assist with validation, deriving new values from user inputs (eg total of invoices entered on this customer) an manipulating metadata (eg because the amount is above X we make some field readonly). Note that the Java that is using the monitored objects is quite unaware of the rules layer implemented here. That means you can change rules without having to go back to your Java code, and it also means you don't have to wonder if everything implemented the same rules. Anything using that Java object has the rules (unless you turn them all off).

A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model. Note: at prediction time, a missing value is returned if no cluster is found for the instance. The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.

The rooster crows immediately before sunrise, the rooster causes the sun to rise. Using GraphX to detect possible causes and effects between time related events. We observe a true causation signal by generating random correlations over same events at different time and back propagate these scores to their most connected events. Finally, we extract the most probable causes and effects together with a score of aggressiveness (how likely an event could explain downstream effects) and sensitivity (how likely an event results from an upstream cause).

In some data processing tasks we need to use huge maps or sets that are bigger than available JVM heap space or they are loading too slow to standard Java or Scala Maps. We use TSV format (text file with tab separated columns) for persist this kind of Maps or Sets. Some columns are used as a key and rest of columns as a value. Idea of this library is simple. We can prepare these maps once (sort by key), store it to file and then use it as memory mapped file. Searching key in sorted file has log(n) complexity. If more processes uses the same memory mapped file, it exists in memory just once (on Linux). This file can be loaded lazy by OS.