Download JAR files tagged by performance with all dependencies

The AppNeta Java instrumentation agent. Contains the required jar, configuration file and native libraries for Windows and Linux. AppNeta provides application performance monitoring for Java web apps through this agent. The files contained here allow for event tracking within your application to identify latency timings between your application layers. More options can be found here: https://docs.appneta.com/java#configuring-instrumentation. For other information on our Java agent please visit https://docs.appneta.com/java.

This is an open source library that allows generating performance indicators easily for java based application. This library is configurable through an xml file (similar to log4j) in order to define the performance object types, dimensions, counters, the output format and statistics generation periodicity. Those statistics can be saved as a file (xml/json) or injected directly in a database. Then they can be correlated and analyzed through any Business Intelligence solution in order to check the application performance and quality: number of transactions, ratio of successful and failure ...

ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance and scalability, ELKI offers many data index structures such as the R*-tree that can provide major performance gains. ELKI is designed to be easy to extend for researchers and students in this domain, and welcomes contributions in particular of new methods. ELKI aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms.

MASSIS is a framework that facilitates the simulation of scenarios with multiple agents (representing people, robots, sensors, etc.) in indoor environments (i.e., in a building). MASSIS provides support for designing spaces and specifying the behavior of the elements and agents in them It is possible to define a great diversity of behaviours, from a simple sensor to the decisions of a person. MASSIS has been designed to keep this flexibility withough hindering performance. The framework is capable of supporting thousands of agents, each one with an specific behavior.

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.

The Virtuoso server module (ks-server-virtuoso) provides an implementation of the Triple Store server-side component (API in ks-triplestore) on top of the OpenLink Virtuoso triple store, a scalable native code triple store that is accessed as an external service. The Virtuoso Sesame driver, which builds on the Virtuoso JDBC driver, is used to remotely access the Virtuoso server for writing data and performing SPARQL queries. For performance reasons, data modification is performed in a non-transactional way, with the module managing possible failures in a way that trigger the external repopulation of the Virtuoso triple store starting from the master copy of data stored in the Data Store component.

This is the main aggregator for all implementation code shared between modules. Each module contains one or more service interfaces, exposed statically using "X_" prefixed classes. Core modules will define their API interfaces, their static accessor, and often a "plain-jre" default implementation which can be replaced by injection override. There must be zero external dependencies in any core modules. A low-performance, low-feature default is preferred over thick, dependency-ridden shared inherits.

An low latency, lock free, primitive bounded blocking queue backed by an int[]. This class mimics the interface of {@linkplain java.util.concurrent.BlockingQueue BlockingQueue}, however works with primitive ints rather than objects, so is unable to actually implement the BlockingQueue. This class takes advantage of the Unsafe.putOrderedObject, which allows us to create non-blocking code with guaranteed writes. These writes will not be re-orderd by instruction reordering. Under the covers it uses the faster store-store barrier, rather than the the slower store-load barrier, which is used when doing a volatile write. One of the trade off with this improved performance is we are limited to a single producer, single consumer.

DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. Comprehensive experiments have demonstrated that this technique is consistently more accurate than the base classifier, Bagging and Random Forests. Decorate also obtains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets. For more details see: P. Melville, R. J. Mooney: Constructing Diverse Classifier Ensembles Using Artificial Training Examples. In: Eighteenth International Joint Conference on Artificial Intelligence, 505-510, 2003; P. Melville, R. J. Mooney (2004). Creating Diversity in Ensembles Using Artificial Data. Information Fusion: Special Issue on Diversity in Multiclassifier Systems.