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A van Emde Boas data strucuture that achieves O(lg lg n) predecessor and successor queries with O(n) space

A universal transaction manager that achieves database-agnostic transactions and distributed transactions that span multiple databases

Truffle is a multi-language framework for executing dynamic languages that achieves high performance when combined with Graal.

Truffle is a multi-language framework for executing dynamic languages that achieves high performance when combined with Graal.

Truffle is a multi-language framework for executing dynamic languages that achieves high performance when combined with Graal.

Truffle is a multi-language framework for executing dynamic languages that achieves high performance when combined with Graal.

This is a lightweight library that allows Java developers to scale their usage of MongoDB Change Streams. Mongo Change Stream Enhancer divides Change Stream events into partitions and enables you to handle them in separate Threads, increasing throughput. It achieves that by creating a Change Stream per each partition (number is configurable) and handling each Change Stream in a dedicated Thread.

AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes. The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks. For more information, see G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.

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.