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In brewing, a fermenter is a vessel in which unfinished ingredients become nearly finished beer. In Model Driven Architecture, Fermenter is a project that converts functional concepts into nearly finished applications. This approach allows for the quick definition and assembly of applications with the focus on functional concepts rather than technical underpinnings. Fermenter is an MDA engine that allows common concepts to be modeled and spawn any number of generated source files. In turn, this allows developers to focus more time on business logic, less time on technical underpinnings, and achieve extremely high levels of architectural conformity.

The bit array data structure is implemented in Java as the BitSet class. Unfortunately, this fails to scale without compression. JavaEWAH is a word-aligned compressed variant of the Java bitset class. It uses a 64-bit run-length encoding (RLE) compression scheme. The goal of word-aligned compression is not to achieve the best compression, but rather to improve query processing time. Hence, we try to save CPU cycles, maybe at the expense of storage. However, the EWAH scheme we implemented is always more efficient storage-wise than an uncompressed bitmap (implemented in Java as the BitSet class). Unlike some alternatives, javaewah does not rely on a patented scheme.

This project is a resource for the JCoRe Linnaeus Annotator. The dictionaries contained herein are used for the recognition of concrete species names in text, e.g. "human", "mouse", "n. furzeri", "c. elegans" etc as well as species hints, i.e. indirect clues to a species like the word "patient" which most likely refers to a human. Additionally to such rather clear proxies, this project also includes a small dictionary containing maximum-frequency-proxies for genus expressions like "Drosophila" which will be mapped to "D. melanogaster". For the task of only finding concrete species names in text, there is the project jcore-linnaeus-species-ae-species-dict.

The AppOptics Java instrumentation agent jar file. This artifact provides a convenient way to download the agent jar from public Maven repository. Please take note that this should NOT be included as a part of the project dependencies. Rather, this artifact should be integrated into the build process by using extra step such as the "copy" goal of "maven-dependency-plugin". For more information, please refer to https://docs.appoptics.com/kb/apm_tracing/java/install/#getting-the-agent-jar

With distributed systems being developed as a part of the cloud age. With focus on performance associated with an application dependent on one DB instance, there is a need to bring down the dependency to a database to generate unique numbers for creating a private key when lots of data needs to be stored in the database tables. This library is intended to provide a way of generating unique numbers on the fly in a JAVA based distributed system. This is not a new concept but this library helps in keeping you focused on the business logic that you need to develop for your applications rather than spending time on how this is done. Typically applications powered by RDBMS can leverage the benefit as instant scalability might not be available for these setups when the load to the system spikes.

ModelCC is a model-based parser generator (a.k.a. compiler compiler) that decouples language specification from language processing, avoiding some of the problems caused by grammar-driven parser generators. ModelCC receives a conceptual model as input, along with constraints that annotate it. It is then able to create a parser for the desired textual language and the generated parser fully automates the instantiation of the language conceptual model. ModelCC also includes a built-in reference resolution mechanism that results in abstract syntax graphs, rather than mere abstract syntax trees.

RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many parameters, and to use normalized basis functions instead of unnormalized ones.

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.

JDOM is a Java-oriented object model which models XML documents. It provides a Java-centric means of generating and manipulating XML documents. While JDOM interoperates well with existing standards such as the Simple API for XML (SAX) and the Document Object Model (DOM), it is not an abstraction layer or enhancement to those APIs. Rather, it seeks to provide a robust, light-weight means of reading and writing XML data without the complex and memory-consumptive options that current API offerings provide. built July 23 2009