PGObject::Simple::Role provides an interface to the PGObject service locator for Moo and Moose Perl classes. This service locator is intended to integrate Perl objects with PostgreSQL user-defined functions, and this uses the "simple" mapping approach. Between PGObject::Simple::Rrole and PGObject::Util::DBMethod, it is possible to write fully-declarative classes in which all calculation logic is handed off to database functions.
RecDB is a recommendation engine built entirely inside PostgreSQL 9.2. It allows application developers to build recommendation applications using a wide variety of built-in recommendation algorithms such as user-user collaborative filtering, item-item collaborative filtering, and singular value decomposition. Applications powered by RecDB can produce online and flexible personalized recommendations to end-users. It is easily used and configured and allows novice developers to define a variety of recommenders that fits their application's needs in few lines of SQL. It can seamlessly integrate recommendation functionality with traditional database operations.
"fgallery" is a static photo gallery generator with no frills that has a stylish, minimalist look. It shows your photos, and nothing else. There is no server-side processing, only static generation. The resulting gallery can be uploaded anywhere without additional requirements and works with any modern browser.
Fileevent is a rules-based utility that matches files based on simple patterns and macros and performs actions on them. These actions are typically used to transfer or rename the file ready for further processing. This utility is particularly useful for batch processing environments where files to load/process might arrive on an adhoc basis. Fileevent allows them to be transferred elsewhere, retrieved from elsewhere, or renamed.
LibBi is used for state-space modelling and Bayesian inference on high-performance computer hardware, including multi-core CPUs, many-core GPUs (graphics processing units), and distributed-memory clusters. The staple methods of LibBi are based on sequential Monte Carlo (SMC), also known as particle filtering. These methods include particle Markov chain Monte Carlo (PMCMC) and SMC2. Other methods include the extended Kalman filter and some parameter optimization routines. LibBi consists of a C++ template library and a parser and compiler, written in Perl, for its own modelling language.