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The Apache Jackrabbit™ content repository is a fully conforming implementation of the Content Repository for Java Technology API (JCR, specified in JSR 170 and 283). A content repository is a hierarchical content store with support for structured and unstructured content, full text search, versioning, transactions, observation, and more. Apache Jackrabbit is a project of the Apache Software Foundation.

This artifact provides the implementation of the VPA adaption of the Observation-Pack learning algorithm as discussed in the PhD thesis "Foundations of Active Automata Learning: An Algorithmic Perspective" (https://dx.doi.org/10.17877/DE290R-16359) by Malte Isberner.

Protempa supports the construction of software systems for querying clinical databases, summarizing patient data, detecting clinically significant data patterns in groups of patients or individual patients, and extract, transform and load (ETL). Its special feature, temporal abstraction, supports queries and summarization of data in terms of temporal patterns. Temporal patterns enable robust querying and extraction of clinical history. These patterns include frequencies (at least two, three, etc. of some event or observation); sequences (chemotherapy followed by surgery within 180 days, a hospital encounter followed by another hospital encounter within 30 days); and overlaps (falling platelet counts in the setting of HELLP syndrome).

The Brown-UMBC Reinforcement Learning and Planning (BURLAP) Java code library is for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them. The library uses a highly flexible state/observation representation where you define states with your own Java classes, enabling support for domains that discrete, continuous, relational, or anything else. Planning and learning algorithms range from classic forward search planning to value-function-based stochastic planning and learning algorithms.