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Sirix is a temporal storage system effectively and efficiently storing snapshots of time varying (currently semi-structured) data taking full advantage of flash based drives as for instance SSDs. We not only provide several ways of navigating the tree-structure of a single revision, we also support navigation in time. Furthermore we provide a novel storage technique called sliding snapshot to circumvent intermitant full dump snapshots to fast track their in-memory reconstruction and thus we avoiding write peaks and having to read long chains of page fragments/increments/deltas. Sirix uses copy-on-write (COW) as well as an append-only storage making it an ideal candidate for flash based drives while not dropping support for erstwhile disks.

Balea creates a proxy of javax.sql.DataSource, providing dynamic routing to multiple container based databases following a On Demand Data Source Access architecture. Docker is used as manager to create, start or stop database containers on demand and link correspondent data volumes. Thanks to this approach is possible to distribute data across multiple databases keeping active only the necessary ones in each moment maximizing the use of system resources. The distribution and management of data is hidden under the hood, any process that uses this data source interface (i.e. Hibernate) will perceive it as a single source of data. It makes possible for standard SQL database like Postgresql or MySQL to be maintained, backed up or versioned with simple file system operations as the data volumes are attached dynamically to the managed Docker containers. Docker integration allows also to manage database containers in remote hosts.

--------------------------------------------------------------------------- Version for VIVO 1.8 This uses the source and test code from the Version for VIVO 1.10, edited to make it compatible with VIVO 1.8. It also borrows some code from VIVO 1.10 itself, changes the package name to avoid conflicts, and adds it here. * Vitro 1.8 uses Jena 2, not Jena 3, and Commons Lang 2, not 3, so modify the "import" statements in the Data Distribution code accordingly. * Vitro 1.8 does not have a "...webapp.utils.sparqlrunner" package, so borrow the source from Vitro 1.10, move it to a different package (just for compatibility with the 1.9 version), modify it to use Jena 2 and Commons Lang 2, and modify the Data Distribution API code to use this sparqlrunner package. * Vitro 1.9 has an earlier version of "...webapp.utils.configuration" package, so borrow the source from Vitro 1.10, move it to a different package so it won't conflict with the 1.9 version, modify it to use Jena 2 and Commons Lang 2, and modify the Data Distribution API code to use this configuration package. Make these modifications, compile, JAR it up, and deploy. --------------------------------------------------------------------------- Because VIVO 1.8 is not available as a Maven package, we have extracted the class files, JARed them up, and stored them in a file-based repository within this project. But that doesn't include any transitive dependencies, so any package that this code requires must be explicitly listed as a dependency. ---------------------------------------------------------------------------

Randoop is an automatic unit test generator for Java. It automatically creates unit tests for your classes, in JUnit format. Randoop generates unit tests using feedback-directed random test generation. In a nutshell, this technique randomly, but smartly, generates sequences of methods and constructor invocations for the classes under test, and uses the sequences to create tests. Randoop executes the sequences it creates, using the results of the execution to create assertions that capture the behavior or your program and that catch bugs. Randoop has created tests that find previously unkwon errors even in widely-used libraries including Sun and IBM's JDKs. A .NET version of Randoop, used internally at Microsoft, has been used successfully by a team of test engineers to find errors in a core .NET component that has been heavily tested for years. Randoop's combination of randomized test generation and test execution results in a highly effective test generation technique.

JSONXML project is library used to parse/format tree-like object structures in most popular text formats: XML and JSON. For parsing it accepts "java.io.Reader" and return java object. For formatting it accepts java object and "java.io.Writer". Object is generally structure that contains Map and/or List elements. Map is ordered set of named items. List is set of unnamed items. Reflection may be used to convert objects into set of maps/lists and vice versa. JSON parser is implemented explicitly. XML parser is based on SAX parser and applies only certain rules for result. Library is designed to allow various entry points for variable decisions depending on end use needs. 1. Formats - formats are used to enable locale-specific parsing/formatting of numbers and dates. 2. ReflectiveBuilder - enables reflection. Default implementation uses getters/setters only. 3. ObjectsRegistry - used to keep track of parsed or formatted objects and allow resolvable references in formatted (text) form.

DICTIONARY-BASED COMPOUND SPLITTER FOR GERMAN BananaSplit is a compound splitter for German that uses a dictionary resource. The dictionary can be either a simple word list, or a word list equipped with POS values, or an XML based dictionary. The original version was able to use GermaNet as a dictionary. This is useful in applications that rely on GermaNet anyway: no additional lexicon needs to be generated and held in memory. This was also the original purpose of BananaSplit. It served as a compound splitter for a tool called BananaRelation. BananaRelation cannot be published here as it makes heavy use of unpublished code by EML Research, Heidelberg. BananaSplit can either be used as a standalone application or it can be integrated into other Java programs (as a library). This program emerged from the seminar Lexical Semantic Processing in NLP (winter term 2005/2006) taught by Iryna Gurevych at the Seminar für Sprachwissenschaft, Tübingen. Both BananaSplit and BananaRelation were introduced to the seminar participants on 17th of December, 2005. The key algorithm for compound splitting is based on Langer (1998). The program came to use in Müller and Gurevych (2006). Please note that the program splits compounds into two parts only. Details are given in the documents linked below.