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LiquiBase is an open source (LGPL), DBMS-independent library for tracking, managing and applying database changes. LiquiBase software is built on a simple premise: All database changes (structure and data) are stored in an XML-based descriptive manner and checked into source control.

Here are some key features of “LiquiBase”:

· Supports merging of changes from multiple developers
· Works well with code branches
· A Database Refactoring IDE-Plugin (short demo)
· Isn’t DBMS-specific
· Allows you to manage production data as well as various test datasets
· Works in an environment where multiple machines will be attempting to migrate the database concurrently
· Supports automated change-applying or generation of SQL scripts that can be approved and applied by a DBA
· Supports rolling back changes
· Can create database “diff”s and can generate change logs from them
· Can auto-generate database change documentation

While we are currently focused on the core Java-based migrator functionality, additional features planned include:

· Data extraction for generating test datasets
· Unit testing infrastructure
· .Net support

Requirements:

· Java Runtime Environment (JRE) – tested with JRE6 on Ubuntu 7.10

What’s New in This Release:

· IntelliJ plug-in support.
· Support for specifying schemas in the change log.
· MaxDB/SAPDB support. Refactored code.
· The ability to specify data types as java.sql.Types.
· Support for composite foreign keys.
· Improved Maven support.
· Bugfixes.

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Doxygen project is a documentation system for C++, C, Java, Objective-C, IDL (Corba and Microsoft flavors) and to some extent PHP, C#, and D.

Here are some key features of “Doxygen”:

· It can generate an on-line documentation browser (in HTML) and/or an off-line reference manual (in $mbox{LaTeX}$ ) from a set of documented source files. There is also support for generating output in RTF (MS-Word), PostScript, hyperlinked PDF, compressed HTML, and Unix man pages.The documentation is extracted directly from the sources, which makes it much easier to keep the documentation consistent with the source code.

· You can configure doxygen to extract the code structure from undocumented source files. This is very useful to quickly find your way in large source distributions. You can also visualize the relations between the various elements by means of include dependency graphs, inheritance diagrams, and collaboration diagrams, which are all generated automatically.

· You can even `abuse’ doxygen for creating normal documentation (as I did for this manual).

Doxygen is developed under Linux and Mac OS X, but is set-up to be highly portable. As a result, it runs on most other Unix flavors as well.

What’s New in This Release:

· Support for parsing Fortran 90 and SIP interfaces (used for Python to C++ bindings) was added.
· A large number of bugs have been fixed.

Sources mirror 1 (tar.gz)

Yada is a c library that abstracts SQL databases aiming at allowing transparent use of multiple databases. The goal in mind is to make a common interface to multiple databases, thus stopping the need for application developers to write different modules for each SQL backend they want to support. It’s also useful for caching data in different database types, for example, accessing a remote Oracle database and caching results in a local MySQL one (the original proof of concept did so).

To accomplish this, it uses a compatability layer of functions to bind input and output variables, prepare statements, and retreive data. Functionality and concept are somewhat like perl’s DBI, and somewhat improved (I hope). In fact, I use it on projects where I have no need for multiple database support because I like the API.

This API seems to work well, but I want to wait a bit longer before going to version 1.0 and locking it in to make sure there aren’t any last minute changes or oversights. I know the documentation is sparse right now, I’m working on it… please don’t hesitate to email with questions and I’ll be happy to help. That will also help me to know which parts need better documentation.

The MySQL module is tested and should be stable, SQLite3 is tested but not heavily used, Oracle and PostgreSQL are both tested and used a bit however binary types do not currently work in them. Next in the pipeline is probably going to be freeTDS, etc, etc (volunteers are welcome). See README.modules in the tarball for more information.

What’s New in This Release:

· This version of yada adds va_list functions and more functionality for yadac structure binding.
· A lot of the build system was cleaned up and/or fixed

Sources mirror 1 (tar.gz)

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Howto: PPPOE with Bandwidth Management for OpenBSD is a document which explains how to build an OpenBSD based router for a small flat-sharing community with four residents.

The router should manage the available bandwidth fairly between the paying users and their guests. For the residents, it should be possible to use incoming ports for services like Skype or Emule. The howto basically addresses IT professionals with a rough idea of what OpenBSD is about.

Requirements:

· PDF reader

What’s New in This Release:

· Updated documentation to fit for OpenBSD release 4.2.

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Catalyst Howto will teach you how to create a minimal live CD with Catalyst 2.0 with automated hardware detection. I do not guarantee that it is complete and has no failures. If you find a failure or have ideas how to expand this howto send me a personal mail.

Contents

1. Installing and configurating Catalyst 2.0
2. Creating a portage snapshot
3. Creating a live cd stage 1
4. Creating a live cd stage 2

Appendix

- Troubleshooting
- Needed files

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db4o is designed to be a simple, easy-to-use, and fast, native object database. Software developers using popular Java and .NET object-oriented frameworks know that using object databases is a more natural way to get work done. Developers have three ways to storing and retrieving data: relational, non-native object and native object.

Relational databases, such as Oracle, IBM’s DB2 and Microsoft’s SQL server, store data in a tabular, row-and-column metaphor. The organization of traditional data in a relational database is easy to understand. The concept took off and today most data is stored in relational databases.

However, as object-oriented models became popular, developers were spending time trying to map objects into a relational database. Today, developers are forced to rely on relational-to-object mappers, but this technology compromise adds a layer of complexity and degrades performance. It is best suited for organizations that rely on legacy data.

Non-native object databases came on to the market in the early 1990s with great fanfare. But their promise was largely unfulfilled. Developers found the technology expensive, complex and hard to use. The first native object databases had similar shortcomings.

db4o is based on new native object database technology. db4o offers .NET and Java developers the simplest and easiest way to store objects. db4o is the only native object database designed for both Java and .NET, supplied as one single programming library (Java: .jar /.NET: .dll) to be included with developer applications.

Here are some key features of “db4o”:

· One line of code to store objects
· Native Java / .NET
· Class model == database schema
· Smooth production process
· Automatic schema versioning
· Query by example
· S.O.D.A. active queries
· 250kB footprint
· Saves more than 50% costs to develop your application
· Saves more than 50% time to release your application
· No need for training and administration
· Build lean, structured and manageable code
· Improve your software lifecycle with better refactorability and re-use

What’s New in 6.1 Stable Release:

· This release comes with an improved client/server protocol, which is now completely asynchronous and able to batch messages.
· Both changes reduce the number of client/server message round trips, and therefore improve client/server performance.

What’s New in 7.0 Development Release:

· This development release contains the basic architecture for Transparent Activation, a mechanism that only instantiates required objects from the database.
· It also includes all improvements that have been worked on throughout the last year: Tuned Embedded Client-Server, Cached IoAdapter, Batched Client-Server Messages, a new Freespace Manager, and completely revised exception handling.

Sources mirror 1 (zip) (6.4 Stable)

Sources mirror 2 (zip) (7.0 Development)

csvdiff is a perl script to compare/diff two (comma) separated files with each other. The part that is different to standard diff is, that you’ll get the number of the record where the difference occurs and the field/column which is different.

The separator can be set to the value you want it to, not just comma. Also you can to provide a third file which contains the columnnames in one(!) line separated by your separator. If you do so, columnnames are shown if a difference is found.

I wrote csvdiff to compare two database unloadfiles, but you can use it for any kind of file which has separators.

Here are some key features of “csvdiff German Docs”:

· Choosable field separator
· If you provide a file containing the fieldnames, csvdiff tells you (by name) in which field a difference occured
· Multiple column key
· Ignor upper/lower case
· Trim leading/tailing spaces
· fade out selectet columns for comparision, usefull for columns with timestamps or columns which are not interesting for you
· Sort data befor comparing it, this has only effect if for comparision without key(s)
· Ignore empty lines while importing data files
· Second output format, looks a bit like diff, but with colour

Requirements:

· Getopt::Std
· Data::Dumper, deactivated just needed for debugging

Usage:

perl csvdiff.pl -a Actual-Result -e Expectedt-Result -s “;” -c Columnames -t -i -k 2

Parameters

Usage: csvdiff.pl

Parameters: -e File1 Expected Result
-a File2 Actual Result
-c File3 Columnames in csv format (in one Line!), optional
-k Keycolumn(s) Keycolumn(s), optional (separatet by the same separator used for data)
count starts with 1, if you want to use multiple keys do like “3,1″
which meens the third and the first colums together are the unique key
-s Separator Fieldseparator, optional (default=,)
-t Trim leading and tailing blanks, optional
-v Print csvdiff version and quit
-g Grade/sort data before comparision, this has only effect when there are no key column
-i Ignor upper and lower case, optional
-f Fade out column(s) for compare, optional
-h Help, optional
-d coloured Output which looks like diff

Sources mirror 1 (pdf)

Taste is a flexible, fast collaborative filtering engine for Java. Taste takes users’ preferences for items (“tastes”) and returns estimated preferences for other items. For example, a site that sells books or CDs could easily use Taste to figure out, from past purchase data, which CDs a customer might be interested in listening to. Taste provides a rich set of components from which you can construct a customized recommender system from a selection of algorithms. Taste is designed to be enterprise-ready; it’s designed for performance, scalability and flexibility. It supports a standard EJB interface for J2EE-based applications, but Taste is not just for Java; it can be run as an external server which exposes recommendation logic to your application via web services and HTTP. · Top-level packages define the Taste interfaces to these key abstractions: · DataModel · UserCorrelation and ItemCorrelation · UserNeighborhood · Recommender Subpackages of comp.planetj.taste.impl hold implementations of these interfaces. These are the pieces from which you will build your own recommendation engine. That’s it! For the academically inclined, Taste supports both memory-based and item-based recommender systems, slope one recommenders, and a couple other experimental implementations. It does not currently support model-based recommenders. Requirements: · Java / J2SE 5.0 Optional · Apache Ant 1.5 or later, if you want to build from source or build examples. · Taste web applications require a Servlet 2.3+ container, such as Jakarta Tomcat. It may in fact work with older containers with slight modification. · Taste EJB requires an EJB 2.x container. It may work with older EJB containers with slight changes to the deployment descriptor. · MySQLJDBCDataModel implementation requires a MySQL 4.x (or later) database. Again, it may be made to work with earlier versions or other databases with slight changes. What’s New in This Release: · Replaced PreferenceTransform with PreferenceTransform2 and CorrelationTransform. PreferenceTransform was a flawed abstraction. These new interfaces are, hopefully, more sound. The original implementations of PreferenceTransform have been ported to new implementations of the new interfaces. · Removed Bayes Nets implementations — no plans to complete these in near future · Roll back change to super.setPreference() in SlopeOneRecommender, it caused a different problem. (Credit: daemmon) · AbstractJDBCDiffStorage.getAverageItemDiff() now returns null if count of item prefs is 0, fixing a bug. (Credit: daemmon)

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Ruport aka RubyReport is a pure Ruby report generation and formatting system.

It aims to prevent you from ever having to fight with the low level tasks of parsing data from various sources.

If you’d like to build professional grade custom reports quickly and easily, Ruport is the system for you.

Ruport is reporting, the Ruby Way.

What’s New in This Release:

· This release fixes an issue with embedding images in PDFs on Windows.

Sources mirror 1 (tar.gz)

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Power*MatchMaker will cleanse your data, validate and correct addresses, identify and remove duplicates, and build cross-references between source and target tables. Power*MatchMaker software provides business users with complete and accurate data, and a single 360º view of each customer, product, sales rep and business unit.

Whether you’re building a Data Warehouse, Data Mart or CRM, the Power*MatchMaker goes a long way towards ensuring the data integrity of your decision support environment or CRM database.

Here are some key features of “Power MatchMaker”:

· Transforms and cleanses Key Dimensions
· Validates and corrects address information
· Accepts user-defined data matching criteria
· User-friendly, highly-intuitive interface for Match Verification
· Allows for user confirmation of duplicates through the online verification facility
· Merges duplicate records and their related data
· Allows for Backup of impacted records prior to data merging
· Builds cross-reference tables to link source systems’ identifiers (Primary Keys) to the target database identifiers
· Runs against the entire database to perform initial data cleanup, or incorporated into the data load process

Usage:

To launch the MatchMaker from the command line, do the following:

% java -jar $MM_HOME/matchmaker.jar

(where $MM_HOME is the directory this README file is in).

For medium-to-large projects (with more than a few thousand records),
the default memory size limit (often 64MB) may not be enough. To extend the limit, use:

% java -Xmx600M -jar $MM_HOME/matchmaker.jar

That will give you 600MB of heap space to play with.

Requirements:

· Java Runtime Environment (JRE) – tested with JRE6 on Ubuntu 7.10

What’s New in This Release:

· This release adds a user preference for auto-login into a repository and new munge steps, including data type conversion steps (eg. Date-to-String) and a CSV writer step.
· It is now possible to specify a set of columns to use as the unique index, even if the table doesn’t have a primary key.
· Of particular importance to new users following the example in the documentation, the example table can now be created properly in the built-in database.
· Matching, merging, and cleansing projects are now fully functional in the built-in repository as well.

Binary mirror 1 (tar.gz)