Thrill is a C++ framework for distributed Big Data computations on a cluster. It is currently in development and aims to be more versatile and performant than Java-based alternatives.
Appunta is a Framework for the Android platform that allows us not only to easily show geopositional information to the user, but also to create new ways of showing this information or modifying the existing ones.
Basically, you have a set of POI (Points Of Interest) located in a map (thus, with a latitude, longitude, and optionally, an altitude), and you need to show these POI and their related information to the user.
Appunta allows you, out of the box, to represent this information in two different ways, a radar or an augmented reality view. But, you can modify these components to show data in other ways or create new ways of visualizing this information.
Appunta is Open Source and anybody can freely use it. So, what are you waiting for?
Hazelcast is an open source clustering and highly scalable data distribution platform for Java, which is:
* Lightening-fast; thousands of operations/sec.
* Fail-safe; no losing data after crashes.
* Dynamically scales as new servers added.
* Super-easy to use; include a single jar.
Hazelcast is pure Java. JVMs that are running Hazelcast will dynamically cluster. Although by default Hazelcast will use multicast for discovery, it can also be configured to only use TCP/IP for environments where multicast is not available or preferred.
Katta is a scalable, failure tolerant, distributed, data storage for real time access.
Katta serves large, replicated, indices as shards to serve high loads and very large data sets. These indices can be of different type. Currently implementations are available for Lucene and Hadoop mapfiles.
* Makes serving large or high load indices easy
* Serves very large Lucene or Hadoop Mapfile indices as index shards on many servers
* Replicate shards on different servers for performance and fault-tolerance
* Supports pluggable network topologies
* Master fail-over
* Fast, lightweight, easy to integrate
* Plays well with Hadoop clusters
* Apache Version 2 License
A meta-programming approach to general data modeling.
Introduction
Meta-JB is a MetaClass/MetaObject layer providing generic access to model implementations, decoupling application logic from underlying implementation details, and allowing user interfaces (Swing, HTML, etc.) to be dynamically generated at runtime. By wrapping model implementations in MetaObject adapters, applications can interact with the model layer in a homogenous way.
Description
Meta-JB extends the Java Beans-based meta-programming concept to provide more generic access to object attributes and descriptions for any model object with an appropriate adapter. The descriptions of a class's properties (the MetaClass) and access to an object's attributes are decoupled from actual implementations by adapters implementing a Map-like name/value interface (the MetaObject). Because the thin framework is built on generic interfaces, it is not tied directly to real Java bean implementations and can also be used for anything that can access values by name. (Some examples are SQL result sets, HTTP request data, or simple hash maps.) Once a "class" has been described, the information can even be applied to different underlying implementations.
The MetaClass/MetaObject layer is a foundation for dynamically generating user-level access to application object models. Toolkits are provided for generating Swing GUIs at runtime or dynamically rendering objects as XML using the class descriptions. On the drawing board is support for generating HTML forms and views as well. Future development may also extend to a collaborative data access layer.
The Cyberinfrastructure Shell (CIShell) is an open source, community-driven platform for the integration and utilization of datasets, algorithms, tools, and computing resources. Algorithm integration support is built in for Java and most other programming
Exhibit is a lightweight structured data publishing framework that lets you create web pages with support for sorting, filtering, and rich visualizations by writing only HTML and optionally some CSS and Javascript code.
Using RhNav - Rhizome Navigation I wrote a data aggregator for Technorati's API. The first result is a video which visualizes blog domains by analysing Technorati's Cosmos (the blogs which link to a particular URL). The video is a screencast of RhNav fetc