AllegroGraph RDFStore Web 3.0's Database

Geospatial and Temporal Reasoning AllegroGraph stores geospatial and temporal data types as native data structures. Combined with its indexing and range query mechanisms, AllegroGraph lets you perform geospatial and temporal reasoning efficiently. Social Networking Analysis AllegroGraph includes an SNA library that treats a triple-store as a graph of relations, with functions for measuring importance and centrality as well as several families of search functions. Example algorithms are nodal-degree, nodal-neighbors, ego-group, graph-density, actor-degree-centrality, group-degree-centrality, actor-closeness-centrality, group-closeness-centrality, actor betweenness-centrality, group-betweenness-centrality, page-rank-centrality, and cliques. Geospatial and temporal primitives combined with SNA functions form an Activity Recognition framework for flexibly analyzing networks and events in large volumes of structured and unstructured data.

COMBINATORIAL_BLAS: Combinatorial BLAS Library (MPI reference implementation) Authors: Aydın Buluç , John R. Gilbert , Adam Lugowski This material is based upon work supported by the National Science Foundation under Grant No. 0709385. The Combinatorial BLAS is an extensible distributed-memory parallel graph library offering a small but powerful set of linear algebra primitives specifically targeting graph analytics. The Combinatorial BLAS is also the backend of the Python Knowledge Discovery Toolbox (KDT) . Download Read release notes . Requirements : You need a recent C++ compiler (gcc version 4.4+, Intel version 11.0+ and compatible), a compliant MPI implementation, and C++11 Standard library (libstdc++ that comes with g++ has them). Documentation : This is a reference implementation of the Combinatorial BLAS Library in C++/MPI. The implementation supports both formatted and binary I/O. SpParMat <int, float, SpDCCols<int,float> > A; Sparse and dense vectors can be distributed either along the diagonal processor or to all processor. New in version 1.3 :

Clark & Parsia: Thinking Clearly Titan: Distributed Graph Database Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. In addition, Titan provides the following features: Download Titan or clone from GitHub. <dependency><groupId>com.thinkaurelius.titan</groupId><artifactId>titan-core</artifactId><version>1.0.0</version></dependency><! // who is hercules' grandfather? Continue with the Getting Started with Titan guide for a step-by-step introduction.

Dynamic Semantic Publishing with the Information Workbench twitter/cassovary EnterpriseWeb DEX high-performance graph database Introduction to the Semantic Web Introduction The Semantic Web, Web 3.0, the Linked Data Web, the Web of Data…whatever you call it, the Semantic Web represents the next major evolution in connecting information. It enables data to be linked from a source to any other source and to be understood by computers so that they can perform increasingly sophisticated tasks on our behalf. This lesson will introduce the Semantic Web, putting it in the context of both the evolution of the World Wide Web as we know it today as well as data management in general, particularly in large corporations. Objectives After completing this lesson, you will know: How Semantic Web technology fits in to the past, present, and future evolution of the Internet. Context The World Wide Web was invented by Sir Tim Berners-Lee in 1989, a surprisingly short time ago. In summary, the great advantage of Web 1.0 was that it abstracted away the physical storage and networking layers involved in information exchange between two machines. Today's Lesson

Welcome To Apache Giraph

allegrograph - high performance commercial rdf datastore, free upto 50 million tuples by balamurugan Dec 8