Graph Databases, published by O'Reilly Media
O'Reilly Graph Databases The Definitive Book of Graph Databases. Download your FREE copy Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build
graph-database.org - graph database リソースおよび情報
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Enjoy GraphDB! - graphdb jp #1(Japanese)
Enjoy GraphDB! - graphdb jp #1(Japanese) 1. Enjoy GraphDB! ~ Python with Bulbs and Rexster ~Fungoing LLC / Satoshi MiyauchiTwitter : @bibrost 2. 宮内 聖 / Satoshi Miyauchi @bibrost 野良です MongoDB、Python、Scalaとか 最近HotなのはやはりFluent 秋葉原に別荘を借りました 11月にカリフォルニア行ってきます Page : 1 3. みなさんおつかれさまです Page : 2 4. ここまでの話でGraphDBの理解は 深まったはず Page : 3 5. しかし Page : 4 6. アプリで使えないと意味がないよね Page : 5 7. ということで Page : 6 8. Enjoy
Microsoft Research Watch: AI, NoSQL and Microsoft's Big Data Future - ReadWriteCloud
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Trinity - Microsoft Research
Trinity is a general purpose distributed graph system over a memory cloud. Memory cloud is a globally addressable, in-memory key-value store over a cluster of machines. Through the distributed in-memory storage, Trinity provides fast random data access power over a large data set. With the power of fast graph exploration and distributed parallel computing, Trinity supports both low-latency online
HyperGraphDB - A Graph Database
Key Facts The mathematical definition of a hypergraph is an extension to the standard graph concept that allows an edge to point to more than two nodes. HyperGraphDB extends this even further by allowing edges to point to other edges as well and making every node or edge carry an arbitrary value as payload. The original requirements that triggered the development of the system came from the OpenCo
The Graph Traversal Programming Pattern
A graph is a structure composed of a set of vertices (i.e.~nodes, dots) connected to one another by a set of edges (i.e.~links, lines). The concept of a graph has been around since the late 19th century, however, only in recent decades has there been a strong resurgence in the development of both graph theories and applications. In applied computing, since the late 1960s, the interlinked table str
InfoQ: グラフデータベース、NOSQL、Neo4j
VoldemortやTokyo Cabinetといったキー/バリューシステムにおけるモデリングの最小単位はキー/バリューペアになる。そして、BigTableやそのクローンでは可変数の属性をもつタプルに、CouchDBやMongoDBといったドキュメントデータベースではドキュメントになる。これに対しグラフデータベースでは、データセット全体をひとつの巨大な高密度ネットワーク構造としてモデル化する。 ここではNOSQLデータベースにおける2つの興味深いポイント、スケーラビリティと複雑さについて詳しく説明する。 1. スケーラビリティ CAP: ACID 対 BASE 従来のデータベースシステムのほとんどは、トランザクションに基づいてデータの完全性を保証する。トランザクションを使うことで、データ管理のあらゆる状況において、データの一貫性を確保している。こうしたトランザクションの性質は、ACID（A
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GraphGist Portal
Graph Databases in Life Sciences Workshop - Graph Database & Analytics
http://blog.infinitegraph.com/2011/09/06/infinitegraph-and-rdf-tuples/
Graph Databases, NOSQL and Neo4j
NoSQL Databases - NoSQL Databases - Gremlin: A Graph-Based Programming Language
Neo4j: NOSQL and the Benefits of Graph Databases
Bigdata
neo4j open source nosql graph database
The D2RQ Plattform v0.5.1 - User Manual