Connections in Time Some relationships change over time. Think about your friends from high school, college, work, the city you used to live in, the ones that liked you ex- better, etc. When exploring a social network it is important that we understand not only the strength of the relationship now, but over time. We can use communication between people as a measure. I ran into a visualization that explored how multiple parties where connected by communications in multiple projects. Let’s give our network a little something special. The code to create a relationship is pretty simple, we’ll use the Batch commands again and reference the nodes we create. Let’s put it together to create our graph. Our visualization was built using D3.js and it makes a web request expecting to see a JSON object that looks like: We spent some time getting our data into our graph, now let’s get it all back out. We’ll write another query to get the incoming relationships for each node. Like this: Like Loading...
The Internet map bio4j blog | news and updates on bio4j Hello everyone, I’m happy to announce a new set of features for our tool Bio4jExplorer plus some changes in its design. I hope this may help both potential and current users to get a better understanding of Bio4j DB structure and contents. Node & Relationship properties You can now check with Bio4jExplorer the properties that has either a node or relationship in the table situated on the lower part of the interface. Name: property name Type: property type (String, int, float, String, …) Indexed: either the property is indexed or not (yes/no) Index name: name of the index associated to this property -if there’s any Index name: type of the index associated to this property -if there’s any Node & Relationship Data source You can also see now from which source a Node or Relationship was imported, some examples would be Uniprot, Uniref, GO, RefSeq… Relationships Name property Get proteins (accession and names) associated to an interpro motif (limited to 10 results) I wish you all a great weekend!
Deploying the Aurelius Graph Cluster The Aurelius Graph Cluster is a cluster of interoperable graph technologies that can be deployed on a multi-machine compute cluster. This post demonstrates how to set up the cluster on Amazon EC2 (a popular cloud service provider) with the following graph technologies: Titan is an Apache2-licensed distributed graph database that leverages existing persistence technologies such as Apache HBase and Cassandra. Titan implements the Blueprints graph API and therefore supports the Gremlin graph traversal/query language. [OLTP] Faunus is an Apache2-licensed batch analytics, graph computing framework based on Apache Hadoop. Please note the date of this publication. Cluster Configuration The examples in this post assume the reader has access to an Amazon EC2 account. 1.~$ ssh email@example.com 4.ubuntu@ip-10-117-55-34:~$ tar -xzf whirr-0.8.0.tar.gz Whirr is a cloud service agnostic tool that simplifies the creation and destruction of a compute cluster. 09. 10. 08. 09.
EC2 Instance Types Use Cases Small and mid-size databases, data processing tasks that require additional memory, caching fleets, and for running backend servers for SAP, Microsoft SharePoint, cluster computing, and other enterprise applications. *M3 instances may also launch as an Intel Xeon E5-2670 (Sandy Bridge) Processor running at 2.6 GHz. High performance front-end fleets, web-servers, batch processing, distributed analytics, high performance science and engineering applications, ad serving, MMO gaming, video-encoding, and distributed analytics. Each vCPU is a hyperthread of an Intel Xeon core for M3, C4, C3, R3, HS1, G2, I2, and D2. *M3 instances may also launch as an Intel Xeon E5-2670 (Sandy Bridge) Processor running at 2.6 GHz. † AVX, AVX2, and Enhanced Networking are only available on instances launched with HVM AMIs. Amazon EC2 instances provide a number of additional features to help you deploy, manage, and scale your applications.
HyperGraphDB - A Graph Database HyperGraphDB is a general purpose, extensible, portable, distributed, embeddable, open-source data storage mechanism. It is a graph database designed specifically for artificial intelligence and semantic web projects, it can also be used as an embedded object-oriented database for projects of all sizes. The system is reliable and in production use is several projects, including a search engine and our own Seco scripting IDE where most of the runtime environment is automatically saved as a hypergraph. HyperGraphDB is primarily what its carefully chosen name implies: a database for storing hypergraphs. While it falls into the general family of graph databases, it is hard to categorize HyperGraphDB as yet another database because much of its design evolves around providing the means to manage structure-rich information with arbitrary layers of complexity. Key Facts Possible Usage Scenarios Semantic Web projects are an obvious domain of application of HyperGraphDB.
Visual Map Of The Internet And Websites' Connectedness This is actually a worthwhile little time waster. Internet-map.net is a website that depicts websites as different sized circles based on the size of their audience, and their relatedness to one another based on their proximity on the map. It has a handy search feature too, so you don't have to go scrolling around to find the site you're looking for. Mathematically speaking, the Internet is a two-dimensional map display of links between sites on the Internet. I searched around looking at some of my favorite sites, and it was pretty neat to see what they were bunched with. Thanks to The Baron, who told me yesterday he'll get tired of reading Reddit, close the window, then open a new one and go to Reddit again.
Neo4j Blog Suggesions on large scale web applications architecture Graph database Graph databases are part of the NoSQL databases created to address the limitations of the existing relational databases. While the graph model explicitly lays out the dependencies between nodes of data, the relational model and other NoSQL database models link the data by implicit connections. Graph databases, by design, allow simple and fast retrieval of complex hierarchical structures that are difficult to model[according to whom?] in relational systems. Graph databases differ from graph compute engines. Background Graph databases, on the other hand, portrays the data as it is viewed conceptually. Graph Graph databases employ nodes, properties, and edges. A graph within graph databases is based on graph theory. Nodes represent entities or instances such as people, businesses, accounts, or any other item to be tracked. Graph models Labeled-property graph A labeled-property graph model is represented by a set of nodes, relationships, properties, and labels. Graph types
phpCallGraph - A Static Call Graph Generator for PHP