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SPARQL (pronounced "sparkle", a recursive acronym for SPARQL Protocol and RDF Query Language) is an RDF query language, that is, a semantic query language for databases, able to retrieve and manipulate data stored in Resource Description Framework format.[2][3] It was made a standard by the RDF Data Access Working Group (DAWG) of the World Wide Web Consortium, and is recognized as one of the key technologies of the semantic web. On 15 January 2008, SPARQL 1.0 became an official W3C Recommendation,[4][5] and SPARQL 1.1 in March, 2013.[6] SPARQL allows for a query to consist of triple patterns, conjunctions, disjunctions, and optional patterns.[7] Implementations for multiple programming languages exist.[8] "SPARQL will make a huge difference" making the web machine-readable according to Sir Tim Berners-Lee in a May 2006 interview.[9] Advantages[edit] The example below demonstrates a simple query that leverages the ontology definition "foaf", often called the "friend-of-a-friend" ontology.

Jena (framework) Jena supports serialisation of RDF graphs to: 12 Devs of Xmas I’d like to start off by asking what seems like quite a simple question: What is the difference between data and information? And taking it a step further, where does knowledge fit into this definition? Not as easy to answer as you might expect, is it! Before we start getting our hands dirty creating some awesome data visualisations, it’s going to be useful to learn the definitions of – and difference between – these three words. Data is a set of unprocessed facts (the word comes from the latin datum which means “that which is given”). Information is what you get after you’ve processed the data. Knowledge is perhaps the most difficult to define, but the definition which I think makes most sense in this instance is how we use our past experiences to decide what to do based on information. So what are we going to build, and what with? There are a number of tools out there to help you to create charts and visualisations. A brief introduction to d3 There are four main parts to a d3 project:

mikeaddison93/blueprints Sparksee high-performance graph database Evaluation license Evaluate Sparksee for free downloading the evaluation version right now. The evaluation license provided has the following configuration: XSMALL size (up to 1M objects) / 1 session / DEXHA disabled If you need a bigger configuration consider the rest of license options. Free License! Required information will be only used to manage the download of the software and to provide promotional news in the future, but it will never be public or distributed to third parties. Sparsity Technologies encourages research with Sparksee by offering free licenses to PhD students, academics and other university staff, for non-commercial purposes. Sparsity Technologies encourages the use of Sparksee by offering free licenses to selected companies during their development stages. Functionalities & Benefits We offer special functionalites and benefits for bigger corporations that require high level tailored support during their development and deployment of applications using Sparksee.

Integrating AllegroGraph with MongoDB Introduction AllegroGraph has implemented extensions allowing users to query MongoDB databases using SPARQL and to execute heterogeneous joins, even though MongoDB, a NoSQL JSON document store, does not natively support joins, SPARQL or RDF-enabled linked data. In this document, we describe how to configure AllegroGraph and MongoDB to work together. Interfacing with MongoDB The steps for using MongoDB with AllegroGraph are: Installing MongoDBSynchronizing MongoDB data with AllegroGraph dataConfiguring AllegroGraph with MongoDB connection settings Please note that populating and maintaining the MongoDB database is separate from adding or deleting triples from the AllegroGraph triple-store. Installing MongoDB MongoDB is not a Franz Inc. product. In the rest of this document, we assume you have the MongoDB server installed and running on a computer you can access. Synchronizing MongoDB data with AllegroGraph You must link AllegroGraph data and MongoDB data referring to the same object. ? Footnotes

Python MongoDB Drivers This is an overview of the available tools for using Python with MongoDB. Those wishing to skip to more detailed discussion should check out the Python Driver Tutorial. Python Tools ORM Like Layers Because MongoDB is so easy to use the basic Python driver is often the best solution for many applications. However, if you need data validation, associations and other high-level data modeling functionality then ORM like layers may be desired. ORM like layers Framework Tools Several tools and adapters for integration with various Python frameworks and libraries also exist. Framework Tools mikeaddison93/mongo-tools Using MongoDB as a Graph Database - This is a presentation I gave to the NoSQL and Big Data Birmingham meetup group a couple of weeks back, and actually it’s a redux of a presentation I gave a couple of years ago at MongoDB UK. It tells the story of what has been quite a journey for us over the last couple of years, as we migrated away from a general purpose graph database technology to managing our graph data in a document database. The history here is that Talis used to be a graph database vendor of sorts; we had a PaaS offering called the Talis Platform, the genesis of which dates back to 2006. The TL;DR of that particular chapter of Talis history is that we decided in 2012 that our future was not in triple stores, so the platform was mothballed and access for external customers wound down. However, the platform, and graph data, formed the architectural cornerstone of our growing EdTech business and its products. Meanwhile the slideshare presentation is embedded below. Comments on HN

HypergraphDB - A Graph Database mongoviewer-server MongoDB + Node.js + Express + D3 Install API Server CLI MongoDB + Node.js + Express npm install -g mongoviewer-server Usage Start your MongoViewer API Server: mongoviewer-server By default it will serve on port 8080: See configuration options for customizing your API server. Example customized: mongoviewer-server --server:port 8081 --mongo:database testDb See the API documentation for more details. Installation npm install bower install node index.js Configuration Uses nconf. See the default configuration file at default_config.json. Alternatively you can use additional command line arguments: --server:port 8080 - Default 8080. For example: node index.js --server:port 8081 --mongo:database testDb Version 1 /api/v1/ Find See db.collection.find. GET /api/v1/:collection/find Query Parameters: queryoptions FindOne See db.collection.findOne. GET /api/v1/:collection/findOne Aggregate See db.collection.aggregate. GET /api/v1/:collection/aggregate pipelineoptions Example API Usage node load.js Response:

D3.js and MongoDB | Architecture and Planning I have not been shy in my love of MongoDB. The honeymoon is not over. Now I want to graph and visualize my data from MongoDB. A Bar Chart, that should be in SVG, from MongoDB data in D3.js I have not put this on OpenShift yet, but may. The Python code prints out HTML that looks like this: Not much going on here, just a simple D3.js bar chart – not even done in SVG. I return all the HTML and you get the page displayed at the top of this post. Like this: Like Loading...