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How RDF Databases Differ from Other NoSQL Solutions - The Datagr

How RDF Databases Differ from Other NoSQL Solutions - The Datagr
This started out as an answer at Semantic Overflow on how RDF database systems differ from other currently available NoSQL solutions. I've here expanded the answer somewhat and added some general-audience context. RDF database systems are the only standardized NoSQL solutions available at the moment, being built on a simple, uniform data model and a powerful, declarative query language. These systems offer data portability and toolchain interoperability among the dozens of competing implementations that are available at present, avoiding any need to bet the farm on a particular product or vendor. In case you're not familiar with the term, NoSQL ("Not only SQL") is a loosely-defined umbrella moniker for describing the new generation of non-relational database systems that have sprung up in the last several years. These systems tend to be inherently distributed, schema-less, and horizontally scalable. RDF database systems form the largest subset of this last NoSQL category.

Storing RDF data into HBase? - Semantic Overflow I can't comment specifically on HBase, but I have implemented RDF storage for Cassandra which has a very similar BigTable-inspired data model. You basically have two options in how to store RDF data in wide-column databases like HBase and Cassandra: the resource-centric approach and the statement-centric approach. In the statement-oriented approach, each RDF statement corresponds to a row key (for instance, a UUID) and contains subject, predicate and object columns. The above is a relatively simple mapping to implement but suffers from some problems, notably the fact that preventing the creation of duplicate statements (an important RDF semantic) means having to do a read before doing a write, which at least in Cassandra quickly becomes a performance bottleneck as writes are much faster than reads. In view of the previous considerations, the resource-oriented approach is generally a better natural fit for storing RDF data in wide-column databases.

Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase comparison :: KKovacs While SQL databases are insanely useful tools, their monopoly in the last decades is coming to an end. And it's just time: I can't even count the things that were forced into relational databases, but never really fitted them. (That being said, relational databases will always be the best for the stuff that has relations.) But, the differences between NoSQL databases are much bigger than ever was between one SQL database and another. In this light, here is a comparison of Open Source NOSQL databases: The most popular ones # Redis # Best used: For rapidly changing data with a foreseeable database size (should fit mostly in memory). For example: To store real-time stock prices. Cassandra # Written in: JavaMain point: Store huge datasets in "almost" SQLLicense: ApacheProtocol: CQL3 & ThriftCQL3 is very similar to SQL, but with some limitations that come from the scalability (most notably: no JOINs, no aggregate functions.)CQL3 is now the official interface. MongoDB # ElasticSearch # CouchDB #

OpenLink Virtuoso - RDF Views Demo.demo.Orders: Northwind RDF View Definition prefix northwind: < ... create iri class northwind:Order < (in order_id integer not null) . ... alter quad storage virtrdf:DefaultQuadStorage ... from Demo.demo.Customers as customers from Demo.demo.Orders as orders . create virtrdf:NorthwindDemo as graph iri (" { ... northwind:Order (orders.OrderID) a northwind:Order as virtrdf:Order-Order ; northwind:orderDate orders.OrderDate as virtrdf:Order-order_date ; northwind:requiredDate orders.RequiredDate as virtrdf:Order-required_date ; ... northwind:has_customer northwind:Customer (orders.CustomerID) as virtrdf:Order-order_has_customer northwind:has_employee northwind:Employee (orders.EmployeeID) as virtrdf:Order-order_has_employee ; ... } }

pius's rdf-mongo at master - GitHub Visual Representation of SQL Joins Introduction This is just a simple article visually explaining SQL JOINs. Background I'm a pretty visual person. Using the code I am going to discuss seven different ways you can return data from two relational tables. For the sake of this article, I'll refer to 5, 6, and 7 as LEFT EXCLUDING JOIN, RIGHT EXCLUDING JOIN, and OUTER EXCLUDING JOIN, respectively. Inner JOIN This is the simplest, most understood Join and is the most common. Hide Copy Code SELECT <select_list> FROM Table_A A INNER JOIN Table_B B ON A.Key = B.Key Left JOIN This query will return all of the records in the left table (table A) regardless if any of those records have a match in the right table (table B). SELECT <select_list>FROM Table_A A LEFT JOIN Table_B B ON A.Key = B.Key Right JOIN This query will return all of the records in the right table (table B) regardless if any of those records have a match in the left table (table A). SELECT <select_list>FROM Table_A A RIGHT JOIN Table_B B ON A.Key = B.Key Outer JOIN Examples

Quick Intro to RDF Quick Intro to RDF This is a really brief introduction to Resource Description Framework (RDF). You might also be interested in... For a more detailed look at RDF, see RDF in Depth on this site, which this page was based on. RDF is a method for expressing knowledge in a decentralized world and is the foundation of the Semantic Web, in which computer applications make use of distributed, structured information spread throughout the Web. The Big Picture RDF is a general method to decompose any type of knowledge into small pieces, with some rules about the semantics, or meaning, of those pieces. @prefix : < . The meaning is obvious. If you know XML, here's a brief comparison. But you don't have to use XML. What really sets RDF apart from XML and other things is that RDF is designed to represent knowledge in a distributed world. Consider this second document of RDF: This RDF document defines what it means to be an aunt, in terms of two other relations. So why use RDF?

RDFgrid - Map/Reduce-based Linked Data Processing with Hadoop Semantic Web Standards Evri Ties the Knot with Twine — Twine CEO Comments and Analysis Today I am announcing that my company, Radar Networks, and its flagship product, Twine, have been acquired by Evri. TechCrunch broke the story here. This acquisition consolidates two leading providers of semantic discovery and search. As the CEO and founder of Radar Networks and, it is difficult to describe what it feels like to have reached this milestone during what has been a tumultuous period of global recession. Selling was not something we had planned on doing at this time, but given the economy and the fact that is a long-term project that will require significant ongoing investment and work to reach our goals, it is the best decision for the business and our shareholders. While we received several offers for the company, and were in discussions about M&A with multiple industry leading companies in media, search and social software, we eventually selected Evri. The Twine team is joining Evri to continue our work there. What I’m Doing Next Deborah L.

C Semantic Web FAQ The term “rules” in the context of the Semantic Web refers to elements of logic programming and rule based systems bound to Semantic Web data. Rules offer a way to express, for example, constraints on the relationships defined by by RDF, or may be used to discover new, implicit relationships. Various rule systems (production rules, Prolog-like systems, etc) are very different from one another, and it is not possible to define one rule language to encompass them all. A general example may help. The Rule Interchange Format (RIF) Working Group is currently working on a precise definition of this “core” Rule language, on ways to extend this rule language to various variants (production rules, logic programming, etc), to exchange expression of rules among systems, and to define the precise relationships of these rules with OWL ontologies and their usage with RDF triples.

Evri Acquires Radar Networks In Semantic Search Consolidation After shopping itself around to all the major search engines, Radar Networks finally found a buyer in another semantic search startup. Today, Evri is announcing that it will be acquiring Radar Networks, along with its core technical team and its main product, Twine. Rumors surfaced yesterday on ReadWriteWeb that Evri was being acquired, but that is not the case. Evri is the acquirer. I spoke with both CEOs this morning. Evri, on the other hand, has been focusing more on filtering the realtime Web and then creating a semantic index of those pages based on matching similar content. “We had to find a home,” explains Radar CEO Nova Spivack. Semantic search is still in its infancy.