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:
Backbone.js Gallery · d3/d3 Wiki Wiki ▸ Gallery Welcome to the D3 gallery! More examples are available for forking on Observable; see D3’s profile and the visualization collection. Please share your work on Observable, or tweet us a link! Visual Index Basic Charts Techniques, Interaction & Animation Maps Statistics Examples Collections The New York Times visualizations Jerome Cukier Jason Davies Jim Vallandingham Institute for Health Metrics and Evaluation Peter Cook Charts and Chart Components Bar Chart Histogram Pareto Chart Line and Area Chart Pie Chart Scatterplot and Bubble chart Parallel Coordinates, Parallel sets and Sankey Sunburst and Partition layout Force Layout Tree Misc Trees and Graphs Chord Layout (Circular Network) Maps Misc Charts Miscellaneous visualizations Charts using the reusable API Useful snippets Tools Interoperability Online Editors Products Store Apps
SPARQL 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.
Raphaël—JavaScript Library jsoup Java HTML Parser, with best of DOM, CSS, and jquery 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
jQuery Eclipse Collections - Features you want with the collections you need. History of Eclipse Collections The origin of Eclipse Collections was started off as a collections framework named Caramel at Goldman Sachs in 2004. Since then the framework has evolved, and in 2012, it was open sourced to GitHub as a project called GS Collections. GS Collections has been presented at number of conferences including JVM Summit in 2012 and JavaOne in 2014. Over the years, around 40 or so developers from the same company have contributed to the collections framework. To maximize the best nature of open source project, GS Collections has been migrated to the Eclipse Foundation, re-branded as Eclipse Collections in 2015. Reference Guide A comprehensive reference guide of the library is available on GitHub: Reference Guide.
Jena (framework) Jena supports serialisation of RDF graphs to: Introducing PourOver and Tamper Client-side superfast collection management from the NYT This project was documented and released as part of the first OpenNews Code Convening. Today we’re open-sourcing two internal projects from The Times: PourOver.js, a library for fast filtering, sorting, updating and viewing large (100k+ item) categorical datasets in the browser, andTamper, a companion protocol for compressing categorical data on the server and decompressing in your browser. We invite you to explore the docs and examples for both projects; we also have some next examples over on the Times’ Open blog. Collections are important to developers, especially news developers. Dissatisfied with this state of the art, we made PourOver as an attempt to standardize an efficient and extensible model of client-side collection management and weaken reliance on server-side collection operations. The genesis of PourOver is found in the 2012 London Olympics. Enter Tamper The classic arc goes something like this: Buffered PourOvers
Mockito in six easy examples Mockito is a fantastic mock library for Java. I’m fascinated by how easy it is to use, compared to other things out there both in the Java and .NET world. Here is everything you need to know to get started in six really easy examples. First of all, get mockito from To create a stub (or a mock), use mock(class). import static org.mockito.Mockito.*; import static org.junit.Assert.*; import java.util.Iterator; import org.junit.Test; .... This example creates a mock iterator and makes it return “Hello” the first time method next() is called. Stubs can also return different values depending on arguments passed into the method. @Test public void with_arguments(){ Comparable c=mock(Comparable.class); when(c.compareTo("Test")).thenReturn(1); assertEquals(1,c.compareTo("Test")); } This creates a stub Comparable object and returns 1 if it is compared to a particular String value (“Test” in this case). This stub comparable returns -1 regardless of the actual method argument.