What is Pattern Analysis? PATN is a software package that performs Pattern Analysis. PATN aims to try and display patterns in complex data. Complex in PATN's terms, means that you have at least 6 objects that you want to know something about and a suite of more than 4 variables that describe those objects. Data must be in the form of a spreadsheet of rows (the objects in PATN) and the columns (variables), as in Microsoft Excel™. There are usually around 7 components to a 'realistic' (read as adequate, comprehensive, fair, reasonable or intelligent) pattern analysis in PATN- Import the data Check the data using PATN's Visible Statistics functions. PATN is setup to make it easy for you to follow this process.
Introduction to Complex Systems by David Kirshbaum I. Introduction: Complex Systems Theory : Basic Definition II. III. I. A Complex System is any system which involves a number of elements, arranged in structure(s) which can exist on many scales. Previously, when studying a subject, researchers tended to use a reductionist approach which attempted to summarize the dynamics, processes, and change that occurred in terms of lowest common denominators and the simplest, yet most widely provable and applicable elegant explanations. But since the advent of powerful computers which can handle huge amounts of data, researchers can now study the complexity of factors involved in a subject and see what insights that complexity yields without simplification or reduction. Scientists are finding that complexity itself is often characterized by a number of important characteristics: (II.1) Self-Organization(II.2) Non-Linearity(II.3) Order/Chaos Dynamic(II.4) Emergent Properties. (II.1) Self-Organization Examples (II.2) Non-Linearity
NodeXL Graph Gallery: Graph Details The graph represents a network of 1,613 Twitter users whose recent tweets contained "#agchat", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Tuesday, 13 December 2016 at 16:56 UTC. The tweets in the network were tweeted over the 9-day, 18-hour, 37-minute period from Saturday, 03 December 2016 at 22:04 UTC to Tuesday, 13 December 2016 at 16:42 UTC. The graph is directed. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm. The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm. Top Word Pairs in Tweet in Entire Graph:[355] agblog,via[199] agchat,conference[151] rt,agchat[121] vancecrowe,agchat[102] kansas,city[99] rt,agchatfound[86] rt,farmfutures[71] agchat,agchat[67] farm,futures[67] via,beef
untitled Thanks for your interest in Copycat! Copycat is written in Common Lisp. The system is unfortunately rather outdated: it will not run as is without some updates for modern versions of Common Lisp, and some platform-specific modifications to the graphics files. I am hoping that it will be rewritten in a more platform independent way sometime soon. I am still making the source files available. To get the source files, go to : and at your home machine, untar the file to get the source files. If your system can't deal with tar files, then go to and individually get each source file. To get Jim Marshall's Metacat project, go to: Scott Bolland of the University of Queensland wrote a Java version of Copycat and a tutorial; the web site is
New DataBasic Tool Lets You “Connect the Dots” in Data Catherine D'Ignazio and I have launched a new DataBasic tool and activity, Connect the Dots, aimed at helping students and educators see how their data is connected with a visual network diagram. By showing the relationships between things, networks are useful for finding answers that aren’t readily apparent through spreadsheet data alone. To that end, we’ve built Connect the Dots to help teach how analyzing the connections between the “dots” in data is a fundamentally different approach to understanding it. The new tool gives users a network diagram to reveal links as well as a high level report about what the network looks like. Using network analysis helped Google revolutionize search technology and was used by journalists who investigated the connections between people and banks during the Panama Papers Leak. Learn more about Connect the Dots and all the DataBasic tools here. Have you used DataBasic tools in your classroom, organization, or personal projects?
Spreadsheets Are Graphs Too! - Neo4j Graph Database By Felienne Hermans, Assistant Professor, Delft University of Technology | August 26, 2015 Editor’s Note: Last May at GraphConnect Europe, Felienne Hermans – Assistant Professor at Delft University of Technology – gave this engaging talk on why you shouldn’t overlook the power of the humble spreadsheet. Listen to or read her presentation below. Register for GraphConnect San Francisco to hear more speakers like Felienne present on the emerging world of graph database technologies. People often ask me, ‘How is it possible that you research spreadsheets? Did you actually write a dissertation on spreadsheets?’ The answer is, Yes, I did. Ninety-five percent of all U.S. companies still use spreadsheets for financial reporting, so spreadsheets run the financial domain. Analysts decide the strategy of their company based on spreadsheets. Either way, analysts make decisions that steer the company based on the data in their spreadsheets. Spreadsheets often exist under the radar. Spreadsheets Are Code
Tutorial 1: Introducing Graph Data Next: Introducing RDF The semantic web can seem unfamiliar and daunting territory at first. If you're eager to understand what the semantic web is and how it works, you must first understand how it stores data. After this tutorial, you should be able to: Describe in basic terms what the semantic web is.Experience the paradigm-shift of storing information as a graph database, rather than a hierarchical or relational database.Understand that the semantic web of data is defined using Resource Description Framework (RDF).Understand the basic principles of RDF statements and how they can define data graphs. Estimated time: 5 minutes If you come from a traditional IT background and are used to the idea of storing data either in a hierarchy (for example XML) or in a relational database (for example MySQL, MS SQL), you may not yet have come across Resource Description Framework, or RDF. Although it might not be familiar to you, it is the type of database that builds the semantic web, globally. 03.
SKOS Simple Knowledge Organization System - home page SKOS is an area of work developing specifications and standards to support the use of knowledge organization systems (KOS) such as thesauri, classification schemes, subject heading lists and taxonomies within the framework of the Semantic Web ... [read more] Alignment between SKOS and new ISO 25964 thesaurus standard (2012-12-13) ISO 25964-1, published in 2011, replaced the previous thesaurus standards ISO 2788 and ISO 5964 (both now withdrawn). From Chaos, Order: SKOS Recommendation Helps Organize Knowledge (2009-08-18) Today W3C announces a new standard that builds a bridge between the world of knowledge organization systems - including thesauri, classifications, subject headings, taxonomies, and folksonomies - and the linked data community, bringing benefits to both. Call for Review: SKOS Reference Proposed Recommendation (2009-06-15) The Semantic Web Deployment Working Group has published the Proposed Recommendation of SKOS Simple Knowledge Organization System Reference.
What is an ontology and why we need it Figure 8. Hierarchy of wine regions. The "A" icons next to class names indicate that the classes are abstract and cannot have any direct instances. The same class hierarchy would be incorrect if we omitted the word “region” from the class names. We cannot say that the class Alsace is a subclass of the class France: Alsace is not a kind of France. However, Alsace region is a kind of a French region. Only classes can be arranged in a hierarchy—knowledge-representation systems do not have a notion of sub-instance. As a final note on defining a class hierarchy, the following set of rules is always helpful in deciding when an ontology definition is complete: The ontology should not contain all the possible information about the domain: you do not need to specialize (or generalize) more than you need for your application (at most one extra level each way). For our wine and food example, we do not need to know what paper is used for the labels or how to cook shrimp dishes. Figure 9.
MooWheel: a javascript connections visualization library View the project on Google Code 06.29.2008 version 0.2 now available! get it. What's new? The purpose of this script is to provide a unique and elegant way to visualize data using Javascript and the <canvas> object. This script requires three libraries to support it. MooWheel only requires 2 arguments to create a basic wheel graph. new MooWheel(dataArray, canvasElement); The canvas element can be passed as either an element reference or an id string. Note: In this context, the first argument (the data parameter), doesn't matter. Each item that has an "imageUrl" will have that image preloaded, and then added to the wheel when it is drawnIn terms of options, there are a number of options available that allow you to change the way the graph is generated and displayed: Using MooWheel is very simple and extremely easy. Next, you create an array of items for the connections: Then you add a container for the canvas tag to the body of your document:
OntoWiki — Agile Knowledge Engineering and Semantic Web CubeViz -- Exploration and Visualization of Statistical Linked Data Facilitating the Exploration and Visualization of Linked Data Supporting the Linked Data Life Cycle Using an Integrated Tool Stack Increasing the Financial Transparency of European Commission Project Funding Managing Multimodal and Multilingual Semantic Content Improving the Performance of Semantic Web Applications with SPARQL Query Caching