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Knowledge management. Process of creating, sharing, using and managing the knowledge and information of an organization Knowledge management (KM) is the process of creating, sharing, using and managing the knowledge and information of an organization.[1] It refers to a multidisciplinary approach to achieve organisational objectives by making the best use of knowledge.[2] An established discipline since 1991[citation needed], KM includes courses taught in the fields of business administration, information systems, management, library, and information sciences.[3][4] Other fields may contribute to KM research, including information and media, computer science, public health and public policy.[5] Several universities offer dedicated master's degrees in knowledge management.

History[edit] In 1999, the term personal knowledge management was introduced; it refers to the management of knowledge at the individual level.[11] Research[edit] Dimensions[edit] The Knowledge Spiral as described by Nonaka & Takeuchi. Tutorial 4: Introducing RDFS & OWL. Next: Querying Semantic Data Having introduced the advantages of modeling vocabulary and semantics in data models, let's introduce the actual technology used to attribute RDF data models with semantics. RDF data can be encoded with semantic metadata using two syntaxes: RDFS and OWL. After this tutorial, you should be able to: Understand how RDF data models are semantically encoded using RDFS and OWLUnderstand that OWL ontologies are RDF documentsUnderstand OWL classes, subclasses and individualsUnderstand OWL propertiesBuild your own basic ontology, step by stepEstimated time: 5 minutes You should have already understood the following tutorial (and pre-requisites) before you begin: Tutorial 3: Semantic Modeling In the last lesson, we compared some of the more popular traditional forms of modeling data with the semantic model, and then introduced a situation where data sharing was enhanced and made significantly easier by using a semantic web approach. 4.1 A Starting Example 01. 08. 09. 10. 11.

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. 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. We start from the ground up by outlining the graph database - the data storage model used by the semantic web. 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. 03.

Spreadsheets Are Graphs Too! 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. 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. CLUSTER ANALYSIS METHOD OF RHETORICAL CRITICISM – The Visual Communication Guy: Designing, Writing, and Communication Tips for the Soul. Cluster analysis, as a method of rhetorical criticism, is a process critics can use to evaluate the perspectives and worldviews of a person communicating something.

The term “cluster” is used because we can learn a lot about what someone is thinking (even subconsciously) by “clustering” key words and symbols they use in a communication with other words and symbols that are used in proximity or relation to the key words. The method is attributed to late rhetorician Kenneth Burke, who sought to understand how people think and what motivated them to action. Effective cluster analyses follow three steps: Identifying key termsCharting clusters around those key termsExplaining the artifact Review the graphic here for guidance in doing a cluster analysis or read the larger text below. Study your artifact(s) for key terms. What is you are discovering in this process is relationships. Making Sense of the Data, Part 1. This post is an adaptation of my presentation at the 2011 HOW Interactive Design Conference in San Francisco.

I included a variety of slide transitions in my talk, so in some cases below, I’ve consolidated slide groups into animated GIFs. Keep your eye out for those so you don’t miss any details. Do you remember your last yearly physical? Mine was sometime before the Obama administration, so I guess that’s what I mean by “yearly.” After the exam, my doctor and I had the following exchange: Me: Should I go ahead and schedule next year’s appointment? Stuff. Stuff? A few days later, I got the results of my blood work in the mail. And that, right there, is the connection I want to make with measurement. Here’s an example for you to see; a page from an actual KPI that one of my clients paid for a few years ago: Ugh.

I thought it was so important that you see this that I was willing to contaminate this beautiful page by including it. I like smart people. “Where’s my KPI report? “Oh, you mean the…” Infographics Are the Communication Paradigm of the Future. These days, infographics are becoming ubiquitous. Some infographics have become famous and are used by millions over several years (like the USDA MyPlate poster) while others are just normal print journalism passing by as daily news (you can peruse these examples for some recent newsy infographics). There is a long history of infographics and some could argue that they have been around for thousands of years. As we understand them today, however, the modern infographic took early shape in the 1800s. Probably the most famous infographic of all time, in fact, is Charles Minard’s graphic on Napoleon’s March of Russia. Designed in 1861, this complex infographic plots multiple variables of data: the size of Napoleon’s army; it’s location on a two-dimensional space; the direction the army moved over time; and the temperature over several dates in varying locations as the army retreated from Moscow.

Some have argued that Minard’s graphic may be the best statistical graphic ever created. The hidden legacy of Bertin and “The Semiology of Graphics” The Semiology of Graphics (SOG) is a kind mythical book. Everybody knows the title, but few actually know its content. I already suspected it since a long time but this was confirmed by Jean-Daniel Fekete in my interview in my last post. But this is not because people are lazy, it is more, I suspect, that it has always been hard to find a copy, especially in English. And I am also guilty. Apart from reading the short abstract contained in Readings in Information Visualization, I never had the book in my hands until few weeks ago (a French copy of 1967!

Thanks to the wonderful library at the Univ. of Konstanz). As I said, part of the problem resides in its limited availability but, as some of you might already know, Amazon is promising since some weeks to have the new English edition out around December. So, if Bertin is such a wealth of hidden information what can we do now? Retinal Properties Retinal properties as defined by J. Taxonomy of Networks Taxonomy of Networks Reorderable Matrix. TO UNDERSTAND IS TO PERCEIVE PATTERNS. Data Visualization for Human Perception. Gallery of Data Visualization - Bright Ideas. Remixing Rosling. The Church of London commissioned me to remix two of the famous Gapminder bubble graphics to illustrate an interview with Hans Rosling for Google's "Think Quarterly" Magazine.

Fertility and life expectancy This graphics is a condensed, and static version of one of Gapminder's famous animated "worm" graphics. Scaling the diameters of the circles according to the years, and then connecting them, induces a sense of motion over time, even in this static image. It is fascinating to see how Vietnam is today on the same level as the US in 1980 with respect to the fertility rate and life expectancy. Also note how the dip in Botswana's curve reflects the drastic effects of AIDS in this country in the 1980s. Child mortality This chart dramatically shows how Bangladesh manages to reduce its child mortality with a rate faster than Sweden ever did. Full magazine See the graphics as used in the magazine, and browse the whole issue below, or download it as a .pdf file (24MB).

Moritz.stefaner.eu - / Beliv06 - 23 May 2006 - Home Page. News [Jul'07] BELIV'08, a new edition of the workshop, will be hosted at ACM CHI 2008 in Florence, Italy on 5 Apr 2008. Deadline for submission is 30 Oct 2007. For more info see the BELIV'08 workshop website. [May'07] Workshop report published in the ACM <inteactions> magazine: [Dec'06] A draft of the BELIV'06 workshop report is available online. [Nov'06] Workshop proceedings now available in the ACM Digital Library (paper titles below now link to their location in the ACM DL): Workshop description Controlled experiments remain the workhorse of evaluation but there is a growing sense that information visualization systems need new methods of evaluation, from longitudinal field studies, insight based evaluation and other metrics adapted to the perceptual aspects of visualization as well as the exploratory nature of discovery.

Topics of Interest Topics include, but are not limited to: Format of the event (1 full day) Ideally, about 20 participants will attend the workshop. Resources. Enrico Bertini. Enrico.bertini.me/material/biovis2011-hitsee.pdf. Enrico.bertini.me/material/infovis2011-qm-taxonomy.pdf. Enrico.bertini.me/material/eurovis2012-anim-sm.pdf. Enrico.bertini.me/material/tvcg2011-seven-scenarios.pdf. Enrico.bertini.me/material/tvcg2011-seven-scenarios.pdf. Enrico.bertini.me/material/infovis2011-cloudlines.pdf. Fell in Love with Data — Data Stories | A podcast on data visualization with Enrico Bertini and Moritz Stefaner. This is onformative a studio for generative design. Pretty pictures: Can images stop data overload? 16 April 2012Last updated at 19:01 ET By Fiona Graham Technology of business reporter, BBC News Brain scan: Research suggests that one way to avoid being overloaded by data is by presenting it visually rather than text or numbers Sitting at your desk in the middle of the day, yet another email notification pops up in the corner of the screen, covering the figures you're trying to digest in the complicated spreadsheet in front of you.

Your laptop is open on the desk next to you with another set of figures you need - meanwhile you're frantically tabbing through different documents on the main screen. You have a meeting in 20 minutes and you suddenly feel as if you're swimming in a sea of impenetrable data, and you're starting to sink. Welcome to the 21st Century workplace, and "data overload". Under siege You're not alone. Dr Lynda Shaw is a neuroscience and psychology lecturer at Brunel University in the west of London. "When we feel overwhelmed we start to delay making decisions. " “Start Quote. Introducing the Knowledge Graph. The Work of Edward Tufte and Graphics Press.

Graphics Press LLC P.O. Box 430 Cheshire, CT 06410 800 822-2454 Edward Tufte is a statistician and artist, and Professor Emeritus of Political Science, Statistics, and Computer Science at Yale University. He wrote, designed, and self-published 4 classic books on data visualization. The New York Times described ET as the "Leonardo da Vinci of data," and Bloomberg as the "Galileo of graphics. " Topics covered in this one-day course include: A new, widely-adopted method for presentations: meetings are smarter, more effective, 20% shorter. Fundamental design strategies for all information displays: sentences, tables, diagrams, maps, charts, images, video, data visualizations, and randomized displays for making graphical statistical inferences. New ideas on spectatorship, consuming reports.

Standards of comparison for workaday and for cutting edge visualizations. The future of information displays: 4K, 6K, 8K video maps moving in time. Edward Tufte teaches the entire course. Perception and visualization - 02_perception-visualization_1up.pdf. What is Data Visualization? Designing Visualizations for Time-Based Data | Max Kiesler. Most interaction designers understand the concept of timelines and other time-based data. Blogs, calendars, and to-do lists are all examples of time-based data. However, if you are trying to fit 400 data points into a 1024 x 726 screen you’ll quickly see how challenging time-base data can be. Currently, many interaction designers are turning to visualizations to overcome many of the issues associated with this form of data representation. Below you’ll find a list of some of the best time-based visualizations on the web. The Sputnik Legacy The Soviet launch of Sputnik in 1957 kicked of the space race. British History Timeline Explore all of British history, from the Neolithic to the present day, with this easy-to-use interactive timeline.

Timepiece – Visualize Film Ideas Timepiece is an experimental data visualization that help you explore Filmforay’s site content including, all of their film ideas, new members, comments and votes on each film idea. CircaVie – Create and Share Timelines. A Tour Through the Visualization Zoo. Moments of innovation – data visualization. Data Visualization: Modern Approaches. Expose Data. A Periodic Table of Visualization Methods. Infographics & Data Visualisation. Graphical visualization of text similarities in essays in a book | munterbund.de. Relation Browser / Visualisations showing relations | valderama.net. Emerging Information Architectures « Srinivas Reddy’s Weblog.