The EU Provenance Project. Using VisTrails and Provenance for Teaching Scientific Visualization - Silva - 2010 - Computer Graphics Forum. Enhanced PDF available Options for accessing this content: Type your institution's name in the box below. If your institution is a Wiley customer, it will appear in the list of suggested institutions and you will be able to log in to access content. Some users may also log in directly via OpenAthens. Please note that there are currently a number of duplicate entries in the list of institutions.
Registered Users please login: Access your saved publications, articles and searchesManage your email alerts, orders and subscriptionsChange your contact information, including your password Please register to: Save publications, articles and searchesGet email alertsGet all the benefits mentioned below! Register now > Provenance Visualization and Usage. The task in this project is to incorporate provenance information in the visual data analysis workflow. This includes the acquisition and visualization of provenance data as well as the usage of the obtained information to improve navigation and data retrieval. The typical working process in visual data analysis projects leads to a large and steadily increasing number of datasets and visualizations. The first step towards a provenance-based project management is the capturing of the provenance information for every data and visualization object.
We want to integrate this functionality in Amira, based on the specification proposed by the Open Provenance Model. A second task gives attention to the visualization of the collected provenance data. Data visualization primer: What they are and why they're important. Acquiring data is no longer a problem. It's everywhere, and we're already quite adept at hoarding it in databases. The issue now is making sense of all those signals and finding stories in the stream. That's where visualizations come in. Whether you're dealing with a static graph or a real-time data wave, the act of seeing data unlocks much of its utility. Ben Fry, an author and speaker at next week's Making Data Work online conference, has been studying and furthering visualization for years. In the following Q&A, Fry examines the current state of visualization and makes the case for visualization to be treated as its own field. How has data visualization changed over the last few years?
Ben Fry: Ten years ago I had to explain that 1) big data is coming 2) visualization is a solution. What are the biggest problems with data visualization? Do visualizations fall into the programming domain, or is it closer to design? Ben Fry: I think it requires both. Ben Fry: That's a big question. Related:
How do you visualize too much data? We live in the data deluge era. You can hear it everywhere: massive databases, thousands of organizations taking decisions based on their data, millions of transactions executed every second. Fast and large. Massive and relentless. Do you think it’s hard to find examples of databases with a million items? No, it’s not. They are everywhere. But wait a moment … how do you visualize a million items? Visualization is being developed fast and I love the way this whole community is pushing forward to create more and more clever designs.
When is data too much? It’s not easy to define when data is too much. We can intuitively say that data is too much simply when it doesn’t fit the screen. But if the limit is the number of pixels then, what if we just increase the number of pixels according to the size of the data we want to visualize? But then it turns out you reach a new limit. So, when is data too much in visualization?
What are the (visualization) problems with too much data? Clutter. Why Is Data Visualization So Hot? Noah Iliinsky is the co-author of Designing Data Visualizations and technical editor of, and a contributor to, Beautiful Visualization, published By O’Reilly Media. He will lead a Designing Data Visualizations Workshop at O’Reilly’s Strata conference on Tuesday, Feb. 28. Data visualization is hot.
All of a sudden there are dozens of companies and products that want to help you visually analyze your data, build your own visualizations, and visually display interesting data sets of all kinds. So, why is visualization interesting? To answer these questions, we need to go all the way back to biology. That last factor, pattern matching, is the key when it comes to discussing the benefits of presenting information visually. Let’s look at the classic instructive example, Anscombe’s Quartet, devised by statistician Francis Anscombe to demonstrate this very issue.
So we know that visualization is effective at conveying knowledge. So there it is. We’re wired for visualization. The Importance of Data Visualization. No votes for this yet Dashboards are a powerful tool when the right metrics are displayed. But it is equally important to properly visualize the metrics. Rebeckah Blewett, product manager for Dundas Data Visualization Inc., explains the importance of data visualization in decision making: The practice of representing information visually is nothing new.
Data visualization, when done correctly, is an effective way to analyze large amounts of data to identify correlations, trends, outliers, patterns, and business conditions. Read the full article. Great Data Visualization Tells a Great Story. Think of all the popular data visualization pieces out there - the ones that you always hear in lectures, read about in blogs, and the ones that popped into your head as you were reading this sentence.
What do they all have in common? They probably all told a great story. Maybe the story was to convince us of something, compel us to action, enlighten us with new information, or force us to question our own preconceptions. Whatever it is, truly great data visualization reaches us at a very human level and that is why we remember them. Let's face it. Data can be boring if you don't know what you're looking for or don't know that there's something to look for in the first place. Show the Story in the Data I first got my hands dirty with data visualization when I was at The New York Times for a summer. Take a look at any New York Times graphic. Sure you could make a line chart or histogram with default settings straight out of Excel, but where's the story? Humanize the Data Compel to Action.
Data Visualization: 20+ Useful Tools and Resources. There are plenty of cool technologies available to collect and examine data. Both web and desktop applications have provided some really great interfaces to fall in love with data mining, and with the rise in popularity we have noticed an increased number of infographics created over the past few years. Today we’ll be looking into some really cool and popular online resources for data visualization.
You can see all kinds of data like human population, world condition and even human emotion presented via the visualization. While some of the visualization might be experimental, all of them have one similarity: they help you understand the data better, and this is exactly what visualization is for. If you’ve designed your own infographic or visualization tool we’d love to check it out. In addition to the examples highlighted please offer your ideas or thoughts in the discussion area below! Recommended Reading: More Infographic related posts. Better World Flux Visual.ly We Feel Fine RSS Voyage. Provenance: From e-Science to the Web Of Data. Data Visualization: Modern Approaches. Stanford Visualization Group.