Viewing the World through Wikipedia
VARINDIA- INDIA'S FRONTLINE IT MAGAZINE
SGI has partnered with Kalev H. Leetaru of the University of Illinois to create the historical mapping and exploration of the full text contents of the English-language edition of Wikipedia, in time and space. The results include visualizations of modern history captured in under a day utilizing in-memory data-mining techniques. Loading the entire English language edition of Wikipedia into SGI UV 2000, Leetaru was able to show how Wikipedia’s view of the world unfolded over the past two centuries. Location, year and the positive or negative sentiment have been tied to those references.
Franz Aman, Chief Marketing Officer & Head of Strategy, SGI said, “This analysis allows the world to take a step back from the individual articles and text to gain a forest view of the tremendous knowledge captured in Wikipedia, not just a page by page tree view. We can watch how one of the largest collections of human knowledge has evolved and see what we could never see before, such as global sentiment at a certain time and place, or where there might be blind spots in the knowledge coverage. We love to use Google Earth because we can zoom out and get the big picture view. With SGI UV 2, we can apply the same concept to Big Data to get the big picture on our Big Data.”
Leetaru said, “The one-way nature of connections in Wikipedia, the lack of links, and the uneven distribution of Infoboxes, all point to the limitations of metadata-based data mining of collections like Wikipedia. With SGI UV 2, the large shared memory available allowed me to ask questions of the entire dataset in near-real time. With a huge amount of cache-coherent shared memory at my fingertips, I could simply write a few lines of code and run it across the entire dataset, asking whatever questions came to mind. This isn’t possible with a scale-out computing approach. It’s very similar to using a word processor instead of using a typewriter – I can conduct my research in a completely different way, focusing on the outcomes, not the algorithms.”
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