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Puzzling over big data.  Can a presentation about analytics, big data and algorithms be funny.. and perfectly understandable to those of us who know nothing about higher math? Absolutely! Inspired by the way people put puzzles together, Jeff Jonas, chief scientists, entity analytics at IBM, challenges teenagers to a puzzle project. Unbeknownst to them, some pieces are duplicates while others are missing (just like the data you need in real life). Jonas shows how he studies human approaches to problem solving to devise better ways to mine big data. He also explains why you get better information when you co-mingle social network data with other data, rather than analyze it separately. See also Gigaom

How Google Maps is changing the face of data — Cloud Computing News

Geospatial adds an incredible amount of context. It allows for complex tasks such as tracking of people as they go about their business to help determine who’s connected to whom, or predicting where someone might go next and what’s the best route to get there. If we’re talking about a spreading disease, Jonas explained, geospatial data helps us determine its vector and velocity.

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 How Space-Time Makes Sense of Big Data
As the number of people connected to the web continues to grow, so  too does the vast amount of information about those individuals. Putting  this massive data store to use improves predictions and improves the  overall quality of data but can also increase computational speeds. What  happens when you add mobile devices and locations to the mix? That  geospatial data attribute feeds big data analytics like a super-food and  creates “space-time travel” data.
At GigaOM’s Structure Big Data conference on Wednesday, Jeff Jonas, a  IBM Distinguished Engineer explained the concept with some startling  examples. With roughly 600 billion data transactions from cellular  phones on a daily basis, adding space and time to traditional data  objects can help predict where someone will be on a given day and time  with up to 87 percent accuracy, for example. Adding space-time works  because, oddly enough, of physics, says Jonas.

Expert counting is a traditional way to observe data, but  the same thing cannot be in two places at once. Including space and  time observations removes ambiguity. For example, the last 10 years of  address history, taken in context, can tell if a person is the same or  not, when digging through billions of rows of data.

Why would companies want to add the physics of space-time to their  data efforts? More context is needed, Jonas says, because the amount of  captured data is rising faster than the creation of algorithms to make  sense of the data. That’s creating a gap in the understanding of vast  information stores and adding space-time adds much more context.  Observations are where the sense-making begins but without context, it’s  like trying to build a jigsaw puzzle without the picture of the final  product.
Source: Mashable

 How Space-Time Makes Sense of Big Data

As the number of people connected to the web continues to grow, so too does the vast amount of information about those individuals. Putting this massive data store to use improves predictions and improves the overall quality of data but can also increase computational speeds. What happens when you add mobile devices and locations to the mix? That geospatial data attribute feeds big data analytics like a super-food and creates “space-time travel” data.

At GigaOM’s Structure Big Data conference on Wednesday, Jeff Jonas, a IBM Distinguished Engineer explained the concept with some startling examples. With roughly 600 billion data transactions from cellular phones on a daily basis, adding space and time to traditional data objects can help predict where someone will be on a given day and time with up to 87 percent accuracy, for example. Adding space-time works because, oddly enough, of physics, says Jonas.

Expert counting is a traditional way to observe data, but the same thing cannot be in two places at once. Including space and time observations removes ambiguity. For example, the last 10 years of address history, taken in context, can tell if a person is the same or not, when digging through billions of rows of data.

Why would companies want to add the physics of space-time to their data efforts? More context is needed, Jonas says, because the amount of captured data is rising faster than the creation of algorithms to make sense of the data. That’s creating a gap in the understanding of vast information stores and adding space-time adds much more context. Observations are where the sense-making begins but without context, it’s like trying to build a jigsaw puzzle without the picture of the final product.

Source: Mashable