mapping

Google Street View car fleet gets ready to conquer (and map) the world

Source: http://www.engadget.com/2012/10/15/visualized-google-street-view-car-fleet-gets-ready-to-conquer-the-world/

Visualized Google Street View car fleet gets ready to conquer and map the world

Ever wonder how Google can make such grandiose claims for the sheer amount of Street View imagery it collects? Here’s how. Google’s Masrur Odinaev has posted a snapshot of a central mapping car parking lot that shows dozens of the camera-equipped Subaru Imprezas amassed ahead of future runs. While it already represents more Street View cars in one place than anyone outside of Google would normally ever see, what’s most impressive is remembering that this addresses just a portion of the entire vehicle mix — aside from the local cars you don’t see in the photo, there are extra units worldwide providing street-level coverage alongside tricycles and underwater expeditions. Odinaev’s look reminds us just how much effort is needed to make Street View a common feature — and that there are are legions of Google staffers whose low-profile work goes a long way towards making our navigation easier.

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Visualized: Google Street View car fleet gets ready to conquer (and map) the world originally appeared on Engadget on Mon, 15 Oct 2012 02:03:00 EDT. Please see our terms for use of feeds.

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Monday, October 15th, 2012 Uncategorized No Comments

Here Are The Apps Tim Cook Suggests You Use Instead Of Apple Maps (AAPL)

Source: http://www.businessinsider.com/tim-cook-suggests-alternative-map-apps-2012-9

google maps iphone widget

Tim Cook released a letter to Apple customers today, apologizing for failing to deliver on its new mapping application for iPhones and iPads.

Cook promises the app will get better, but suggests users try a few alternative apps in the meantime.

Here are the apps he suggests you use instead of Apple Maps:

For the mobile Web version of Google Maps and Nokia Maps, you’ll need to create a shortcut to the sites on your iPhone’s home screen by bookmarking them. We show you how to do that here for Google and here for Nokia.

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Friday, September 28th, 2012 news No Comments

Latest Amazon Buy Is Another Signal They’re Building A Phone

Source: http://www.businessinsider.com/latest-amazon-buy-is-another-signal-theyre-building-a-phone-2012-7

Amazon made news this week with their acquisition of 3D mapping startup UpNext.

In a news note on BI Intelligence, we analyze what Amazon is up to with this acquisition. We:

For full access to the news note, sign up for a free trial subscription today.

Note: BI Intelligence is Business Insider’s new research and analysis service focused on the mobile industry. Trial subscribers gain full access to a library of research, data, and charts, as well as all news notes. 

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Thursday, July 5th, 2012 news No Comments

How Google Crunches All That Data

Source: http://gizmodo.com/5495097/how-google-crunches-all-that-data

If data centers are the brains of an information company, then Google is one of the brainiest there is. Though always evolving, it is, fundamentally, in the business of knowing everything. Here are some of the ways it stays sharp.

For tackling massive amounts of data, the main weapon in Google’s arsenal is MapReduce, a system developed by the company itself. Whereas other frameworks require a thoroughly tagged and rigorously organized database, MapReduce breaks the process down into simple steps, allowing it to deal with any type of data, which it distributes across a legion of machines.

Looking at MapReduce in 2008, Wired imagined the task of determining word frequency in Google Books. As its name would suggest, the MapReduce magic comes from two main steps: mapping and reducing.

The first of these, the mapping, is where MapReduce is unique. A master computer evaluates the request and then divvies it up into smaller, more manageable “sub-problems,” which are assigned to other computers. These sub-problems, in turn, may be divided up even further, depending on the complexity of the data set. In our example, the entirety of Google Books would be split, say, by author (but more likely by the order in which they were scanned, or something like that) and distributed to the worker computers.

Then the data is saved. To maximize efficiency, it remains on the worker computers’ local hard drives, as opposed to being sent, the whole petabyte-scale mess of it, back to some central location. Then comes the second central step: reduction. Other worker machines are assigned specifically to the task of grabbing the data from the computers that crunched it and paring it down to a format suitable for solving the problem at hand. In the Google Books example, this second set of machines would reduce and compile the processed data into lists of individual words and the frequency with which they appeared across Google’s digital library.

The finished product of the MapReduce system is, as Wired says, a “data set about your data,” one that has been crafted specifically to answer the initial question. In this case, the new data set would let you query any word and see how often it appeared in Google Books.

MapReduce is one way in which Google manipulates its massive amounts of data, sorting and resorting it into different sets that reveal new meanings and have unique uses. But another Herculean task Google faces is dealing with data that’s not already on its machines. It’s one of the most daunting data sets of all: the internet.

Last month, Wired got a rare look at the “algorithm that rules the web,” and the gist of it is that there is no single, set algorithm. Rather, Google rules the internet by constantly refining its search technologies, charting new territories like social media and refining the ones in which users tread most often with personalized searches.

But of course it’s not just about matching the terms people search for to the web sites that contain them. Amit Singhal, a Google Search guru, explains, “you are not matching words; you are actually trying to match meaning.”

Words are a finite data set. And you don’t need an entire data center to store them—a dictionary does just fine. But meaning is perhaps the most profound data set humanity has ever produced, and it’s one we’re charged with managing every day. Our own mental MapReduce probes for intent and scans for context, informing how we respond to the world around us.

In a sense, Google’s memory may be better than any one individual’s, and complex frameworks like MapReduce ensure that it will only continue to outpace us in that respect. But in terms of the capacity to process meaning, in all of its nuance, any one person could outperform all the machines in the Googleplex. For now, anyway. [Wired, Wikipedia, and Wired]

Image credit CNET

Memory [Forever] is our week-long consideration of what it really means when our memories, encoded in bits, flow in a million directions, and might truly live forever.

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Wednesday, March 17th, 2010 news No Comments

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