hard drives

You Can Squeeze 2.2 Petabytes of Data Into One Gram of DNA

Source: http://gizmodo.com/5978581/you-can-squeeze-22-petabytes-of-data-into-one-gram-of-dna

You Can Squeeze 2.2 Petabytes of Data Into One Gram of DNA Scientists from the European Bioinformatics Institute are squeezing unparalleled amounts of data in to synthetic DNA, and now they’ve achieved something absolutely amazing: they can store 2.2 petabytes of information in a single gram of DNA, and recover it with 100 percent accuracy.

The researchers have encoded an MP3 of Martin Luther King’s 1963 “I have a dream” speech, along with all 154 of Shakespeare’s sonnets, into a string of DNA. Scaled up, that represents a storage density of 2.2 petabytes per gram. What’s amazing, though, is that they’ve managed to achieve that whilst also implementing error correction in the complex chains of molecules, allowing them to retrieve content with 100 per cent accuracy.

The technique uses the four bases of DNA—A, T, C and G—to achieve the high information density. It is, understandably, still incredibly expensive: creating synthetic DNA and then sequencing it to read off the data is getting far easier, but it’s still a time- and cash-consuming business. Keep hold of your hard drives for now, but DNA could represent a viable storage solution in the future. [Nature via New Scientist]

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Thursday, January 24th, 2013 news No Comments

Hard drive shipments recover from floods in Thailand, expected to reach record high

Source: http://www.engadget.com/2012/09/29/hard-drives-thailand-floods-recover-record/

Hard drive shipments recover from floods in Thailand, expected to reach record high

Last year’s floods in Thailand caused hard drive shortages after wreaking havoc on a number of electronics manufacturers, but new stats from IHS iSuppli indicate that the HDD market for PCs has fully recovered and is poised to hit an all time high. The firm expects 524 million units for internal use in PCs to ship this year, besting the previous record by 4.3 percent. What’s giving the recovery an added boost? According to the analytics group, the extra demand comes courtesy of Windows 8 and Ultrabooks. Unfortunately for deal hounds, the company noted in a report earlier this year that prices aren’t expected to dip below the pre-flood range until 2014. If IHS iSuppli projections hold true, total annual hard driv! e shipme nts could reach 575.1 million by 2016.

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Sunday, September 30th, 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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