Bitcoin collecting has caused a wave of supercomputers techies hoping to make them a small fortune – even if it costs load of cash to power.
Specially developed Bitcoin ‘mining’ computers are either homemade or can be purchased from one of the growing number of online stores dedicated to cashing in on the supply side of the cult currency trend.
The 21 million Bitcoins hidden across an internet-based network, are expected to all be found by 2040. To unearth them computers have to solve the complex processor-intensive equations which hold them.
What is a Bitcoin?
It’s a piece of data locked in an internet-based network by a complex equation computers can break.
Once released it can be traded and used like money online and can be purchased with real cash.
Many websites are now taking Bitcoins as a form of currency. As well as digital currency, Bitcoin miners enjoy the competitive nature of unlocking the coins.
Famous fans include the Winklevoss twins who own around 1 percent of Bitcoins – currently worth around $11million
It has been dismissed by some as a Ponzi Scheme and touted by others as the future of money. It is not centrally controlled and it’s unique and complex set up means the market cannot be altered or hacked, according to the developers
There are 21 million coins predicted to last until 2140 and their finite nature means they perform more like a commodity, such as gold. The coins first emerged in 2008 and launched as a network in 2009.
They were introduced by an obscure hacker whose identity is a mystery but is known as Satoshi Nakamoto, which is thought to be a pseudonym. Users choose a virtual wallet from one of the various providers which enables them to receive, give and trade coins from other users. Bitcoins can be bought from specialist currency exchanges and online marketplaces such as eBay. Bitcoins mining machines can unlock many of the potentially lucrative coins – essentially computer codes – which are then used as currency online making their collectors potentially very rich.
The Bitcoin network, set up by a mysterious programmer under the pseudonym Satoshi Nakamoto in 2009, is also designed to get more complex the more miners there are looking for Bitcoins – opening the potential for ever-developing mining machines.
Much of the big data tools have been developed at the Walmart Labs, which was created after Walmart took over Kosmix in 2011. The products that were developed at Walmart Labs are ‘Social Genome’, ‘ShoppyCat’ and Get on the Shelf.
The Social Genome product allows Walmart to reach customers, or friends of customers, who have mentioned something online to inform them about that exact product and include a discount.
Public data is combined from the web along with social data and proprietary data such as customer purchasing data and contact information. The result is , constantly changing, up-to-date knowledge base with hundreds of millions of entities and relationships. this provides Walmart with a better understanding of the what their customers are saying online. An example mentioned by Walmart Labs shows a woman tweeting regularly about movies. When she tweets “I love Salt”, Walmart is able to understand that she is talking about the movie Salt and not the condiment.
The Shoppycat product developed by Walmart is able to recommend suitable products to Facebook users based on the hobbies and interests of their friends.
Get on the Shelf a crowd-sourcing solution that gave anyone the chance to promote his or her product in front of a large online audience. The best products would be sold at Walmart with the potential to suddenly reach millions of customers.
A 2011 McKinsey & Co. survey pointed out that many organizations don’t have the skilled personnel needed to mine big data for insights and the structures and incentives required to use big data to make informed decisions and act on them.
Big data is a mixture of distributed data architectures and tools like Hadoop, NoSQL, Hive and R. Data scientists serve as the gatekeepers and mediators between these systems and the people who run the business – the domain experts.
Three main roles served by the data scientist: data architecture, machine learning, and analytics. While these roles are important, but not every company actually needs a highly specialized data team of the sort you’d find at Google or Facebook.
Most of the standard challenges that require big data, like recommendation engines and personalization systems, can be abstracted out. On a per domain basis, however, feature creation could be templatized. What if domain experts could directly encode their ideas and representations of their domains into the system, bypassing the data scientists as middleman and translator?
After having been accustomed to terms like MegaByte, GigaByte, and TerraByte, we must now prepare ourselves for a whole new vocabulary, such as PetaByte, ExaByte, and ZettaByte which will be as common as the aforementioned.
Dr Riza Berkan CEO and Board Member of Hakia provides a list of Mechanisms generating Big Data
Dr Riza Berkan says Big Data can be a blessing and a curse.
He says that although there should be clear boundaries between data segments that belong to specific objectives, this very concept is misleading and can undermine potential opportunities. For example, scientists working on human genome data may improve their analysis if they could take the entire content (publications) on Medline (or Pubmed) and analyze it in conjunction with the human genome data. However, this requires natural language processing (semantic) technology combined with bioinformatics algorithms, which is an unusual coupling at best. Two different data segments in different formats, when combined, actually define a new “big data”. Now, add to that a 3rd data segment, such as the FBI’s DNA bank, or geneology.com and you’ll see the complications/opportunities can go on and on. This is where the mystery and the excitement resides with the concept of big data.
Dr Riza Berkan asks are we prepared for generating data at colossal volumes? and we should look at this question in two stages: (1) Platform and (2) Analytics “super” Software
Apache Hadoop’s open source software enables the distributed processing of large data sets across clusters of commodity servers, aka cloud computing. IBM’s Platform Symphony is another example of grid management suitable for a variety of distributed computing and big data analytics applications. Oracle, HP, SAP, and Software AG are very much in the game for this $10 billion industry. While these giants are offering variety of solutions for distributed computing platforms, there is still a huge void at the level of Analytics Super Software . Super Software’s main function would be to discover new knowledge which would otherwise be impossible to acquire via manual means says Dr Berkan.
Discovery requires the following functions:
Moreover, Dr Berkan says that” Super Software would be able to identify genetic patterns of a disease from human genome data, supported by clinical results reported in Medline, and further analyzed to unveil mutation possibilities using FBI’s DNA bank of millions of DNA information. One can extend the scope and meaning of top level objectives which is only limited by our imagination.”
Then too, Dr Berkan says big data can also be a curse if the cleaning (deleting) technologies are not considered as part of the Super Software operation. In his previous post, “information pollution”, he emphasized the danger of uncontrollable growth of information which is the invisible devil in information age.
credits: Search Engine Journal/SEG
Gartner says, Bring Your Own Device is an alternative strategy that allows employees, business partners and other users to use a personally selected and purchased client device to execute enterprise applications and access data. For most organizations, the program is limited to smartphones and tablets, but the strategy may also be used for PCs. It may or may not include subsidies for equipment or service fees.
Currently Big Data is synonymous with technologies like Hadoop, and the “NoSQL” class of databases like Mongo (document stores) and Cassandra (key-values). Today it’s possible to stream real-time analytics with ease. Spinning clusters up and down is a (relative) cinch, accomplished in 20 minutes or less.
Now there are new untapped open source technologies out there.
Storm and Kafka is said to handle data velocities of tens of thousands of messages every second.
R is an open source statistical programming language. It is incredibly powerful. Over two million (and counting) analysts use R. R works very well with Hadoop
SAP Hana is an in-memory analytics platform that includes an in-memory database and a suite of tools and software for creating analytical processes and moving data in and out, in the right formats.
Big data is measured in terabytes, petabytes, or more. Data becomes “big data” when it outgrows your current ability to process it, store it, and cope with it efficiently. Storage has become very cheap in the past ten years, allowing loads of data to be collected. However, our ability to actually process the loads of data quickly has not scaled as fast. Traditional tools to analyze and store data — SQL databases, spreadsheets, the Chinese abacus — were not designed to deal with vast data problems. The amount of information in the world is now measured in zettabytes. A zettabyte, which is 1021 bytes (that is 1 followed by twenty-one zeroes), is a big number. Imagine writing three paragraphs describing your favorite movie – that’s about 1 kilobyte. Next, imagine writing three paragraphs for every grain of sand on the earth — that amount of information is in the zettabyte range.
The best tool available today for processing and storing herculean amounts of big data is Hadoop. Hundreds or thousands of computers are thrown at the big data problem, rather than using single computer.
Hadoop makes data mining, analytics, and processing of big data cheap and fast. Hadoop can take most of your big data problems and unlock the answers, because you can keep all your data, including all of your historical data, and get an answer before your children graduate college.
Apache Hadoop is an open-source project inspired by research of Google. Hadoop is named after the stuffed toy elephant of the lead programmer’s son. In Hadoop parlance, the group of coordinated computers is called a cluster, and the individual computers in the cluster are called nodes.
Tech Giants Google and Facebook have shown their presences in New York in recent years. Some big-name newcomers are headquartered here. Plans for an elite technology graduate school, attracted with city money, are getting enough attention that a federal patent officer is being stationed on campus in a first-of-its-kind arrangement.
Entrepreneurs say New York also faces particular challenges, including problematic broadband access in a few areas and a limited tech talent base, though the city is trying to address the concerns. New York solid ground so to speak in financial technology and online publishing, but the growth of social media and digital marketing opens new prospects for a city known for communications, design and advertising. Some prominent start ups include Foursquare, Tumblr, Kickstarter and Gilt Groupe. They were established in New York in the past five years.
The city’s biggest move was : offering 12 acres of land and up to $100 million in improvements for a tech-focused graduate school. Cornell University and Technion-Israel Institute of Technology won a competition to run the school, set to start with a handful of students in January. It will be the first institution in the country to boast about an on-campus patent officer, acting U.S. Commerce Secretary Rebecca Blank announced this month. Columbia University and New York University were also offered $15 million apiece in incentives to create new technology programs.
Business intelligence applications, have begun to transition from an OLAP to a new type of service that connects different data sources from social networks, third-party apps and other sources. NoSQL has begun to appear as a popular option for its scaling capability across cheap, commodity-based nodes. It’s much cheaper than scaling with vertically integrated systems that require attaching expensive storage arrays.
A new generation of big data applications are turning up. Which in turn has put pressure on enterprise vendors to modify existing software suites.Venture capitalists will continue to invest in data infrastructure and big data apps that represent the manifest disruption in IT.