Technology has started to invade and improve different sectors including healthcare. Big data analytics are introducing positive development to this area and it makes it easier for patients and clients to go through different processes.
Showing posts with label big data analytics. Show all posts
Showing posts with label big data analytics. Show all posts
Wednesday, October 18, 2017
Monday, March 16, 2015
Big Data
| Traditional approaches to data integration that rely on moving data, are struggling to handle the extreme volume and diversity of Big Data. Data virtualization has emerged to address the need for real-time, universal access to data, regardless of format or location. |
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No person would ever discount the importance that data plays in today’s business environment. This data has grown exponentially over the years—whether it’s in the form of streaming data, legacy data, or even operational data—and it is moving faster than ever before. All told, this data is completely transforming the face of business, and it is no incumbent upon businesses to have a comprehensive solution for dealing with it in order for those businesses to ensure their long-term success.
Dealing with all of this data presents a number of technical challenges. But, it also presents a number of tremendous opportunities. Further, not all of the data that businesses have to contend with these days is transactional. Some of it is machine-to-machine based data, as is the case with RFID tags, and some of it has to do with regulatory compliance, as is the case with data in the financial sector.
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Having ready access to this data is incredibly important for every business’ analytics and business intelligence. However, facilitating such ready access requires a great deal, namely the realignment of data closer to analytics. Also, such ready access requires that non-relational data and relational data be seamlessly blended together. To accomplish this, traditional methods of accessing this data, which require data to be physically moved, must be done away with.
No one can doubt the importance of offering this level of convenient access to data. Both decision makers and customers have been conditioned by our modern society to expect access to data on the fly. However, just because this expectation has been conditioned doesn’t mean that meeting that expectation is easy. There are a number of obstacles that must be overcome in order to facilitate this. Most importantly, a solution must allow for data from multiple sources to be integrated together and standardized, and the data must be made consistent across the business side of things and the customer side of things.
The way to accomplish this is through a solution that virtually combines data indiscriminately of where that data comes from. If this can be accomplished, then the BI tools and analytics tools that a business employs can be used to their maximum effectiveness. However, the majority of businesses do not deploy such a solution. Rather, they rely upon the outdated ETL method, which stands for Extract, Transform and Load. While this method may have been sufficient in the past, it isn’t sufficient any longer. Such a method fails to meet the need of timely access to data, as the data must be physically moved for the process to work. Further, the process of physically moving the data reduces the consistency of the data, introducing a number of additional costs and complexities for businesses.
Mainframe data virtualization is the answer, as it places data next to the analytics software used to analyze that data. It accomplishes this by diverting the process of data integration to specialized processors – IBM System Z processors – that operate in tandem with a mainframe’s central processors. There are no software license charges that need to be considered with this method, and MIPs capacity is not affected by the process of data integration. Because of this, the production of data on the mainframe is undisturbed and TCO is dramatically reduced.
The problems with latency, consistency, and accurateness experienced with ETL methods are not experienced with mainframe data virtualization. In fact, those problems are entirely eliminated. Such a method allows data to be easily accessed through and dealt with through BI tools and analytics tools, and the problem of dealing with unfamiliar mainframe environments is eliminated.
All told, this empowers the decision makers of businesses to meet their goals of mitigating risk and driving expansion. Timely and accurate data is put right in their hands, empowering these decision makers to successfully meet the demands of their customers, identify threats in the market place, and even to attack new business opportunities. Mainframe data virtualization is the future, and it leaves everything else in the dust.
| By Mike Miranda | Embed |
Author Bio - Mike Miranda is a writer and PR person for Rocket Software.
Wednesday, June 26, 2013
Quantum Computing
| Researchers have proposed a new algorithm for quantum computing, that will speed a particular type of problem. But swifter calculations would come at the cost of greater physical resources devoted to precise timekeeping, their analysis has found. |
Fundamental discoveries in quantum information science have potential for dramatic impact on technologies. While considerable progress has been made in recent years in understanding the fundamentals of quantum information science on both experimental and theoretical sides, many fundamental issues and challenges remain unresolved.
Another question is how it will be possible to incorporate quantum computing into work with Big Data. With advances in this area, it is interesting to consider how the environment for big data analytics will evolve in the future. Will CPUs, core, and other discrete processing elements in Big Data architecture also be calculated through quantum entanglement?
The goal of DARPA's Quantum Entanglement Science and Technology (QuEST) program is to investigate innovative approaches that enable revolutionary advances in the fundamental understanding of quantum information science related to small quantum systems.
Under the QuEST program, Tom Wong, a graduate student in physics and David Meyer, professor of mathematics at the University of California, San Diego, have proposed a new algorithm for quantum computing, that will speed a particular type of problem. But swifter calculations would come at the cost of greater physical resources devoted to precise timekeeping, their analysis has found.
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Wong and Meyer's algorithm would be used to conduct a task called an unstructured search. The goal is to locate a particular item within an unsorted pile of data. Solving this problem on a classical computer, which uses 1s and 0s stored on magnetic media, is akin to flipping through a deck of cards, one by one, Wong said. Searching through a large data set could take a very long time.
Quantum computing, based on matter held in a quantum state, often for quite brief periods of time, takes advantage of an oddity of the quantum world in which a particle, like a photon or a boson, can exist in more than one state at once, a property called superposition. This would allow multiple possibilities to be considered simultaneously, though once measured, quantum objects will yield a single answer.
The trick then, is to design algorithms so that wrong answers cancel out and correct answers accumulate. The nature of those algorithms depends on the medium in which information is stored.
Meyer and Wong considered a computer based on a state of matter called a Bose-Einstein condensate. These are atoms caught in an electromagnetic trap and chilled so cold that they “fall” into a shared lowest quantum state and act as one.
The equation usually used to describe quantum systems is linear, but the one that approximates the state of a Bose-Einstein condensate has a term that is cubed. In a paper published in the New Journal of Physics, they propose computing with this cubic equation which will more rapidly converge on the answer.
Their algorithm can be made to search for a particular item among a million items in the same time it would take to search among ten items.
“It seems like we’re cheating somehow,” Wong said, exceeding the theoretical maximum speed, but on careful consideration of the resources required to accomplish this, he and Meyer determined that gains in speed would have physical costs.
“It seems like we’re cheating somehow,” Wong said, exceeding the theoretical maximum speed, but on careful consideration of the resources required to accomplish this, he and Meyer determined that gains in speed would have physical costs.
The algorithm, and derivatives of it may have implications for the future management and use of Big Data.
Because the search is so sudden, timekeeping, which uses an atomic clock, would have to be very precise. This requirement sets a lower limit on the number of ions that make up the atomic clock.
The other resource is the computing medium itself, the Bose-Einstein condensate. “If we want to run this algorithm, we’re going to need a certain number of atoms,” Wong said. “This is how many atoms we need for this nonlinear equation to be valid, to be a correct approximation of the underlying quantum theory. That is new.”
A patent on this novel approach to quantum computing is pending.
SOURCE University of California, San Diego
Because the search is so sudden, timekeeping, which uses an atomic clock, would have to be very precise. This requirement sets a lower limit on the number of ions that make up the atomic clock.
The other resource is the computing medium itself, the Bose-Einstein condensate. “If we want to run this algorithm, we’re going to need a certain number of atoms,” Wong said. “This is how many atoms we need for this nonlinear equation to be valid, to be a correct approximation of the underlying quantum theory. That is new.”
A patent on this novel approach to quantum computing is pending.
SOURCE University of California, San Diego
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