Big Data in Companies Essay Example for Free

Big Data in Companies Essay Big data (also spelled Big Data) is a general term used to describe the voluminous amount of unstructured and semi-structured data a company creates data that would take too much time and cost too much money to load into a relational database for analysis. Although Big data doesnt refer to any specific quantity, the term is often used when speaking about petabytes and exabytes of data. A primary goal for looking at big data is to discover repeatable business patterns. It’s generally accepted that unstructured data, most of it located in text files, accounts for at least 80% of an organization’s data. If left unmanaged, the sheer volume of unstructured data that’s generated each year within an enterprise can be costly in terms of storage. Unmanaged data can also pose a liability if information cannot be located in the event of a compliance audit or lawsuit. Big data analytics is often associated with cloud computing because the analysis of large data sets in real-time requires a framework like MapReduce to distribute the work among tens, hundreds or even thousands of computers. Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it. The hot IT buzzword of 2012, big data has become viable as cost-effective approaches have emerged to tame the volume, velocity and variability of massive data. Within this data lie valuable patterns and information, previously hidden because of the amount of work required to extract them. To leading corporations, such as Walmart or Google, this power has been in reach for some time, but at fantastic cost. Today’s commodity hardware, cloud architectures and open source software bring big data processing into the reach of the less well-resourced. Big data processing is eminently feasible for even the small garage startups, who can cheaply rent server time in the cloud. The value of big data to an organization falls into two categories: analytical use, and enabling new products. Big data analytics can reveal insights hidden previously by data too costly to process, such as peer influence among customers, revealed by analyzing shoppers’ transactions, social and geographical data. Being able to process every item of data in reasonable time removes the troublesome need for sampling and promotes an investigative approach to data, in contrast to the somewhat static nature of running predetermined reports. The past decade’s successful web startups are prime examples of big data used as an enabler of new products and services. For example, by combining a large number of signals from a user’s actions and those of their friends, Facebook has been able to craft a highly personalized user experience and create a new kind of advertising business. It’s no coincidence that the lion’s share of ideas and tools underpinning big data have emerged from Google, Yahoo, Amazon and Facebook. The emergence of big data into the enterprise brings with it a necessary counterpart: agility. Successfully exploiting the value in big data requires experimentation and exploration. Whether creating new products or looking for ways to gain competitive advantage, the job calls for curiosity and an entrepreneurial outlook. What does big data look like? As a catch-all term, â€Å"big data† can be pretty nebulous, in the same way that the term â€Å"cloud† covers diverse technologies. Input data to big data systems could be chatter from social networks, web server logs, traffic flow sensors, satellite imagery, broadcast audio streams, banking transactions, MP3s of rock music, the content of web pages, scans of government documents, GPS trails, telemetry from automobiles, financial market data, the list goes on. Are these all really the same thing? To clarify matters, the three Vs of volume, velocity and variety are commonly used to characterize different aspects of big data. They’re a helpful lens through which to view and understand the nature of the data and the software platforms available to exploit them. Most probably you will contend with each of the Vs to one degree or another. Volume The benefit gained from the ability to process large amounts of information is the main attraction of big data analytics. Having more data beats out having better models: simple bits of math can be unreasonably effective given large amounts of data. If you could run that forecast taking into account 300 factors rather than 6, could you predict demand better? This volume presents the most immediate challenge to conventional IT structures. It calls for scalable storage, and a distributed approach to querying. Many companies already have large amounts of archived data, perhaps in the form of logs, but not the capacity to process it. Assuming that the volumes of data are larger than those conventional relational database infrastructures can cope with, processing options break down broadly into a choice between massively parallel processing architectures — data warehouses or databases such as Greenplum — and Apache Hadoop-based solutions. This choice is often informed by the degree to which the one of the other â€Å"Vs† — variety — comes into play. Typically, data warehousing approaches involve predetermined schemas, suiting a regular and slowly evolving dataset. Apache Hadoop, on the other hand, places no conditions on the structure of the data it can process. At its core, Hadoop is a platform for distributing computing problems across a number of servers. First developed and released as open source by Yahoo, it implements the MapReduce approach pioneered by Google in compiling its search indexes. Hadoop’s MapReduce involves distributing a dataset among multiple servers and operating on the data: the â€Å"map† stage. The partial results are then recombined: the â€Å"reduce† stage. To store data, Hadoop utilizes its own distributed filesystem, HDFS, which makes data available to multiple computing nodes. A typical Hadoop usage pattern involves three stages: * loading data into HDFS, * MapReduce operations, and * retrieving results from HDFS. This process is by nature a batch operation, suited for analytical or non-interactive computing tasks. Because of this, Hadoop is not itself a database or data warehouse solution, but can act as an analytical adjunct to one. One of the most well-known Hadoop users is Facebook, whose model follows this pattern. A MySQL database stores the core data. This is then reflected into Hadoop, where computations occur, such as creating recommendations for you based on your friends’ interests. Facebook then transfers the results back into MySQL, for use in pages served to users. Velocity The importance of data’s velocity — the increasing rate at which data flows into an organization — has followed a similar pattern to that of volume. Problems previously restricted to segments of industry are now presenting themselves in a much broader setting. Specialized companies such as financial traders have long turned systems that cope with fast moving data to their advantage. Now it’s our turn. Why is that so? The Internet and mobile era means that the way we deliver and consume products and services is increasingly instrumented, generating a data flow back to the provider. Online retailers are able to compile large histories of customers’ every click and interaction: not just the final sales. Those who are able to quickly utilize that information, by recommending additional purchases, for instance, gain competitive advantage. The smartphone era increases again the rate of data inflow, as consumers carry with them a streaming source of geolocated imagery and audio data. It’s not just the velocity of the incoming data that’s the issue: it’s possible to stream fast-moving data into bulk storage for later batch processing, for example. The importance lies in the speed of the feedback loop, taking data from input through to decision. A commercial from IBM makes the point that you wouldn’t cross the road if all you had was a five-minute old snapshot of traffic location. There are times when you simply won’t be able to wait for a report to run or a Hadoop job to complete. Industry terminology for such fast-moving data tends to be either â€Å"streaming data,† or â€Å"complex event processing. This latter term was more established in product categories before streaming processing data gained more widespread relevance, and seems likely to diminish in favor of streaming. There are two main reasons to consider streaming processing. The first is when the input data are too fast to store in their entirety: in order to keep storage requirements practical some level of analysis must occur as the data streams in. At the extreme end of the scale, the Large Hadron Collider at CERN generates so much data that scientists must discard the overwhelming majority of it — hoping hard they’ve not thrown away anything useful. The second reason to consider streaming is where the application mandates immediate response to the data. Thanks to the rise of mobile applications and online gaming this is an increasingly common situation. Product categories for handling streaming data divide into established proprietary products such as IBM’s InfoSphere Streams, and the less-polished and still emergent open source frameworks originating in the web industry: Twitter’s Storm, and Yahoo S4. As mentioned above, it’s not just about input data. The velocity of a system’s outputs can matter too. The tighter the feedback loop, the greater the competitive advantage. The results might go directly into a product, such as Facebook’s recommendations, or into dashboards used to drive decision-making. It’s this need for speed, particularly on the web, that has driven the development of key-value stores and columnar databases, optimized for the fast retrieval of precomputed information. These databases form part of an umbrella category known as NoSQL, used when relational models aren’t the right fit. Microsoft SQL Server is a comprehensive information platform offering enterprise-ready technologies and tools that help businesses derive maximum value from information at the lowest TCO. SQL Server 2012 launches next year, offering a cloud-ready information platform delivering mission-critical confidence, breakthrough insight, and cloud on your terms; find out more at www. microsoft. com/sql. Variety Rarely does data present itself in a form perfectly ordered and ready for processing. A common theme in big data systems is that the source data is diverse, and doesn’t fall into neat relational structures. It could be text from social networks, image data, a raw feed directly from a sensor source. None of these things come ready for integration into an application. Even on the web, where computer-to-computer communication ought to bring some guarantees, the reality of data is messy. Different browsers send different data, users withhold information, they may be using differing software versions or vendors to communicate with you. And you can bet that if part of the process involves a human, there will be error and inconsistency. A common use of big data processing is to take unstructured data and extract ordered meaning, for consumption either by humans or as a structured input to an application. One such example is entity resolution, the process of determining exactly what a name refers to. Is this city London, England, or London, Texas? By the time your business logic gets to it, you don’t want to be guessing. The process of moving from source data to processed application data involves the loss of information. When you tidy up, you end up throwing stuff away. This underlines a principle of big data: when you can, keep everything. There may well be useful signals in the bits you throw away. If you lose the source data, there’s no going back. Despite the popularity and well understood nature of relational databases, it is not the case that they should always be the destination for data, even when tidied up. Certain data types suit certain classes of database better. For instance, documents encoded as XML are most versatile when stored in a dedicated XML store such as MarkLogic. Social network relations are graphs by nature, and graph databases such as Neo4J make operations on them simpler and more efficient. Even where there’s not a radical data type mismatch, a disadvantage of the relational database is the static nature of its schemas. In an agile, exploratory environment, the results of computations will evolve with the detection and extraction of more signals. Semi-structured NoSQL databases meet this need for flexibility: they provide enough structure to organize data, but do not require the exact schema of the data before storing it.

Ludwig Wittgenstein: The Nature of Religious Language Essay -- Languag

Ludwig Wittgenstein once believed that language's function was to name objects and the meaning of language was found in the objects for which it stands. He later rejected this and centred on how language works and is used, believing that problems of religious language come from misunderstanding its usage. Wittgenstein was no longer concerned with the truth or falsity of language but the way it is used and the functions that it performs, as he said 'Don't ask for the meaning ask for the use.' Wittgenstein recognised that language is equivocal as words have many different meanings, such as the word 'pen' whose meaning changes in different contexts. He saw language as a game, which like all games had its own set of rules. Different contexts or 'forms of life' are like different language games with their own self contained rules. Those not involved in a particular language game effectively become 'non-players' and so the language holds no meaning for them, however, this does not give the non-believer the right to dismiss religious language as meaningless. Wittgenstein used the example of 'soul' to illustrate the problems of trying to use words in the wrong language 'game'. He felt that the problems stemming from the word 'soul' are caused because people try to see it as a physical object. Such problems would disappear if people realised that the 'physical object game' didn't apply in this case. It was argued that language is a social product, therefore individuals could not have their own private language as one could not be certain that language was being used correctly. Wittgenstein therefore rejected Descartes ... ... Religious believers are also involved in other language games because they are involved in other aspects of life. This means that religious language is not totally isolated and there will be some common ground with other 'language games'. This may suggest that the non-believer may be able to understand religious language and decide if it holds any meaning for them. It is also argued that if anything, non-believers may be able to understand religious language better than a believer, as they can be more objective about it. It seems that Wittgenstein was mistaken as seeing religious language only being intelligible in the context of religious belief. Many religious statements entail a truth which is not dependent upon context, but statements such as 'Jesus died to bring salvation' are though of as true for everyone.

Communications Character Conflict Essay Essay

â€Å"I can’t believe you, all your gifts, all your powers, and you.. you squander them for your own personal gain. (Hal Stewart) ‘Yes! (Megamind) ‘No! I’m the villain! † this is the main conflict between characters’ Megamind and Hal Stewart in the hit motion picture Megamind. Megamind defeats the cities beloved super hero â€Å"Metro Man†, they had been enemies since grade school and Megamind has always been the bad guy causing trouble and Metro Man was always saving the day and the people from the evils of Megeminds treachery. But soon after this deafeat Megamind realizes he misses the way things used to be, there was no more excitement and rush of being the villain in an epic good vs. evil battle. So, Megamind has devised a way that he can give any normal human being all the gifts and powers that Metro Man himself possessed. He settles on a young man named Hal Stewart, a cameraman for a news station, which he filmed the news girl who he claimed to be in love with, but everyone thought she was with Metro Man because he was always saving her. So Megamind gives Hal all the super powers, and gives him a new identity of â€Å"Titan†. However, to Megminds Dismay, Hal starts abusing his powers and begins stealing from banks, and using his powers to get equipment, video games etc.. This is a great example of unproductive conflict. Instead of creating a new evil versus good scenario with Hal or â€Å"Titan†, Hal just commandeers the whole city and puts them all at his mercy. It has many negative impacts the two characters, their relationship, and the situation. The aggression between them and the situation in general becomes increases greatly and turns even hostile because of the flaws in their communication. Megamind has certain qualities about the way he communicates that it often gets him into trouble. In fact pretty much every idea he comes up with in the process, he always seems to get either seriously injured, or suffers in some other way, all because of his communicational handicap. Some of the functions of communication Megamind uses, is he begins a relationship with a character named Hal Stewart to achieve a specific goal premeditated so he could accomplish what he wished to have done. What he wanted done was to turn Hal into a super hero and give him all the powers that the late Metro Man possessed, so that he could resume his role as the villain once again, since he defeated the last super hero he longed for the epic battle of â€Å"Good vs. Evil. † First of all, Hal isn’t the brightest pick to turn someone into a super hero, but Megamind is determined he is the perfect pick after a misunderstood first impression of Hal. Megaminds best friend even shows in many ways, he does not approve of Hal as being the one who has the traits of being a good super hero, most of the ways non verbal. Megamind even changes his own identity as Hal’s â€Å"Space Dad†, for the purpose of guiding Hal into fulfilling his destiny as the city’s new found super hero â€Å"Titan†. So Megamind actually is affecting the situation and their relationship as two different important character’s in Hal’s life and will play play big parts in his decisions. Hal Stewart, aka â€Å"Titan† aka â€Å"Cameraman†, is a very interesting character because of how he expresses his own communicational traits, it is entertaining. His perception of what a â€Å"super hero† means, and what sort of privileges or advantages he believes comes with being â€Å"Titan† are affected by the culture he lives in. Some of his expectations, the most important towards the story and the one that makes the most dramatic influence is; the super hero always gets the girl. This thinking of Hal’s is a mindlessness perception and also selective perception of how he sees a super hero. Obviously he does not get the girl he claims he’s in love with, because the thing he is mindlessness about is that just because you can fly and have huge muscles doesn’t mean you will always get girl, its about what’s underneath, not on the surface, which I believe is the lesson â€Å"Life† is trying to teach him. Another misperception he has is what should be accomplished with the incredible new â€Å"gifts† that he has recently required. Instead of catching bad guys, and making sure justice stands within the city and her people, Hal actually robs banks, and other equipment, video games etc.. nd is determined that the powers mean â€Å"get rich, and get rich quick†. These are all things that Megamind does not anticipate before hand and turns into being the exact opposite of what he wanted, all because Hal’s misunderstanding, and the communicational barriers of Megamind. Although, there isn’t a whole lot of competent communicati on going on with Megamind and Hal, in the end he still gets an incredibly large amount accomplished. The things that were bigger then even his beloved epic good vs. evil battles. He grew in more important ways, he grew as a specimen, and became a happier person, which ultimately should be eternal goal of communication and the functions and steps that are not only necessary and basically impossible to accomplish anything without them, competent or incompetent, things still move forward and things get done. In the end Megamind finds his true purpose and a higher purpose, along the way falling in love and discovering a more meaningful life all thanks to communication, and the culture which helps him perceive and judge.

The New Class Of Anti Diabetic Drugs - 2008 Words

Abstract Glitazones are the new class of anti-diabetic drugs that are the first to be able to manage glycaemia goals. Troglitazone was first approved for the market in 1997, but it was withdrawn from the market by 2000. After the withdrawal of troglitazone, rosiglitazone and pioglitazone were introduced in 1999 as potentially safer alternatives. However, currently rosiglitazone is under black box warning for increased risk of cardiovascular disease and pioglitazone is in a nutshell as the drug required more investigation. In this review, the potential ability to predict the adverse drug reactions (ADRs) are examined. If it is possible to predict ADRs, should it be done in the future. Introduction Millions of people worldwide are affected due to diabetes, 90% of them having type 2 diabetes mellitus (non-insulin dependent). Since 1921, drug therapy has been improved minutely and in 1950s the antidiabetic sulphonamides and biguanides has been introduced. 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