Bog Rata

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Bog Rata

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The perception of shared storage architectures— Storage area network SAN and Network-attached storage NAS —is that they are relatively slow, complex, and expensive.

These qualities are not consistent with big data analytics systems that thrive on system performance, commodity infrastructure, and low cost.

Real or near-real-time information delivery is one of the defining characteristics of big data analytics. Latency is therefore avoided whenever and wherever possible.

Data in direct-attached memory or disk is good—data on memory or disk at the other end of a FC SAN connection is not. The cost of a SAN at the scale needed for analytics applications is very much higher than other storage techniques.

There are advantages as well as disadvantages to shared storage in big data analytics, but big data analytics practitioners as of [update] did not favour it.

Developed economies increasingly use data-intensive technologies. There are 4. The world's effective capacity to exchange information through telecommunication networks was petabytes in , petabytes in , 2.

This also shows the potential of yet unused data i. While many vendors offer off-the-shelf solutions for big data, experts recommend the development of in-house solutions custom-tailored to solve the company's problem at hand if the company has sufficient technical capabilities.

The use and adoption of big data within governmental processes allows efficiencies in terms of cost, productivity, and innovation, [54] but does not come without its flaws.

Data analysis often requires multiple parts of government central and local to work in collaboration and create new and innovative processes to deliver the desired outcome.

CRVS civil registration and vital statistics collects all certificates status from birth to death. CRVS is a source of big data for governments.

Research on the effective usage of information and communication technologies for development also known as ICT4D suggests that big data technology can make important contributions but also present unique challenges to International development.

Big data analytics has helped healthcare improve by providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries and fragmented point solutions.

The level of data generated within healthcare systems is not trivial. With the added adoption of mHealth, eHealth and wearable technologies the volume of data will continue to increase.

This includes electronic health record data, imaging data, patient generated data, sensor data, and other forms of difficult to process data.

There is now an even greater need for such environments to pay greater attention to data and information quality.

Big data in health research is particularly promising in terms of exploratory biomedical research, as data-driven analysis can move forward more quickly than hypothesis-driven research.

A related application sub-area, that heavily relies on big data, within the healthcare field is that of computer-aided diagnosis in medicine.

For this reason, big data has been recognized as one of the seven key challenges that computer-aided diagnosis systems need to overcome in order to reach the next level of performance.

A McKinsey Global Institute study found a shortage of 1. Private boot camps have also developed programs to meet that demand, including free programs like The Data Incubator or paid programs like General Assembly.

Because one-size-fits-all analytical solutions are not desirable, business schools should prepare marketing managers to have wide knowledge on all the different techniques used in these sub domains to get a big picture and work effectively with analysts.

To understand how the media uses big data, it is first necessary to provide some context into the mechanism used for media process.

It has been suggested by Nick Couldry and Joseph Turow that practitioners in Media and Advertising approach big data as many actionable points of information about millions of individuals.

The industry appears to be moving away from the traditional approach of using specific media environments such as newspapers, magazines, or television shows and instead taps into consumers with technologies that reach targeted people at optimal times in optimal locations.

The ultimate aim is to serve or convey, a message or content that is statistically speaking in line with the consumer's mindset.

For example, publishing environments are increasingly tailoring messages advertisements and content articles to appeal to consumers that have been exclusively gleaned through various data-mining activities.

Channel 4 , the British public-service television broadcaster, is a leader in the field of big data and data analysis.

Health insurance providers are collecting data on social "determinants of health" such as food and TV consumption , marital status, clothing size and purchasing habits, from which they make predictions on health costs, in order to spot health issues in their clients.

It is controversial whether these predictions are currently being used for pricing. Big data and the IoT work in conjunction. Data extracted from IoT devices provides a mapping of device inter-connectivity.

Such mappings have been used by the media industry, companies and governments to more accurately target their audience and increase media efficiency.

IoT is also increasingly adopted as a means of gathering sensory data, and this sensory data has been used in medical, [81] manufacturing [82] and transportation [83] contexts.

We would know when things needed replacing, repairing or recalling, and whether they were fresh or past their best. Especially since , big data has come to prominence within business operations as a tool to help employees work more efficiently and streamline the collection and distribution of information technology IT.

Big data can be used to improve training and understanding competitors, using sport sensors. It is also possible to predict winners in a match using big data analytics.

Thus, players' value and salary is determined by data collected throughout the season. In Formula One races, race cars with hundreds of sensors generate terabytes of data.

These sensors collect data points from tire pressure to fuel burn efficiency. Besides, using big data, race teams try to predict the time they will finish the race beforehand, based on simulations using data collected over the season.

Significant applications of big data included minimising the spread of the virus, case identification and development of medical treatment.

Governments used big data to track infected people to minimise spread. Encrypted search and cluster formation in big data were demonstrated in March at the American Society of Engineering Education.

Amir Esmailpour at UNH Research Group investigated the key features of big data as the formation of clusters and their interconnections.

They focused on the security of big data and the orientation of the term towards the presence of different types of data in an encrypted form at cloud interface by providing the raw definitions and real-time examples within the technology.

Moreover, they proposed an approach for identifying the encoding technique to advance towards an expedited search over encrypted text leading to the security enhancements in big data.

The SDAV Institute aims to bring together the expertise of six national laboratories and seven universities to develop new tools to help scientists manage and visualize data on the Department's supercomputers.

The U. The European Commission is funding the 2-year-long Big Data Public Private Forum through their Seventh Framework Program to engage companies, academics and other stakeholders in discussing big data issues.

The project aims to define a strategy in terms of research and innovation to guide supporting actions from the European Commission in the successful implementation of the big data economy.

Outcomes of this project will be used as input for Horizon , their next framework program. The British government announced in March the founding of the Alan Turing Institute , named after the computer pioneer and code-breaker, which will focus on new ways to collect and analyze large data sets.

At the University of Waterloo Stratford Campus Canadian Open Data Experience CODE Inspiration Day, participants demonstrated how using data visualization can increase the understanding and appeal of big data sets and communicate their story to the world.

The findings suggest there may be a link between online behaviour and real-world economic indicators. The results hint that there may potentially be a relationship between the economic success of a country and the information-seeking behavior of its citizens captured in big data.

Eugene Stanley introduced a method to identify online precursors for stock market moves, using trading strategies based on search volume data provided by Google Trends.

Big data sets come with algorithmic challenges that previously did not exist. Hence, there is a need to fundamentally change the processing ways.

The Workshops on Algorithms for Modern Massive Data Sets MMDS bring together computer scientists, statisticians, mathematicians, and data analysis practitioners to discuss algorithmic challenges of big data.

An important research question that can be asked about big data sets is whether you need to look at the full data to draw certain conclusions about the properties of the data or is a sample good enough.

The name big data itself contains a term related to size and this is an important characteristic of big data.

But Sampling statistics enables the selection of right data points from within the larger data set to estimate the characteristics of the whole population.

For example, there are about million tweets produced every day. Is it necessary to look at all of them to determine the topics that are discussed during the day?

Is it necessary to look at all the tweets to determine the sentiment on each of the topics? In manufacturing different types of sensory data such as acoustics, vibration, pressure, current, voltage and controller data are available at short time intervals.

To predict downtime it may not be necessary to look at all the data but a sample may be sufficient.

Big Data can be broken down by various data point categories such as demographic, psychographic, behavioral, and transactional data. With large sets of data points, marketers are able to create and use more customized segments of consumers for more strategic targeting.

There has been some work done in Sampling algorithms for big data. A theoretical formulation for sampling Twitter data has been developed.

Critiques of the big data paradigm come in two flavors: those that question the implications of the approach itself, and those that question the way it is currently done.

Mark Graham has leveled broad critiques at Chris Anderson 's assertion that big data will spell the end of theory: [] focusing in particular on the notion that big data must always be contextualized in their social, economic, and political contexts.

To overcome this insight deficit, big data, no matter how comprehensive or well analyzed, must be complemented by "big judgment," according to an article in the Harvard Business Review.

Much in the same line, it has been pointed out that the decisions based on the analysis of big data are inevitably "informed by the world as it was in the past, or, at best, as it currently is".

In order to make predictions in changing environments, it would be necessary to have a thorough understanding of the systems dynamic, which requires theory.

Agent-based models are increasingly getting better in predicting the outcome of social complexities of even unknown future scenarios through computer simulations that are based on a collection of mutually interdependent algorithms.

In health and biology, conventional scientific approaches are based on experimentation. For these approaches, the limiting factor is the relevant data that can confirm or refute the initial hypothesis.

Broad , are to be considered. Privacy advocates are concerned about the threat to privacy represented by increasing storage and integration of personally identifiable information ; expert panels have released various policy recommendations to conform practice to expectations of privacy.

Nayef Al-Rodhan argues that a new kind of social contract will be needed to protect individual liberties in a context of Big Data and giant corporations that own vast amounts of information.

The use of Big Data should be monitored and better regulated at the national and international levels.

The 'V' model of Big Data is concerting as it centres around computational scalability and lacks in a loss around the perceptibility and understandability of information.

This led to the framework of cognitive big data , which characterizes Big Data application according to: [].

Large data sets have been analyzed by computing machines for well over a century, including the US census analytics performed by IBM 's punch-card machines which computed statistics including means and variances of populations across the whole continent.

In more recent decades, science experiments such as CERN have produced data on similar scales to current commercial "big data". However, science experiments have tended to analyze their data using specialized custom-built high-performance computing super-computing clusters and grids, rather than clouds of cheap commodity computers as in the current commercial wave, implying a difference in both culture and technology stack.

Ulf-Dietrich Reips and Uwe Matzat wrote in that big data had become a "fad" in scientific research. Integration across heterogeneous data resources—some that might be considered big data and others not—presents formidable logistical as well as analytical challenges, but many researchers argue that such integrations are likely to represent the most promising new frontiers in science.

Users of big data are often "lost in the sheer volume of numbers", and "working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth".

Big data analysis is often shallow compared to analysis of smaller data sets. Big data is a buzzword and a "vague term", [] [] but at the same time an "obsession" [] with entrepreneurs, consultants, scientists and the media.

Big data showcases such as Google Flu Trends failed to deliver good predictions in recent years, overstating the flu outbreaks by a factor of two.

Similarly, Academy awards and election predictions solely based on Twitter were more often off than on target. Big data often poses the same challenges as small data; adding more data does not solve problems of bias, but may emphasize other problems.

In particular data sources such as Twitter are not representative of the overall population, and results drawn from such sources may then lead to wrong conclusions.

Google Translate —which is based on big data statistical analysis of text—does a good job at translating web pages. However, results from specialized domains may be dramatically skewed.

On the other hand, big data may also introduce new problems, such as the multiple comparisons problem : simultaneously testing a large set of hypotheses is likely to produce many false results that mistakenly appear significant.

Ioannidis argued that "most published research findings are false" [] due to essentially the same effect: when many scientific teams and researchers each perform many experiments i.

Furthermore, big data analytics results are only as good as the model on which they are predicated. In an example, big data took part in attempting to predict the results of the U.

Presidential Election [] with varying degrees of success. Big Data has been used in policing and surveillance by institutions like law enforcement and corporations.

According to Sarah Brayne's Big Data Surveillance: The Case of Policing , [] big data policing can reproduce existing societal inequalities in three ways:.

If these potential problems are not corrected or regulating, the effects of big data policing continue to shape societal hierarchies. Conscientious usage of big data policing could prevent individual level biases from becoming institutional biases, Brayne also notes.

From Wikipedia, the free encyclopedia. This article is about large collections of data. For the band, see Big Data band.

For the practice of buying and selling of personal and consumer data, see Surveillance capitalism. Information assets characterized by such a high volume, velocity, and variety to require specific technology and analytical methods for its transformation into value.

This article may contain an excessive number of citations. Please consider removing references to unnecessary or disreputable sources , merging citations where possible, or, if necessary, flagging the content for deletion.

In particular many references are "spammed" here for promotional purposes. These need to be removed. November Learn how and when to remove this template message.

Main article: Internet of Things. Further information: Edge computing. For a list of companies, and tools, see also: Category:Big data.

Bibcode : Sci Retrieved 13 April Journal of Marketing Analytics. The Economist. Retrieved 9 December September Bibcode : Natur. Gigaom Blog. O'Reilly Media.

Retrieved 26 August Retrieved 2 November Release 2. Mashey 25 April Slides from invited talk. Retrieved 28 September The New York Times. International Journal of Internet Science.

Lecture Notes in Business Information Processing. Retrieved 22 March Information Systems. Retrieved 5 January Data Science for Transport.

Retrieved 8 October Exploring the ontological characteristics of 26 datasets". Journal of Financial Regulation and Compliance. Washington Post. Retrieved 15 July Retrieved on 14 November Retrieved on 25 March Retrieved 8 December Retrieved 9 March Informatica Economica.

August Computer Networks. McKinsey Global Institute. Retrieved 16 January May Pattern Recognition. Bibcode : arXivP. IEEE Access.

Monash, Curt 6 October Archived from the original on 1 March The Information Society. Retrieved 14 July Amy Gershkoff: "Generally, I find that off-the-shelf business intelligence tools do not meet the needs of clients who want to derive custom insights from their data.

Therefore, for medium-to-large organizations with access to strong technical talent, I usually recommend building custom, in-house solutions.

Retrieved 12 September World Economic Forum. Jupiter je vrhovni rimski bog, bog oluja, groma i munje. Jupiter je bog svjetla.

Neptun je rimski bog mora. Njegov je pandan kod Grka Posejdon. Po Neptunu je ime dobio jedan planet i radioaktivni element neptunij.

Imaju sina Tritona , vodenjaka. Pluton je bog podzemlja i smrti. Pluton je povezivan s Plutosom , a po njemu je nazvan planet Pluton i radioaktivni element plutonij.

Venera je udata za Vulkana , ali ga je stalno varala s Marsom , njegovim bratom. Noris Patriticus je sin Venere i Marsa.

Bila je djevica, izumiteljica glazbe. Minerva je u Rimu slavljena od U ranom Vulkanov brat je Mars.

In manufacturing different types of sensory data such as acoustics, vibration, pressure, current, voltage and controller data are available at short time intervals.

To predict downtime it may not be necessary to look at all the data but a sample may be sufficient. Big Data can be broken down by various data point categories such as demographic, psychographic, behavioral, and transactional data.

With large sets of data points, marketers are able to create and use more customized segments of consumers for more strategic targeting.

There has been some work done in Sampling algorithms for big data. A theoretical formulation for sampling Twitter data has been developed.

Critiques of the big data paradigm come in two flavors: those that question the implications of the approach itself, and those that question the way it is currently done.

Mark Graham has leveled broad critiques at Chris Anderson 's assertion that big data will spell the end of theory: [] focusing in particular on the notion that big data must always be contextualized in their social, economic, and political contexts.

To overcome this insight deficit, big data, no matter how comprehensive or well analyzed, must be complemented by "big judgment," according to an article in the Harvard Business Review.

Much in the same line, it has been pointed out that the decisions based on the analysis of big data are inevitably "informed by the world as it was in the past, or, at best, as it currently is".

In order to make predictions in changing environments, it would be necessary to have a thorough understanding of the systems dynamic, which requires theory.

Agent-based models are increasingly getting better in predicting the outcome of social complexities of even unknown future scenarios through computer simulations that are based on a collection of mutually interdependent algorithms.

In health and biology, conventional scientific approaches are based on experimentation. For these approaches, the limiting factor is the relevant data that can confirm or refute the initial hypothesis.

Broad , are to be considered. Privacy advocates are concerned about the threat to privacy represented by increasing storage and integration of personally identifiable information ; expert panels have released various policy recommendations to conform practice to expectations of privacy.

Nayef Al-Rodhan argues that a new kind of social contract will be needed to protect individual liberties in a context of Big Data and giant corporations that own vast amounts of information.

The use of Big Data should be monitored and better regulated at the national and international levels. The 'V' model of Big Data is concerting as it centres around computational scalability and lacks in a loss around the perceptibility and understandability of information.

This led to the framework of cognitive big data , which characterizes Big Data application according to: []. Large data sets have been analyzed by computing machines for well over a century, including the US census analytics performed by IBM 's punch-card machines which computed statistics including means and variances of populations across the whole continent.

In more recent decades, science experiments such as CERN have produced data on similar scales to current commercial "big data".

However, science experiments have tended to analyze their data using specialized custom-built high-performance computing super-computing clusters and grids, rather than clouds of cheap commodity computers as in the current commercial wave, implying a difference in both culture and technology stack.

Ulf-Dietrich Reips and Uwe Matzat wrote in that big data had become a "fad" in scientific research.

Integration across heterogeneous data resources—some that might be considered big data and others not—presents formidable logistical as well as analytical challenges, but many researchers argue that such integrations are likely to represent the most promising new frontiers in science.

Users of big data are often "lost in the sheer volume of numbers", and "working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth".

Big data analysis is often shallow compared to analysis of smaller data sets. Big data is a buzzword and a "vague term", [] [] but at the same time an "obsession" [] with entrepreneurs, consultants, scientists and the media.

Big data showcases such as Google Flu Trends failed to deliver good predictions in recent years, overstating the flu outbreaks by a factor of two.

Similarly, Academy awards and election predictions solely based on Twitter were more often off than on target.

Big data often poses the same challenges as small data; adding more data does not solve problems of bias, but may emphasize other problems.

In particular data sources such as Twitter are not representative of the overall population, and results drawn from such sources may then lead to wrong conclusions.

Google Translate —which is based on big data statistical analysis of text—does a good job at translating web pages.

However, results from specialized domains may be dramatically skewed. On the other hand, big data may also introduce new problems, such as the multiple comparisons problem : simultaneously testing a large set of hypotheses is likely to produce many false results that mistakenly appear significant.

Ioannidis argued that "most published research findings are false" [] due to essentially the same effect: when many scientific teams and researchers each perform many experiments i.

Furthermore, big data analytics results are only as good as the model on which they are predicated. In an example, big data took part in attempting to predict the results of the U.

Presidential Election [] with varying degrees of success. Big Data has been used in policing and surveillance by institutions like law enforcement and corporations.

According to Sarah Brayne's Big Data Surveillance: The Case of Policing , [] big data policing can reproduce existing societal inequalities in three ways:.

If these potential problems are not corrected or regulating, the effects of big data policing continue to shape societal hierarchies.

Conscientious usage of big data policing could prevent individual level biases from becoming institutional biases, Brayne also notes.

From Wikipedia, the free encyclopedia. This article is about large collections of data. For the band, see Big Data band.

For the practice of buying and selling of personal and consumer data, see Surveillance capitalism. Information assets characterized by such a high volume, velocity, and variety to require specific technology and analytical methods for its transformation into value.

This article may contain an excessive number of citations. Please consider removing references to unnecessary or disreputable sources , merging citations where possible, or, if necessary, flagging the content for deletion.

In particular many references are "spammed" here for promotional purposes. These need to be removed. November Learn how and when to remove this template message.

Main article: Internet of Things. Further information: Edge computing. For a list of companies, and tools, see also: Category:Big data.

Bibcode : Sci Retrieved 13 April Journal of Marketing Analytics. The Economist. Retrieved 9 December September Bibcode : Natur.

Gigaom Blog. O'Reilly Media. Retrieved 26 August Retrieved 2 November Release 2. Mashey 25 April Slides from invited talk.

Retrieved 28 September The New York Times. International Journal of Internet Science. Lecture Notes in Business Information Processing. Retrieved 22 March Information Systems.

Retrieved 5 January Data Science for Transport. Retrieved 8 October Exploring the ontological characteristics of 26 datasets".

Journal of Financial Regulation and Compliance. Washington Post. Retrieved 15 July Retrieved on 14 November Retrieved on 25 March Retrieved 8 December Retrieved 9 March Informatica Economica.

August Computer Networks. McKinsey Global Institute. Retrieved 16 January May Pattern Recognition. Bibcode : arXivP. IEEE Access.

Monash, Curt 6 October Archived from the original on 1 March The Information Society. Retrieved 14 July Amy Gershkoff: "Generally, I find that off-the-shelf business intelligence tools do not meet the needs of clients who want to derive custom insights from their data.

Therefore, for medium-to-large organizations with access to strong technical talent, I usually recommend building custom, in-house solutions. Retrieved 12 September World Economic Forum.

Retrieved 24 August Retrieved 30 May Statistics Views. Health Information Science and Systems. Journal of Data and Information Quality.

Computers in Biology and Medicine. Bibcode : arXivM. Healthcare Journal of New Orleans : 22— Expert Systems with Applications.

Journal of Medical Informatics. Retrieved 21 February Venture Beat. Journal of Marketing. International Journal of Communication.

Retrieved 15 April Retrieved 21 July Jenipher Wang March Data Center Journal. Retrieved 21 June Retrieved 4 August Add to that the unprecedented security and surveillance state in Xinjiang, which includes all-encompassing monitoring based on identity cards, checkpoints, facial recognition and the collection of DNA from millions of individuals.

The authorities feed all this data into an artificial-intelligence machine that rates people's loyalty to the Communist Party in order to control every aspect of their lives.

Human Rights Watch. The Nation. CBS News. Are Indian companies making enough sense of Big Data? Live Mint.

Retrieved 22 November Retrieved 28 February International Journal of Engineering Development and Research. Retrieved 14 September International Journal of Network Management.

White House. Retrieved 26 September Archived from the original PDF on 11 December Retrieved 31 May Information Week.

Wired Magazine. Retrieved 18 March Archived from the original on 5 September Retrieved 31 October Huffington Post. Retrieved 7 May Retrieved 24 March A presentation of the largest and the most powerful particle accelerator in the world, the Large Hadron Collider LHC , which started up in Its role, characteristics, technologies, etc.

LHC Brochure, English version. Retrieved 20 January LHC Guide, English version. Ars Technica.

The Conversation. Retrieved 27 September CSC World. Computer Sciences Corporation. Archived from the original on 4 January Retrieved 18 January The Globe and Mail.

Retrieved 1 October Google Cloud Platform. Retrieved 29 December The Verge. Fast Company. Scientific American. MIT Technology Review. Retrieved 12 December Retrieved 12 February Retrieved 5 March Retrieved 3 September Search Engine Land.

Indian Journal of Orthapedics. Retrieved 30 October Gov Insider. Archived from the original PDF on 9 August Minerva je u Rimu slavljena od U ranom Vulkanov brat je Mars.

Mars je u rimskoj mitologiji sin Jupitera i Junone, Vulkanov brat, Venerin ljubavnik, i bog rata. Prema njemu je nazvana planet Mars. Po njoj je nazvan prvi otkriveni asteroid 1 Ceres.

Merkur je rimski bog trgovine, putovanja, te je glasnik bogova. Bio je Venerin ljubavnik. Zbog takve uloge, bilo ih je nevjerojatno mnogo.

Izvor: Wikipedija. Kategorija : Rimska mitologija. Imenski prostori Stranica Razgovor. Liber i Libera. Karmenta Eduka Potina.

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