Cute Cartoon Pictures Biography
Source(Google.com.pk)
sqrrl data, Inc. is a Boston-based enterprise software company which provides Apache Accumulo-based products. It contributes to Accumulo and related Apache projects. [1]Most of its founding staff worked at the National Security Agency; "because after you’ve created data-analysis tools for top-secret intelligence work, what’s the next logical life-step but to start a small company with a cute cartoon squirrel for a logo" A few on the team also helped develop the Apache Accumulo project. [2]Apache Accumulo is based on Google’s BigTable design and built atop Apache Hadoop. Other open-source projects incorporating some elements of BigTable include Cassandra and HBase. [3]The sqrrl technology is promised to provide a secure, scalable and easily adaptable way for companies to manage big data.In August 2012, sqrrl announced a $2 million seed round led by Atlas Venture and Matrix Partners.[4] [5] [6]Big data[1][2] is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage,[3] search, sharing, transfer, analysis,[4] and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to "spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions."[5][6][7]A visualization created by IBM of Wikipedia edits. At multiple terabytes in size, the text and images of Wikipedia are a classic example of big data.As of 2012, limits on the size of data sets that are feasible to process in a reasonable amount of time were on the order of exabytes of data.[8] Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genomics,[9] connectomics, complex physics simulations,[10] and biological and environmental research.[11] The limitations also affect Internet search, finance and business informatics. Data sets grow in size in part because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, radio-frequency identification readers, and wireless sensor networks.[12][13] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[14] as of 2012, every day 2.5 quintillion (2.5×1018) bytes of data were created.[15] The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.[16]Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of servers".[17] What is considered "big data" varies depending on the capabilities of the organization managing the set, and on the capabilities of the applications that are traditionally used to process and analyze the data set in its domain. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."[18]Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within a tolerable elapsed time.[19] Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. The target moves due to constant improvement in traditional DBMS technology as well as new databases like NoSQL and their ability to handle larger amounts of data.[20] With this difficulty, new platforms of "big data" tools are being developed to handle various aspects of large quantities of data.In a 2001 research report[21] and related lectures, META Group (now Gartner) analyst Doug Laney defined data growth challenges and opportunities as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources). Gartner, and now much of the industry, continue to use this "3Vs" model for describing big data.[22] In 2012, Gartner updated its definition as follows: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization."[23] Additionally, a new V "Veracity" is added by some organizations to describe it.[24]If Gartner’s definition (the 3Vs) is still widely used, the growing maturity of the concept fosters a more sound difference between Big Data and Business Intelligence, regarding data and their use:Business Intelligence uses descriptive statistics with data with high information density to measure things, detect trends etc.;Big Data uses inductive statistics with data with low information density [25] whose huge volume allow to infer laws (regressions…) and thus giving (with the limits of inference reasoning) to Big Data some predictive capabilities.[26]include Big Science, RFID, sensor networks, social networks, big social data analysis[27] (due to the social data revolution), Internet documents, Internet search indexing, call detail records, astronomy, atmospheric science, genomics, biogeochemical, biological, and other complex and often interdisciplinary scientific research, military surveillance, forecasting drive times for new home buyers, medical records, photography archives, video archives, and large-scale e-commerce.[28]The Large Hadron Collider experiments represent about 150 million sensors delivering data 40 million times per second. There are nearly 600 million collisions per second. After filtering and refraining from recording more than 99.999% of these streams, there are 100 collisions of interest per second.[29][30][31]As a result, only working with less than 0.001% of the sensor stream data, the data flow from all four LHC experiments represents 25 petabytes annual rate before replication (as of 2012). This becomes nearly 200 petabytes after replication. If all sensor data were to be recorded in LHC, the data flow would be extremely hard to work with. The data flow would exceed 150 million petabytes annual rate, or nearly 500 exabytes per day, before replication. To put the number in perspective, this is equivalent to 500 quintillion (5×1020) bytes per day, almost 200 times higher than all the other sources combined in the world.When the Sloan Digital Sky Survey (SDSS) began collecting astronomical data in 2000, it amassed more in its first few weeks than all data collected in the history of astronomy. Continuing at a rate of about 200 GB per night, SDSS has amassed more than 140 terabytes of information. When the Large Synoptic Survey Telescope, successor to SDSS, comes online in 2016 it is anticipated to acquire that amount of data every five days.[5]Decoding the human genome originally took 10 years to process, now it can be achieved in less than a week : the DNA sequencers have divided the sequencing cost by 10 000 in the last ten years, which is a factor 100 compared to Moore's Law.[32]Computational social science — Tobias Preis et al. used Google Trends data to demonstrate that Internet users from countries with a higher per capita gross domestic product (GDP) are more likely to search for information about the future than information about the past. The findings suggest there may be a link between online behaviour and real-world economic indicators.[33][34][35] The authors of the study examined Google queries logs made by Internet users in 45 different countries in 2010 and calculated the ratio of the volume of searches for the coming year (‘2011’) to the volume of searches for the previous year (‘2009’), which they call the ‘future orientation index’.[36] They compared the future orientation index to the per capita GDP of each country and found a strong tendency for countries in which Google users enquire more about the future to exhibit a higher GDP. 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.The NASA Center for Climate Simulation (NCCS) stores 32 petabytes of climate observations and simulations on the Discover supercomputing cluster.[37]
Tobias Preis and his colleagues Helen Susannah Moat and H. 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.[38] Their analysis of Google search volume for 98 terms of varying financial relevance, published in Scientific Reports,[39] suggests that increases in search volume for financially relevant search terms tend to precede large losses in financial markets
Source(Google.com.pk)
sqrrl data, Inc. is a Boston-based enterprise software company which provides Apache Accumulo-based products. It contributes to Accumulo and related Apache projects. [1]Most of its founding staff worked at the National Security Agency; "because after you’ve created data-analysis tools for top-secret intelligence work, what’s the next logical life-step but to start a small company with a cute cartoon squirrel for a logo" A few on the team also helped develop the Apache Accumulo project. [2]Apache Accumulo is based on Google’s BigTable design and built atop Apache Hadoop. Other open-source projects incorporating some elements of BigTable include Cassandra and HBase. [3]The sqrrl technology is promised to provide a secure, scalable and easily adaptable way for companies to manage big data.In August 2012, sqrrl announced a $2 million seed round led by Atlas Venture and Matrix Partners.[4] [5] [6]Big data[1][2] is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage,[3] search, sharing, transfer, analysis,[4] and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to "spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions."[5][6][7]A visualization created by IBM of Wikipedia edits. At multiple terabytes in size, the text and images of Wikipedia are a classic example of big data.As of 2012, limits on the size of data sets that are feasible to process in a reasonable amount of time were on the order of exabytes of data.[8] Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genomics,[9] connectomics, complex physics simulations,[10] and biological and environmental research.[11] The limitations also affect Internet search, finance and business informatics. Data sets grow in size in part because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, radio-frequency identification readers, and wireless sensor networks.[12][13] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[14] as of 2012, every day 2.5 quintillion (2.5×1018) bytes of data were created.[15] The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.[16]Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of servers".[17] What is considered "big data" varies depending on the capabilities of the organization managing the set, and on the capabilities of the applications that are traditionally used to process and analyze the data set in its domain. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."[18]Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within a tolerable elapsed time.[19] Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. The target moves due to constant improvement in traditional DBMS technology as well as new databases like NoSQL and their ability to handle larger amounts of data.[20] With this difficulty, new platforms of "big data" tools are being developed to handle various aspects of large quantities of data.In a 2001 research report[21] and related lectures, META Group (now Gartner) analyst Doug Laney defined data growth challenges and opportunities as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources). Gartner, and now much of the industry, continue to use this "3Vs" model for describing big data.[22] In 2012, Gartner updated its definition as follows: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization."[23] Additionally, a new V "Veracity" is added by some organizations to describe it.[24]If Gartner’s definition (the 3Vs) is still widely used, the growing maturity of the concept fosters a more sound difference between Big Data and Business Intelligence, regarding data and their use:Business Intelligence uses descriptive statistics with data with high information density to measure things, detect trends etc.;Big Data uses inductive statistics with data with low information density [25] whose huge volume allow to infer laws (regressions…) and thus giving (with the limits of inference reasoning) to Big Data some predictive capabilities.[26]include Big Science, RFID, sensor networks, social networks, big social data analysis[27] (due to the social data revolution), Internet documents, Internet search indexing, call detail records, astronomy, atmospheric science, genomics, biogeochemical, biological, and other complex and often interdisciplinary scientific research, military surveillance, forecasting drive times for new home buyers, medical records, photography archives, video archives, and large-scale e-commerce.[28]The Large Hadron Collider experiments represent about 150 million sensors delivering data 40 million times per second. There are nearly 600 million collisions per second. After filtering and refraining from recording more than 99.999% of these streams, there are 100 collisions of interest per second.[29][30][31]As a result, only working with less than 0.001% of the sensor stream data, the data flow from all four LHC experiments represents 25 petabytes annual rate before replication (as of 2012). This becomes nearly 200 petabytes after replication. If all sensor data were to be recorded in LHC, the data flow would be extremely hard to work with. The data flow would exceed 150 million petabytes annual rate, or nearly 500 exabytes per day, before replication. To put the number in perspective, this is equivalent to 500 quintillion (5×1020) bytes per day, almost 200 times higher than all the other sources combined in the world.When the Sloan Digital Sky Survey (SDSS) began collecting astronomical data in 2000, it amassed more in its first few weeks than all data collected in the history of astronomy. Continuing at a rate of about 200 GB per night, SDSS has amassed more than 140 terabytes of information. When the Large Synoptic Survey Telescope, successor to SDSS, comes online in 2016 it is anticipated to acquire that amount of data every five days.[5]Decoding the human genome originally took 10 years to process, now it can be achieved in less than a week : the DNA sequencers have divided the sequencing cost by 10 000 in the last ten years, which is a factor 100 compared to Moore's Law.[32]Computational social science — Tobias Preis et al. used Google Trends data to demonstrate that Internet users from countries with a higher per capita gross domestic product (GDP) are more likely to search for information about the future than information about the past. The findings suggest there may be a link between online behaviour and real-world economic indicators.[33][34][35] The authors of the study examined Google queries logs made by Internet users in 45 different countries in 2010 and calculated the ratio of the volume of searches for the coming year (‘2011’) to the volume of searches for the previous year (‘2009’), which they call the ‘future orientation index’.[36] They compared the future orientation index to the per capita GDP of each country and found a strong tendency for countries in which Google users enquire more about the future to exhibit a higher GDP. 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.The NASA Center for Climate Simulation (NCCS) stores 32 petabytes of climate observations and simulations on the Discover supercomputing cluster.[37]
Tobias Preis and his colleagues Helen Susannah Moat and H. 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.[38] Their analysis of Google search volume for 98 terms of varying financial relevance, published in Scientific Reports,[39] suggests that increases in search volume for financially relevant search terms tend to precede large losses in financial markets
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