Tuesday, 22 January 2013

Big Data

Big Data could have implications for economics in the future, and hence, its inclusion here o n this blog. It has special relevance to the possible future development of Transfinancial Economics.
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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.
In information technology, big data[1][2] is 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, 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]
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][9] Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genomics,[10] connectomics, complex physics simulations,[11] and biological and environmental research.[12] 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.[13][14] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[15] as of 2012, every day 2.5 quintillion (2.5×1018) bytes of data were created.[16] The challenge for Large enterprises is who should own big data initiatives that straddle the entire organization [17]

Big data is difficult to work with using relational databases and desktop statistics and visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of servers".[18] 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."[19]


[edit] Definition

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. 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. With this difficulty, a new platform of "big data" tools has arisen to handle sensemaking over large quantities of data, as in the Apache Hadoop Big Data Platform.
MIKE2.0, an open approach to Information Management, defines big data in terms of useful permutations of data sources, complexity in interrelationships, and difficulty to delete (or modify) individual records.
In a 2001 research report[20] 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.[21] In 2012, Gartner updated its definition as follows: "Big data are 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."[22]

[edit] Examples

Examples include Big Science, web logs, RFID, sensor networks, social networks, social data (due to the social data revolution), Internet text and documents, Internet search indexing, call detail records, astronomy, atmospheric science, genomics, biogeochemical, biological, and other complex and often interdisciplinary scientific research, military surveillance, medical records, photography archives, video archives, and large-scale e-commerce.

[edit] Big Science

The Large Hadron Collider (LHC) experiments represent about 150 million sensors delivering data 40 million times per second. There are nearly 600 million collisions per second. After filtering and not recording more than 99.999% of these streams, there are 100 collisions of interest per second. [23] [24] [25]
  • 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 high 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.

[edit] Science and research

  • 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 one week.[5]
  • 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.[26][27][28] 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’.[29] 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.

[edit] Government

  • In 2012, the Obama administration announced the Big Data Research and Development Initiative, which explored how big data could be used to address important problems facing the government.[30] The initiative was composed of 84 different big data programs spread across six departments.[31]
  • The United States Federal Government owns six of the ten most powerful supercomputers in the world.[32]
  • The NASA Center for Climate Simulation (NCCS) stores 32 petabytes of climate observations and simulations on the Discover supercomputing cluster. [33]

[edit] Private sector

  • Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data – the equivalent of 167 times the information contained in all the books in the US Library of Congress.[5]
  • Facebook handles 40 billion photos from its user base.
  • FICO Falcon Credit Card Fraud Detection System protects 2.1 billion active accounts world-wide.[34]
  • The volume of business data worldwide, across all companies, doubles every 1.2 years, according to estimates.[35]

[edit] Market

"Big data" has increased the demand of information management specialists in that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, and HP have spent more than $15 billion on software firms only specializing in data management and analytics. This industry on its own is worth more than $100 billion and growing at almost 10 percent a year, about twice as fast as the software business as a whole.[5]
Developed economies make increasing use of data-intensive technologies. There are 4.6 billion mobile-phone subscriptions worldwide and there are between 1 billion and 2 billion people accessing the internet.[5] Between 1990 and 2005, more than 1 billion people worldwide entered the middle class which means more and more people who gain money will become more literate which in turn leads to information growth. The world's effective capacity to exchange information through telecommunication networks was 281 petabytes in 1986, 471 petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 2007[15] and it is predicted that the amount of traffic flowing over the internet will reach 667 exabytes annually by 2013.[5]

[edit] Technologies

DARPA’s Topological Data Analysis program seeks the fundamental structure of massive data sets.
Big data requires exceptional technologies to efficiently process large quantities of data within tolerable elapsed times. A 2011 McKinsey report[36] suggests suitable technologies include A/B testing, association rule learning, classification, cluster analysis, crowdsourcing, data fusion and integration, ensemble learning, genetic algorithms, machine learning, natural language processing, neural networks, pattern recognition, anomaly detection, predictive modelling, regression, sentiment analysis, signal processing, supervised and unsupervised learning, simulation, time series analysis and visualisation. Multidimensional big data can also be represented as tensors, which can be more efficiently handled by tensor-based computation,[37] such as multilinear subspace learning[38]. Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data-mining grids, distributed file systems, distributed databases, cloud based infrastructure (applications, storage and computing resources) and the Internet.[citation needed]
Some but not all MPP relational databases have the ability to store and manage petabytes of data. Implicit is the ability to load, monitor, back up, and optimize the use of the large data tables in the RDBMS.[39]
The practitioners of big data analytics processes are generally hostile to slower shared storage[citation needed], preferring direct-attached storage (DAS) in its various forms from solid state disk (SSD) to high capacity SATA disk buried inside parallel processing nodes. The perception of shared storage architectures—SAN and 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 memory is good—data on spinning 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 2011 did not favour it.[40]

[edit] Research activities

In March 2012, The White House announced a national "Big Data Initiative" that consisted of six Federal departments and agencies committing more than $200 million to big data research projects.[41]
The initiative included a National Science Foundation "Expeditions in Computing" grant of $10 million over 5 years to the AMPLab[42] at the University of California, Berkeley.[43] The AMPLab also received funds from DARPA, and over a dozen industrial sponsors and uses big data to attack a wide range of problems from predicting traffic congestion[44] to fighting cancer.[45]
The White House Big Data Initiative also included a commitment by the Department of Energy to provide $25 million in funding over 5 years to establish the Scalable Data Management, Analysis and Visualization (SDAV) Institute,[46] led by the Energy Department’s Lawrence Berkeley National Laboratory. 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.S. state of Massachusetts announced the Massachusetts Big Data Initiative in May 2012, which provides funding from the state government and private companies to a variety of research institutions.[47] The Massachusetts Institute of Technology hosts the Intel Science and Technology Center for Big Data in the MIT Computer Science and Artificial Intelligence Laboratory, combining government, corporate, and institutional funding and research efforts.[48]

[edit] Critique

Danah Boyd has raised concerns about the use of big data in science neglecting principles such as choosing a representative sample by being too concerned about actually handling the huge amounts of data.[49] This approach may lead to results biased in one way or another. 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.[50] Broader critiques have also been levelled at Chris Anderson's assertion that big data will spell the end of theory: focusing in particular on the notion that big data will always need to be contextualized in their social, economic and political contexts.[51] Even as companies invest eight- and nine-figure sums to derive insight from information streaming in from suppliers and customers, less than 40% of employees have sufficiently mature processes and skills to do so. To overcome this insight deficit, "big data", no matter how comprehensive or well analyzed, needs to be complemented by "big judgment", according to an article in the Harvard Business Review.[52]
Consumer 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.[53]

[edit] See also

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[edit] References

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[edit] Further reading

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