Big
data -
SPACE
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.
Big
data 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, search, sharing, transfer,
analysis, 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."
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.
Scientists
regularly encounter limitations due to large data sets in many areas, including
meteorology, genomics, connectomics,
complex physics simulations, and biological and environmental research. 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.
The
world's technological per-capita capacity to store information has roughly
doubled every 40 months since the 1980s; as of 2012, every day 2.5 quintillion (2.5×1018) bytes of data were created.
The
challenge for large enterprises is determining who should own big data
initiatives that straddle the entire organization.
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".
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."
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. 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. 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 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.
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."
Additionally,
a new V "Veracity" is added by some organizations to describe it.
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 whose huge volume
allow to infer laws and thus giving to
Big Data some predictive capabilities.
Examples
Examples include Big Science, RFID, sensor networks, social networks,
big social data analysis (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.
Big science
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.
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.
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.
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.
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. 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’. 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.
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. The initiative was
composed of 84 different big data programs spread across six departments.
·The
United States Federal Government
owns six of the ten most powerful supercomputers in the world.
·The
Utah
Data Center is a data center currently being constructed by the United States National Security Agency.
·When
finished, the facility will be able to handle yottabytes of
information collected by the NSA over the Internet.
Private
sector
·Amazon.com
handles millions of back-end operations every day, as well as queries from more
than half a million third-party sellers. The core technology that keeps Amazon
running is Linux-based and as of 2005 they had the world’s three largest Linux
databases, with capacities of 7.8 TB, 18.5 TB, and 24.7 TB.
·Walmart handles
more than 1 million customer transactions every hour, which is imported into
databases estimated to contain more than 2.5 petabytes (2560 terabytes) of
data – the equivalent of 167 times the information contained in all the
books in the US Library of Congress.
·Facebook
handles 50 billion photos from its user base.
·The
volume of business data worldwide, across all companies, doubles every 1.2
years, according to estimates.
·Windermere Real Estate uses anonymous GPS signals
from nearly 100 million drivers to help new home buyers determine their typical
drive times to and from work throughout various times of the day.
International
development
Following decades of work in the
area of the effective usage of information and communication
technologies for development (or ICT4D), it has been suggested that Big
Data can make important contributions to international development.
On the one hand, the advent of Big Data delivers the cost-effective prospect to
improve decision-making in critical development areas such as health care, employment, economic productivity, crime and security, and natural disaster
and resource management. On
the other hand, all the well-known concerns of the Big Data debate, such as privacy,
interoperability challenges, and the almighty power of imperfect algorithms,
are aggravated in developing countries by long-standing development challenges
like lacking technological infrastructure and economic and human resource
scarcity. "This has the potential to result in a new kind of digital divide: a
divide in data-based intelligence to inform decision-making."
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. In 2010, this industry on its own was worth more than
$100 billion and was growing at almost 10 percent a year: about twice as
fast as the software business as a whole.
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. 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.
and it is predicted that the amount of traffic flowing over
the internet will reach
Architecture
In 2004, Google published a paper on
a process called MapReduce
that used such an architecture. MapReduce framework provides a parallel
programming model and associated implementation to process huge amount of data.
With MapReduce, queries are split and distributed across parallel nodes and
processed in parallel (the Map step). The results are then gathered and
delivered (the Reduce step). The framework was incredibly successful, so others
wanted to replicate the algorithm. Therefore, an implementation of MapReduce
framework was adopted by an Apache open source project named Hadoop.
MIKE2.0 is
an open approach to information management. The methodology addresses handling
big data in terms of useful permutations of
data sources, complexity in
interrelationships, and difficulty in deleting (or modifying) individual
records.
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[54]
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, such as multilinear subspace learning.[56]
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.]
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.
DARPA’s Topological Data Analysis
program seeks the fundamental structure of massive data sets and in 2008 the
technology went public with the launch of a company called Ayasdi.
The practitioners of big data
analytics processes are generally hostile to slower shared storage, 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.
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.
The initiative included a National
Science Foundation "Expeditions in Computing" grant of $10 million
over 5 years to the AMPLab at the University of California, Berkeley. 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] to
fighting cancer.
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, 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. 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.
The European Commission is funding a
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 2020,
their next framework program.
The IBM sponsored 37th annual
"Battle of the Brains" student Big Data championship will be held in
July 2013. The inaugural professional 2014 Big Data World Championship is to be
held in Dallas, Texas.
Critique
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.
Critiques
of the Big Data paradigm
"A crucial problem is that we
do not know much about the underlying empirical micro-processes that lead to
the emergence of the[se] typical network characteristics of Big Data". In
their critique, Snijders, Matzat, and Reips
point out that often very strong assumptions are made about mathematical
properties that may not at all reflect what is really going on at the level of
micro-processes. 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
will always need to be contextualized in their social, economic and political
contexts. 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.
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". Fed by a large number of data on past experiences, algorithms can
predict future development if the future is similar to the past. If the systems
dynamics of the future change, the past can say little about the future. For
this, it would be necessary to have a thorough understanding of the systems
dynamic, which implies theory. As a response to this critique it has been
suggested to combine Big Data approaches with computer simulations, such as agent-based models,
for example. Those 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
addition, use of multivariate methods that probe for the latent structure of
the data, such as factor
analysis and cluster analysis,
have proven useful as analytic approaches that go well beyond the bi-variate
approaches (cross-tabs) typically employed with smaller data sets.
In Health and biology, conventional
scientific approaches are based on experimentation. For these approaches, the
limiting factor are the relevant data that can confirm or refute the initial
hypothesis. A new postulate is accepted now in biosciences : the
information provided by the data in huge volumes (omics) without prior hypothesis is
complementary and sometimes necessary to conventional approaches based on
experimentation. In the massive approaches it is the formulation of a relevant
hypothesis to explain the data that is the limiting factor. The search logic is
reversed and the limits of induction ("Glory of Science and Philosophy
scandal", C. D.
Broad, 1926) to be considered.
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.
Critiques
of Big Data execution
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. This approach may lead to results bias 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.
