441. Big Data - 3, 10 Emerging Technologies

Big Data   10 Emerging Technologies

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By Thoran Rodrigues

December 5, 2012, 6:00 AM PST

Takeaway: Thoran Rodrigues interviewed Dr. Satwant Kaur about the 10 emerging technologies that will drive Big Data forward.

I’ve recently had the opportunity to have a conversation with Dr. Satwant Kaur on the topic of Big Data (see my previous interview with Dr. Kaur, “The 10 traits of the smart cloud“). Dr. Kaur has an extensive history in IT, being the author of Intel’s Transitioning Embedded Systems to Intelligent Environments. Her professional background, which includes four patents while at Intel & CA, 20 distinguished awards, ten keynote conference speeches at IEEE, and over 50 papers and publications, has earned her the nickname, “The First Lady of Emerging Technologies.” Dr. Kaur will be delivering the keynote at the CES show: 2013 IEEE International Conference on Consumer Electronics (ICCE).

While the topic of Big Data is broad and encompasses many trends and new technology developments, she managed to give me a very good overview of what she considers to be the top ten emerging technologies that are helping users cope with and handle Big Data in a cost-effective manner.

Dr. Kaur:

Column-oriented databases

Traditional, row-oriented databases are excellent for online transaction processing with high update speeds, but they fall short on query performance as the data volumes grow and as data becomes more unstructured. Column-oriented databases store data with a focus on columns, instead of rows, allowing for huge data compression and very fast query times. The downside to these databases is that they will generally only allow batch updates, having a much slower update time than traditional models.

Schema-less databases, or NoSQL databases

There are several database types that fit into this category, such as key-value stores and document stores, which focus on the storage and retrieval of large volumes of unstructured, semi-structured, or even structured data. They achieve performance gains by doing away with some (or all) of the restrictions traditionally associated with conventional databases, such as read-write consistency, in exchange for scalability and distributed processing.

MapReduce

This is a programming paradigm that allows for massive job execution scalability against thousands of servers or clusters of servers. Any MapReduce implementation consists of two tasks:

·The “Map” task, where an input dataset is converted into a different set of key/value pairs, or tuples;

·The “Reduce” task, where several of the outputs of the “Map” task are combined to form a reduced set of tuples (hence the name).

Hadoop

Hadoop is by far the most popular implementation of MapReduce, being an entirely open source platform for handling Big Data. It is flexible enough to be able to work with multiple data sources, either aggregating multiple sources of data in order to do large scale processing, or even reading data from a database in order to run processor-intensive machine learning jobs. It has several different applications, but one of the top use cases is for large volumes of constantly changing data, such as location-based data from weather or traffic sensors, web-based or social media data, or machine-to-machine transactional data.

Hive

Hive is a “SQL-like” bridge that allows conventional BI applications to run queries against a Hadoop cluster. It was developed originally by Facebook, but has been made open source for some time now, and it’s a higher-level abstraction of the Hadoop framework that allows anyone to make queries against data stored in a Hadoop cluster just as if they were manipulating a conventional data store. It amplifies the reach of Hadoop, making it more familiar for BI users.

PIG

PIG is another bridge that tries to bring Hadoop closer to the realities of developers and business users, similar to Hive. Unlike Hive, however, PIG consists of a “Perl-like” language that allows for query execution over data stored on a Hadoop cluster, instead of a “SQL-like” language. PIG was developed by Yahoo!, and, just like Hive, has also been made fully open source.

WibiData

WibiData is a combination of web analytics with Hadoop, being built on top of HBase, which is itself a database layer on top of Hadoop. It allows web sites to better explore and work with their user data, enabling real-time responses to user behavior, such as serving personalized content, recommendations and decisions.

PLATFORA

Perhaps the greatest limitation of Hadoop is that it is a very low-level implementation of MapReduce, requiring extensive developer knowledge to operate. Between preparing, testing and running jobs, a full cycle can take hours, eliminating the interactivity that users enjoyed with conventional databases. PLATFORA is a platform that turns user’s queries into Hadoop jobs automatically, thus creating an abstraction layer that anyone can exploit to simplify and organize datasets stored in Hadoop.

Storage Technologies

As the data volumes grow, so does the need for efficient and effective storage techniques. The main evolutions in this space are related to data compression and storage virtualization.

SkyTree

SkyTree is a high-performance machine learning and data analytics platform focused specifically on handling Big Data. Machine learning, in turn, is an essential part of Big Data, since the massive data volumes make manual exploration, or even conventional automated exploration methods unfeasible or too expensive.

Big Data in the cloud

As we can see, from Dr. Kaur’s roundup above, most, if not all, of these technologies are closely associated with the cloud. Most cloud vendors are already offering hosted Hadoop clusters that can be scaled on demand according to their user’s needs. Also, many of the products and platforms mentioned are either entirely cloud-based or have cloud versions themselves.

Big Data and cloud computing go hand-in-hand. Cloud computing enables companies of all sizes to get more value from their data than ever before, by enabling blazing-fast analytics at a fraction of previous costs. This, in turn drives companies to acquire and store even more data, creating more need for processing power and driving a virtuous circle.

440. BIG DATA - 2, SPACE

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^[update], 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^[update], 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^[update] 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.

·Big data analysis played a large role in Barack Obama's successful 2012 re-election campaign.

·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.

·FICO Falcon Credit Card Fraud Detection System protects 2.1 billion active accounts world-wide.

·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.

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

and it is predicted that the amount of traffic flowing over the internet will reach

667 exabytes annually by 2013.

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^[update] 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.