Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
Lately, the term “big data” tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analysis methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.”
Data sets grow rapidly – in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.The world’s technological per-capita capacity to store information has roughly doubled every month since.
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require “massively parallel software running on tens, hundreds, or even thousands of servers”.
Visualization created by IBM of daily Wikipedia edits. At multiple terabytes in size, the text and images of Wikipedia are an example of big data.
The term has been in use since the s, with some giving credit to John Mashey for coining or at least making it popular.
In a research report
- Volume: big data doesn’t sample; it just observes and tracks what happens
Velocity: big data is often available in real-time
- Variety: big data draws from text, images, audio, video; plus it completes missing pieces through data fusion
Machine learning: big data often don’t ask why and simply detects patterns
- Digital footprint: big data is often a cost-free byproduct of digital interaction
- The growing maturity of the concept more starkly delineates the difference between big data and Business Intelligence:
Business Intelligence uses descriptive statistics with data with high information density to measure things, detect trends, etc..
Big data uses inductive statistics and concepts from nonlinear system identification
Big data can be described by the following characteristics:
- Volume – The quantity of generated and stored data. The size of the data determines the value and potential insight- and whether it can actually be considered big data or not. With Big Data one often speaks of PetaBytes in stead Terabytes, Gigabytes or Megabytes.
- Variety – The type and nature of the data. This helps people who analyze it to effectively use the resulting insight.
- Velocity. In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development.
- Variability. The inconsistency of the data set can hamper processes to handle and manage it.
- Veracity. The quality of captured data can vary greatly, affecting accurate analysis.
- Factory work and Cyber-physical systems may have a C system:
- Connection (sensor and network
- Cloud (computing and data on demand)
- Cyber (model and memory)
- Content/context (meaning and correlation)
- Community (sharing and collaboration)
- Customization (personalization and value
Data must be processed with advanced tools (analytics and algorithms) to reveal meaningful information. For example, to manage a factory one must consider both visible and invisible issues with various components. Information generation algorithms must detect and address invisible issues such as machine degradation, component wear, etc. on the factory floor.
Big data repositories have existed in many forms, often built by corporations with a special need. Commercial vendors historically offered parallel database management systems for big data beginning in the s. For many years, WinterCorp published the largest database report.
Teradata Corporation in marketed the parallel processing DBC system. Teradata systems were the first to store and analyze terabytes of data in. Hard disk drives were.GB in so the definition of big data continuously evolves according to Kryder’s Law. Teradata installed the first petabyte class RDBMS based system in. As of, there are a few dozen petabyte class Teradata relational databases installed, the largest of which exceeds PB. Systems up until were % structured relational data. Since then, Teradata has added unstructured data types including XML, JSON, and Avro.
In, Seisint Inc. (now LexisNexis Group) developed a C++-based distributed file-sharing framework for data storage and query. The system stores and distributes structured, semi-structured, and unstructured data across multiple servers. Users can build queries in a C++ dialect called ECL. ECL uses an “apply schema on reading” method to infer the structure of stored data when it is queried, instead of when it is stored. In, LexisNexis acquired Seisint Inc.
Google published a paper on a process called MapReduce that uses a similar architecture. The MapReduce concept provides a parallel processing model, and an associated implementation was released to process huge amounts 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 very successful,
MIKE. is an open approach to information management that acknowledges the need for revisions due to big data implications identified in an article titled “Big Data Solution Offering”.
studies showed that a multiple-layer architecture is one option to address the issues that big data presents. A distributed parallel architecture distributes data across multiple servers; these parallel execution environments can dramatically improve data processing speeds. This type of architecture inserts data into a parallel DBMS, which implements the use of MapReduce and Hadoop frameworks. This type of framework looks to make the processing power transparent to the end-user by using a front-end application server.
Big data analytics for manufacturing applications is marketed as a C architecture (connection, conversion, cyber, cognition, and configuration).
The data lake allows an organization to shift its focus from centralized control to a shared model to respond to the changing dynamics of information management. This enables quick segregation of data into the data lake, thereby reducing the overhead time.
A McKinsey Global Institute report characterizes the main components and ecosystem of big data as follows:
Techniques for analyzing data, such as A/B testing, machine learning, and natural language processing Big data technologies, like business intelligence, cloud computing, and databases.
Visualization, such as charts, graphs and other displays of the data
Multidimensional big data can also be represented as tensors, which can be more efficiently handled by tensor-based computation,
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 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 the solid-state drive (SSD) to high capacity SATA disk buried inside parallel processing nodes. 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 memory is good—data on spinning disk at the other end of an 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 did not favor it.
Bus wrapped with SAP Big data parked outside IDF. Big data has increased the demand of information management specialists so much so that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP, and Dell have spent more than $ billion on software firms specializing in data management and analytics. In, this industry was worth more than $ billion and was growing at almost percent a year: about twice as fast as the software business as a whole.
Developed economies increasingly use data-intensive technologies. There are. billion mobile-phone subscriptions worldwide, and between billion and billion people accessing the internet.which is the format most useful for most big data applications. This also shows the potential of yet unused data (i.e. in the form of video and audio content).
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 allow efficiencies in terms of cost, productivity, and innovation, 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. Below are some examples of initiatives in the governmental big data space.
United States of America
In, the Obama administration announced the Big Data Research and Development Initiative, to explore how big data could be used to address important problems faced by the government.
Big data analysis played a large role in Barack Obama’s successful re-election campaign.
The United States Federal Government owns six of the ten most powerful supercomputers in the world.
The Utah Data Center has been constructed by the United States National Security Agency. When finished, the facility will be able to handle a large amount of information collected by the NSA over the Internet. The exact amount of storage space is unknown, but more recent sources claim it will be on the order of a few exabytes.
Big data analysis was tried out for the BJP to win the Indian General Election.
The Indian government utilizes numerous techniques to ascertain how the Indian electorate is responding to government action, as well as ideas for policy augmentation.
Examples of uses of big data in public services:
Data on prescription drugs: by connecting origin, location and the time of each prescription, a research unit was able to exemplify the considerable delay between the release of any given drug, and a UK-wide adaptation of the National Institute for Health and Care Excellence guidelines. This suggests that new or most up-to-date drugs take some time to filter through to the general patient.
Joining up data: a local authority blended data about services, such as road gritting rotas, with services for people at risk, such as ‘meals on wheels’. The connection of data allowed the local authority to avoid any weather-related delay.
Research on the effective usage of information and communication technologies for development (also known as ICTD) suggests that big data technology can make important contributions but also present unique challenges to international development.
The use of big data in the form of historical financial market data is called technical analysis. The use of non-finance data for market prediction is sometimes called alternative data. See also surveillance capitalism.
Based on the TCS Global Trend Study, improvements in supply planning and product quality provide the greatest benefit of big data for manufacturing. Big data provides an infrastructure for transparency in the manufacturing industry, which is the ability to unravel uncertainties such as inconsistent component performance and availability. Predictive manufacturing as an applicable approach toward near-zero downtime and transparency requires a vast amount of data and advanced prediction tools for a systematic process of data into useful information.
Current PHM implementations mostly use data during the actual usage while analytical algorithms can perform more accurately when more information throughout the machine’s lifecycle, such as system configuration, physical knowledge, and working principles, are included. There is a need to systematically integrate, manage and analyze machinery or process data during different stages of the machine life cycle to handle data/information more efficiently and further achieve better transparency of machine health conditions for the manufacturing industry.
With such motivation, a cyber-physical (coupled) model scheme has been developed. The coupled model is a digital twin of the real machine that operates in the cloud platform and simulates the health condition with an integrated knowledge of both data-driven analytical algorithms as well as other available physical knowledge. It can also be described as a systematic approach consisting of sensing, storage, synchronization, synthesis, and service. The coupled model first constructs a digital image from the early design stage. System information and physical knowledge are logged during product design, based on which a simulation model is built as a reference for future analysis. Initial parameters may be statistically generalized and they can be tuned using data from testing or the manufacturing process using parameter estimation. After that step, the simulation model can be considered a mirrored image of the real machine—able to continuously record and track machine condition during the later utilization stage. Finally, with the increased connectivity offered by cloud computing technology, the coupled model also provides better accessibility of machine conditions for factory managers in cases where physical access to actual equipment or machine data is limited.
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. See also this article,
A McKinsey Global Institute study found a shortage of million highly trained data professionals and managers
To understand how the media utilizes big data, it is first necessary to provide some context into the mechanism used for the 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.
Targeting of consumers (for advertising by marketers)
Data journalism: publishers and journalists use big data tools to provide unique and innovative insights and infographics.
Channel, the British public-service television broadcaster, is a leader in the field of big data and data analysis.
Internet of Things (IoT)
Main article: Internet of Things. Big data and the IoT work in conjunction. Data extracted from IoT devices provides a mapping of device interconnectivity. 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 contexts.
Kevin Ashton, digital innovation expert who is credited with coining the term, defines the Internet of Things in this quote: “If we had computers that knew everything there was to know about things—using data they gathered without any help from us—we would be able to track and count everything, and greatly reduce waste, loss, and cost. We would know when things needed replacing, repairing or recalling, and whether they were fresh or past their best.”
eBay.com uses two data warehouses at . petabytes and PB as well as a PB Hadoop cluster for search, consumer recommendations, and merchandising.
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 they had the world’s three largest Linux databases, with capacities of. TB, . TB, and. TB.
Facebook handles a billion photos from its user base.
Google was handling roughly a billion searches per month as of August.
Oracle NoSQL Database has been tested to past the Mops/sec mark with shards and proceeded to hit .M ops/sec with shards.
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). The use of big data to resolve IT and data collection issues within an enterprise is called IT Operations Analytics (ITOA). In this time, ITOA businesses were also beginning to play a major role in systems management by offering platforms that brought individual data silos together and generated insights from the whole of the system rather than from isolated pockets of data.
Walmart handles more than a million customer transactions every hour, which are imported into databases estimated to contain more than. petabytes ( terabytes) of data—the equivalent of times the information contained in all the books in the US Library of Congress.
FICO Card Detection System protects accounts worldwide.
The volume of business data worldwide, across all companies, doubles every. years, according to estimates.
Windermere Real Estate uses location information from nearly a million drivers to help new home buyers determine their typical drive times to and from work throughout various times of the day.
The Large Hadron Collider experiments represent about million sensors delivering data million times per second. There are nearly a million collisions per second. After filtering and refraining from recording more than .%
As a result, only working with less than .% of the sensor stream data, the data flow from all four LHC experiments represents petabytes annual rate before replication (as of ). This becomes nearly petabytes after replication.
If all sensor data were recorded in LHC, the data flow would be extremely hard to work with. The data flow would exceed a million petabytes annual rate, or nearly exabytes per day, before replication. To put the number in perspective, this is equivalent to quintillion (×) bytes per day, almost times more than all the other sources combined in the world.
The Square Kilometre Array is a radio telescope built of thousands of antennas. It is expected to be operational. Collectively, these antennas are expected to gather exabytes and store one petabyte per day.
Science and research
When the Sloan Digital Sky Survey (SDSS) began to collect astronomical data in , it amassed more in its first few weeks than all data collected in the history of astronomy previously. Continuing at a rate of about GB per night, SDSS has amassed more than terabytes of information.
Decoding the human genome originally took years to process, now it can be achieved in less than a day. The DNA sequencers have divided the sequencing cost by, in the last ten years, which is times cheaper than the reduction in cost predicted by Moore’s Law.
The NASA Center for Climate Simulation (NCCS) stores petabytes of climate observations and simulations on the Discover supercomputing cluster.
Google’s DNAStack compiles and organizes DNA samples of genetic data from around the world to identify diseases and other medical defects. These fast and exact calculations eliminate any ‘friction points,’ or human errors that could be made by one of the numerous science and biology experts working with the DNA. DNAStack, a part of Google Genomics, allows scientists to use the vast sample of resources from Google’s search server to scale social experiments that would usually take years, instantly.
anime’s DNA database contains the genetic information of over, people worldwide.
Computational Fluid Dynamics (CFD) and hydrodynamic turbulence research generate massive datasets. The Johns Hopkins Turbulence Databases (JHTDB) contains over terabytes of spatiotemporal fields from Direct Numerical simulations of various turbulent flows. Such data have been difficult to share using traditional methods such as downloading flat simulation output files. The data within JHDTB can be accessed using “virtual sensors” with various access modes ranging from direct web-browser queries, access through Matlab, Python, Fortran and C programs executing on clients’ platforms, to cut out services to download raw data. The data have been used in over scientific publications.
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.
The movie Moneyball demonstrates how big data could be used to scout players and also identify undervalued players.
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
Google Research activities
Encrypted search and cluster formation in big data was demonstrated in March at the American Society of Engineering Education. Gautam Siwach engaged at Tackling the challenges of Big Data by MIT Computer Science and Artificial Intelligence Laboratory and Dr. 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 actual orientation of the term towards the presence of a different type of data in an encrypted form at the 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.
In March, The White House announced a national “Big Data Initiative” that consisted of six Federal departments and agencies committing more than $ million to big data research projects.
The initiative included a National Science Foundation “Expeditions in Computing” grant of $ million over the years to the AMPLab
The White House Big Data Initiative also included a commitment by the Department of Energy to provide $ million in funding over 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 , which provides funding from the state government and private companies to a variety of research institutions.
The European Commission is funding the -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.
To make manufacturing more competitive in the United States (and globe), there is a need to integrate more American ingenuity and innovation into manufacturing; Therefore, National Science Foundation has granted the Industry-University cooperative research center for Intelligent Maintenance Systems (IMS) at university of Cincinnati to focus on developing advanced predictive tools and techniques to be applicable in a big data environment. In May, IMS Center held an industry advisory board meeting focusing on big data where presenters from various industrial companies discussed their concerns, issues and future goals in a big data environment.
Computational social sciences – Anyone can use Application Programming Interfaces (APIs) provided by big data holders, such as Google and Twitter, to do research in the social and behavioral sciences. They compared the future orientation index to the per capita GDP of each country and found a strong tendency for countries where Google users inquire more about the future to have 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.
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.
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. More google research can be read here.
Sampling 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 a 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 utilize 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. One approach to this criticism is the field of Critical data studies.
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 technologies and data.
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 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 is the relevant data that can confirm or refute the initial hypothesis.
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 the 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.
Critiques of big data execution
Ulf-Dietrich Reips and Uwe Matzat wrote in that big data had become a “fad” in scientific research.
Big data analysis is often shallow compared to the analysis of smaller data sets.
Big data is a buzzword and a “vague term”, with varying degrees of success. Forbes predicted, “If you believe in Big Data analytics, it’s time to begin planning for a Hillary Clinton presidency and all that entails.”