Big data

big data analyticsbig data analysisbig-datadatalarge data setsBigData(big) databigBig data analyticalbig data applications
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with, by traditional data-processing application software.wikipedia
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Data visualization

visualizationData Presentation Architecturedata visualisation
Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.
Increased amounts of data created by Internet activity and an expanding number of sensors in the environment are referred to as "big data" or Internet of things.

E-Science

eSciencee-laboratoriesUK e-science core
Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research.
E-science encompasses "what is often referred to as big data [which] has revolutionized science... [such as] the Large Hadron Collider (LHC) at CERN... [that] generates around 780 terabytes per year... highly data intensive modern fields of science...that generate large amounts of E-science data include: computational biology, bioinformatics, genomics" and the human digital footprint for the social sciences.

Urban informatics

Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searches, fintech, urban informatics, and business informatics.
Since then, the emergence and growing popularity of ubiquitous computing, open data and big data analytics, as well as smart cities contributed to a surge in interest in urban informatics, not just from academics but also from industry and city governments seeking to explore and apply the possibilities and opportunities of urban informatics.

Data integration

Customer data integrationintegrationintegrate
Big data requires a set of techniques and technologies with new forms of integration to reveal insights from data-sets that are diverse, complex, and of a massive scale.
Data integration appears with increasing frequency as the volume (that is, big data) and the need to share existing data explodes.

User behavior analytics

browsing habitsmanipulating user behavioruser and entity behavior analytics
Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.
Big data platforms like Apache Hadoop are increasing UBA functionality by allowing them to analyze petabytes worth of data to detect insider threats and advanced persistent threats.

Small data

Compared to small data, big data are produced more continually.
The term "big data" is about machines and "small data" is about people.

Data curation

curatecurationcurated
Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.
In the modern era of big data, the curation of data has become more prominent, particularly for software processing high volume and complex data systems.

MapReduce

Map Reducemap-reducemap/reduce
In 2004, Google published a paper on a process called MapReduce that uses a similar architecture.
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster.

Predictive analytics

predictiveCARTpredictive analysis
Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.
Big data is a collection of data sets that are so large and complex that they become awkward to work with using traditional database management tools.

HPCC

HPCC SystemsHigh Performance Computing Clusterhigh-performance computing clusters
In 2000, Seisint Inc. (now LexisNexis Risk Solutions) developed a C++-based distributed platform for data processing and querying known as the HPCC Systems platform.
The HPCC platform incorporates a software architecture implemented on commodity computing clusters to provide high-performance, data-parallel processing for applications utilizing big data.

Financial technology

fintechFin-tech fintech
Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searches, fintech, urban informatics, and business informatics.
Artificial Intelligence (AI), Big Data, Robotic Process Automation (RPA), Blockchain.

Array DBMS

array databaseArray Database Systems
Array Database Systems have set out to provide storage and high-level query support on this data type.
Such arrays tend to be Big Data, with single objects frequently ranging into Terabyte and soon Petabyte sizes; for example, today’s earth and space observation archives typically grow by Terabytes a day.

Statistics

statisticalstatistical analysisstatistician
Relational database management systems, desktop statistics and software packages used to visualize data often have difficulty handling big data.
Statistics continues to be an area of active research for example on the problem of how to analyze Big data.

Ayasdi

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.
Ayasdi is a machine intelligence software company that offers a software platform and applications to organizations looking to analyze and build predictive models using big data or highly dimensional data sets.

LexisNexis Risk Solutions

ChoicePoint ChoicePoint CorporationChoicePoint, Inc.
In 2000, Seisint Inc. (now LexisNexis Risk Solutions) developed a C++-based distributed platform for data processing and querying known as the HPCC Systems platform.
LexisNexis Risk Solutions uses HPCC Systems, also known as DAS (Data Analytics Supercomputer), extensively —its software architecture runs from commodity computing clusters to provide high-performance, data-parallel processing for big data applications.

Information and communication technologies for development

ICT4DICT for DevelopmentInformation and communication technologies for development (ICT4D)
Research on the effective usage of information and communication technologies for development (also known as ICT4D) suggests that big data technology can make important contributions but also present unique challenges to International development.
As information and communication technologies evolve, so does ICT4D: more recently it has been suggested that big data can be used as an important ICT tool for development and that it represents a natural evolution of the ICT4D paradigm.

Business intelligence

BIBusiness Intelligence (BI)Business discovery
They aim to allow for the easy interpretation of these big data.

John Mashey

John R. Mashey
The term has been in use since the 1990s, with some giving credit to John Mashey for popularizing the term.
He has been credited for being the first to spread the term and concept of big data in the 1990s.

Data analysis

data analyticsanalysisdata analyst
Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.

Query language

querydatabase query languageData query language
Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.

The Data Incubator

Data Incubator
Private bootcamps have also developed programs to meet that demand, including free programs like The Data Incubator or paid programs like General Assembly.
It is best known for an 8-week educational fellowship preparing students with Master's degrees and PhDs for careers in big data and data science.

IT operations analytics

AIOpsartificial intelligence for operationsIT Operations Analytics (ITOA)
The use of big data to resolve IT and data collection issues within an enterprise is called IT operations analytics (ITOA).
ITOA may apply big data analytics to large datasets to produce business insights.

Data processing

processingdata-processingprocessing of data
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with, by traditional data-processing application software.

Social Credit System

Chinese Social Credit Systemnew social policysocial credit rating
The system is considered a form of mass surveillance which uses facial recognition system and big data analysis technology.

Artificial intelligence

AIA.I.artificially intelligent
By applying big data principles into the concepts of machine intelligence and deep computing, IT departments can predict potential issues and move to provide solutions before the problems even happen.
In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.