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Big Data (megadados o grandes dados em português) é a área do conhecimento que estuda como tratar, analisar e obter informações a partir de conjuntos de dados grandes demais para serem analisados por sistemas tradicionais. Ao longo das últimas décadas, a quantidade de dados gerados tem crescido de forma exponencial.O surgimento da Internet aumentou de forma abrupta a quantidade de dados produzidos, e a popularização da Internet das coisas fez sairmos da era do terabyte para o petabyte. Em 2015, entramos na era do zetabytes, e atualmente geramos mais de 2,5 quintilhões de bytes diariamente. O termo Big Data surgiu em 1997 e seu uso foi utilizado para nomear essa quantidade cada vez mais crescente e não estruturada de dados sendo gerados a cada segundo. Atualmente o big data é essencial nas relações econômicas e sociais e representou uma evolução nos sistemas de negócio e na ciência. As ferramentas de big data são de grande importância na definição de estratégias de marketing, aumentar a produtividade, reduzir custos e tomar decisões mais inteligentes. A essência do conceito está em gerar valor para negócios. No que tange a ciência, o surgimento do big data representou a criação de um novo paradigma (4° paradigma) sendo concebido um novo método de avançar as fronteiras do conhecimento, por meio de novas tecnologias para coletar, manipular, analisar e exibir dados, construindo valor agregado com as análises geradas. 빅 데이터(영어: big data)란 기존 데이터베이스 관리도구의 능력을 넘어서는 대량(수십 테라바이트)의 정형 또는 심지어 데이터베이스 형태가 아닌 비정형의 데이터 집합조차 포함한 데이터로부터 가치를 추출하고 결과를 분석하는 기술이다. 다양한 종류의 대규모 데이터에 대한 생성, 수집, 분석, 표현을 그 특징으로 하는 빅 데이터 기술의 발전은 다변화된 현대 사회를 더욱 정확하게 예측하여 효율적으로 작동케 하고 개인화된 현대 사회 구성원마다 맞춤형 정보를 제공, 관리, 분석 가능케 하며 과거에는 불가능했던 기술을 실현시키기도 한다. 이같이 빅 데이터는 정치, 사회, 경제, 문화, 과학 기술 등 전 영역에 걸쳐서 사회와 인류에게 가치 있는 정보를 제공할 수 있는 가능성을 제시하며 그 중요성이 부각되고 있다. 하지만 빅데이터의 문제점은 바로 사생활 침해와 보안 측면에 자리하고 있다. 빅데이터는 수많은 개인들의 수많은 정보의 집합이다. 그렇기에 빅데이터를 수집, 분석할 때에 개인들의 사적인 정보까지 수집하여 관리하는 빅브라더의 모습이 될 수도 있는 것이다. 그리고 그렇게 모은 데이터가 보안 문제로 유출된다면, 이 역시 거의 모든 사람들의 정보가 유출되는 것이기에 큰 문제가 될 수 있다. 세계 경제 포럼은 2012년 떠오르는 10대 기술 중 그 첫 번째를 빅 데이터 기술로 선정 했으며 대한민국 지식경제부 R&D 전략기획단은 IT 10대 핵심기술 가운데 하나로 빅 데이터를 선정 하기도 했다. Big data utgörs av digitalt lagrad information av sådan storlek (vanligen terabyte och petabyte), att det är svårt att bearbeta den med traditionella databasmetoder. Big data innefattar tekniker för very large databases (VLDB), datalager (data warehouse) och informationsutvinning (data mining). Termen big data fick sitt genomslag under 2009. Ingen svensk översättning har blivit etablerad, men stora datamängder har använts. Stora datamängder skapas bland annat inom meteorologi, bioinformatik, genomik, fysik, miljöforskning, handel, avancerade simuleringar, försvaret och vid kommunikationstjänster med många användare, som mobiltelefoni, webbtjänster som Youtube, Flickr, Twitter, Facebook och Google. I många fall skapas datamängderna kontinuerligt (i realtid) och måste även analyseras i realtid. Framväxten av dessa stora datamängder beror på möjligheten att samla in (bland annat via Internet och digitalkameror) och lagra information (på hårddiskar), och svårigheten att hantera dem beror på att den traditionella tekniken för databaser inte har utvecklats lika fort. Två slag av angreppssätt för big data har varit NoSQL-databaser (som programvaran MongoDB) och ramverket (som bland annat implementeras med programvaran Apache ). Los macrodatos,​ también llamados datos masivos, inteligencia de datos, datos a gran escala o big data (terminología en idioma inglés utilizada comúnmente) es un término que hace referencia a conjuntos de datos tan grandes y complejos como para que hagan falta aplicaciones informáticas no tradicionales de procesamiento de datos para tratarlos adecuadamente. Por ende, los procedimientos usados para encontrar patrones repetitivos dentro de esos datos son más sofisticados y requieren un software especializado. En textos científicos en español, con frecuencia se usa directamente el término en inglés big data, tal como aparece en el ensayo de Viktor Schönberger La revolución de los datos masivos.​​ El uso moderno del término "big data" tiende a referirse al análisis del comportamiento del usuario, extrayendo valor de los datos almacenados, y formulando predicciones a través de los patrones observados. La disciplina dedicada a los datos masivos se enmarca en el sector de las tecnologías de la información y la comunicación. Esta disciplina se ocupa de todas las actividades relacionadas con los sistemas que manipulan grandes conjuntos de datos. Las dificultades más habituales vinculadas a la gestión de estos grandes volúmenes de datos, se centran en la recolección y el almacenamiento de los mismos,​ en las búsquedas, las comparticiones, y los análisis,​ y en las visualizaciones y representaciones. La tendencia a manipular enormes volúmenes de datos, se debe en muchos casos a la necesidad de incluir dicha información, para la creación de informes estadísticos y modelos predictivos utilizados en diversas materias, como los análisis sobre negocios, sobre publicidad, sobre enfermedades infecciosas, sobre el espionaje y el seguimiento a la población, o sobre la lucha contra el crimen organizado.​ El límite superior de procesamiento ha ido creciendo a lo largo de los años.​ Se estima que el mundo almacenó unos 5 zettabytes en 2014. Si se pone esta información en libros, convirtiendo las imágenes y todo eso a su equivalente en letras, se podría hacer 4500 pilas de libros que lleguen hasta el sol.​ Los científicos con cierta regularidad encuentran límites en el análisis debido a la gran cantidad de datos en ciertas áreas, tales como la meteorología, la genómica,​ la (una aproximación al estudio del cerebro; en inglés:Connectomics; en francés: Conectomique), las complejas simulaciones de procesos físicos​ y las investigaciones relacionadas con los procesos biológicos y ambientales.​ Las limitaciones también afectan a los motores de búsqueda en internet, a los sistemas de finanzas y a la informática de negocios. Los data sets crecen en volumen debido en parte a la recolección masiva de información procedente de los sensores inalámbricos y los dispositivos móviles (por ejemplo las VANET), el constante crecimiento de los históricos de aplicaciones (por ejemplo de los registros), las cámaras (sistemas de teledetección), los micrófonos, los lectores de identificación por radiofrecuencia.​​ La capacidad tecnológica per cápita a nivel mundial para almacenar datos se dobla aproximadamente cada cuarenta meses desde los años 1980.​ Se estima que en 2012 cada día fueron creados cerca de 2.5 trillones de bytes de datos.​ Los sistemas de gestión de bases de datos relacionales y los paquetes de software utilizados para visualizar datos, a menudo tienen dificultades para manejar big data. Este trabajo puede requerir "un software masivamente paralelo que se ejecute en decenas, cientos o incluso miles de servidores"​. Lo que califica como "big data" varía según las capacidades de los usuarios y sus herramientas, y las capacidades de expansión hacen que big data sea un objetivo en movimiento. "Para algunas organizaciones, enfrentar cientos de gigabytes de datos por primera vez puede provocar la necesidad de reconsiderar las opciones de administración de datos. Para otros, puede tomar decenas o cientos de terabytes antes de que el tamaño de los datos se convierta en una consideración importante".​ In statistica e informatica, la locuzione inglese big data ("grandi [masse di] dati"), o in italiano megadati, indica genericamente una raccolta di dati informativi così estesa in termini di volume, velocità e varietà da richiedere tecnologie e metodi analitici specifici per l'estrazione di valore o conoscenza. Il termine è utilizzato in riferimento alla capacità (propria della scienza dei dati) di analizzare ovvero estrapolare e mettere in relazione un'enorme mole di dati eterogenei, strutturati e non strutturati (grazie a sofisticati metodi statistici e informatici di elaborazione), allo scopo di scoprire i legami tra fenomeni diversi (ad esempio correlazioni) e prevedere quelli futuri. Datu handiak edo datu masiboak (ingelesez: Big data) prozesatzeko oso multzo handia osatzen duten datuak dira, konplexutasun handikoak; ohiko informatika-sistementzat zaila izaten da horrelako datuak prozesatzea. Bere , tratamendu, eskuratze, partekatze eta babeste erronka handiak dira. Gehienetan, egiteko erabiltzen dituzte Interneteko bilakaeran, finantzetan, meteorologian, genetikan eta beste hainbat arlotan. 1980ko hamarkadatik aurrera, 40 hilabetero munduan informazioa pilatzeko gaitasuna bikoiztu egin da; 2012. urtean, egunero 2,5 exabyte (2,5×1018) datu sortzen zen. Datu masiboen bolumena etengabe hazten da. Termino hau 1990. hamarkadatik aurrera erabili da eta, batzuek, John Mashey zientzilariari eman diote hedatzearen ospea. 2012an bere tamaina hamabi terabyte eta hainbat petabyte artekoa zela balioztatu zen datu multzo bakar batean. metodologiak definizio hau ematen du Datu handientzat: " erlazionaturiko gaiak ikertzen ditu, permutazio erabilgarrien, konplexutasunen eta erregistro indibidualak ezabatzeko zailtasunen terminoetan". 2001ean, kongresu eta erlazionatutako aurkezpenetan oinarritzen zen ikerketa txosten batean, META Group (orain Gartner) enpresak datuen hazkuntza konstantea bolumena, abiadura eta aniztasuna ikertzeko aukera eta erronka bezala definitzen zuen. Gartner enpresak datu masiboak erreferentzia bezala erabiltzen jarraitzen du. Gainera, datu masiboen merkatuko hornitzaile handiek datu kantitate horien prozesatzeari buruzko eskaera kritikoenei erantzuteko irtenbideak garatzen dituzte, hala nola, MapR eta Cloudera. 2016ko definizio batek terminoa horrela definitzen du: “Datu handiek balioan eraldatzeko teknologia espezifiko eta metodo analitikoak beharrezkoak dituen bolumen, abiadura eta aniztasun handiagatik bereizitako informazio aktiboa adierazten dute”. Gainera, erakunde batzuek beste V bat gehitzen dute, alegia, deskribatzeko egiazkotasuna (gaztelaniaz Veracidad para describir), industriaren autoritate batzuek zalantzan jartzen duten errebisionismoa dena. 大数据(英語:Big data),又称为巨量资料,指的是在傳統數據處理應用軟件不足以處理的大或複雜的數據集的術語。 大數據也可以定義為来自各種來源的大量非結構化或結構化數據。從學術角度而言,大數據的出現促成廣泛主題的新穎研究。這也導致各種大數據統計方法的發展。大數據並沒有統計學的抽樣方法;它只是觀察和追踪發生的事情。因此,大數據通常包含的數據大小超出傳統軟件在可接受的時間內處理的能力。由於近期的技術進步,發布新數據的便捷性以及全球大多數政府對高透明度的要求,大數據分析在現代研究中越來越突出。 Mahadata, lebih dikenal dengan istilah bahasa Inggris big data, adalah istilah umum untuk segala himpunan data (data set) dalam jumlah yang sangat besar, rumit dan tak terstruktur sehingga menjadikannya sukar ditangani apabila hanya menggunakan perkakas manajemen basis data biasa atau aplikasi pemroses data tradisional belaka. Mahadata juga dapat diartikan sebagai pertumbuhan data dan informasi yang eksponensial dengan kecepatan dalam pertambahannya dan memiliki data yang bervariasi sehingga menyebabkan tantangan baru dalam pengolahan sejumlah data besar yang heterogen dan mengetahui bagaimana cara memahami semua data tersebut. Big data dapat diterapkan di semua aspek yang ada misalnya pada bidang bisnis, kesehatan, pariwisata, pemerintahan, kejahatan, dan lainnya. Dengan menggunakan tools untuk pengambilan ataupun pengolahan datanya misalnya dengan menggunakan software Gephi, Python, Netlytics, NiFi, dan Tableau. Dengan memahami bahwa big data itu penting, maka suatu organisasi akan dengan mudah mengolah dan menganalisis sekumpulan data atau suatu permasalahan yang sedang dihadapi baik dari internal maupun eskternal organisasinya. Organisasi tersebut dapat menghemat biaya, mengehamat waktu, dan menciptakan sebuah keputusan yang tepat. Le big data /ˌbɪɡ ˈdeɪtə/ (litt. « grosses données » en anglais), les mégadonnées ou les données massives, désigne des ensembles de données devenus si volumineux qu'ils dépassent l'intuition et les capacités humaines d'analyse et même celles des outils informatiques classiques de gestion de base de données ou de l'information. L’explosion quantitative (et souvent redondante) de la donnée numérique contraint à de nouvelles manières de voir et analyser le monde. De nouveaux ordres de grandeur concernent la capture, le stockage, la recherche, le partage, l'analyse et la visualisation des données. Les perspectives du traitement des big data sont énormes et en partie encore insoupçonnées[non neutre] ; on évoque souvent de nouvelles possibilités d'exploration de l'information diffusée par les médias, de connaissance et d'évaluation, d'analyse tendancielle et prospective (climatiques, environnementales ou encore sociopolitiques, etc.) et de gestion des risques (commerciaux, assuranciels, industriels, naturels) et de phénomènes religieux, culturels, politiques, mais aussi en termes de génomique ou métagénomique, pour la médecine (compréhension du fonctionnement du cerveau, épidémiologie, écoépidémiologie...), la météorologie et l'adaptation aux changements climatiques, la gestion de réseaux énergétiques complexes (via les smartgrids ou un futur « internet de l'énergie »), l'écologie (fonctionnement et dysfonctionnement des réseaux écologiques, des réseaux trophiques avec le GBIF par exemple), ou encore la sécurité et la lutte contre la criminalité. La multiplicité de ces applications laisse d'ailleurs déjà poindre un véritable écosystème économique impliquant, d'ores et déjà, les plus gros acteurs du secteur des technologies de l'information. Certains[Qui ?] supposent que le big data pourrait aider les entreprises à réduire leurs risques et faciliter la prise de décision, ou créer la différence grâce à l'analyse prédictive et une « expérience client » plus personnalisée et contextualisée. Divers experts, grandes institutions (comme le MIT aux États-Unis, le Collège de France en Europe), administrations et spécialistes sur le terrain des technologies ou des usages considèrent le phénomène big data comme l'un des grands défis informatiques de la décennie 2010-2020 et en ont fait une de leurs nouvelles priorités de recherche et développement, qui pourrait notamment conduire à l'Intelligence artificielle en étant exploré par des réseaux de neurones artificiels autoapprenants. Der aus dem englischen Sprachraum stammende Begriff Big Data [ˈbɪɡ ˈdeɪtə] (von englisch big ‚groß‘ und data ‚Daten‘, deutsch auch Massendaten) bezeichnet Datenmengen, welche beispielsweise zu groß, zu komplex, zu schnelllebig oder zu schwach strukturiert sind, um sie mit manuellen und herkömmlichen Methoden der Datenverarbeitung auszuwerten. „Big Data“ wird häufig als Sammelbegriff für digitale Technologien verwendet, die in technischer Hinsicht für eine neue Ära digitaler Kommunikation und Verarbeitung und in sozialer Hinsicht für einen gesellschaftlichen Umbruch verantwortlich gemacht werden. Dabei unterliegt der Begriff als Schlagwort einem kontinuierlichen Wandel; so wird damit ergänzend auch oft der Komplex der Technologien beschrieben, die zum Sammeln und Auswerten dieser Datenmengen verwendet werden. 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. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. When we handle big data, we may not sample but simply observe and track what happens. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value. 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. "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."Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on." 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. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research. Data sets grow rapidly, to a certain extent 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 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.5×260 bytes) of data are generated. Based on an IDC report prediction, the global data volume was predicted to grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts there will be 163 zettabytes of data. One question for large enterprises is determining who should own big-data initiatives that affect the entire organization. Relational database management systems, desktop statistics and software packages used to visualize data often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers". What qualifies as being "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "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." ビッグデータ (英: big data)とは、一般的なデータ管理・処理ソフトウエアで扱うことが困難なほど巨大で複雑なデータの集合を表す用語である。 ビッグデータを取り巻く課題の範囲は、情報の収集、取捨選択、保管、検索、共有、転送、解析、可視化等多岐にわたる。これら課題を克服しビッグデータの傾向をつかむことで「ビジネスに使える発見、疾病予防、犯罪防止、リアルタイムの道路交通状況判断」に繋がる可能性がある。 用語自体はデータマイニングで一般的に使われてきたが、2010年代に入ってある種のトレンドを示すキーワードとして、新聞・雑誌などでも広く取り上げられるようになってきた。 البيانات الضخمة مصطلح يشير إلى مجموعة بيانات تستعصي لضخامتها أو تعقيدها على التخزين أو المعالجة بإحدى الأدوات أو التطبيقات المعتادة لإدارة البيانات. أو ببساطة لتقريب الأفهام، لا يُمكن التعامل معها على حاسوب عادي بمفرده من خلال قاعدة بيانات بسيطة. ومن سمات مجال «البيانات الضخمة» استعمال حواسب عديدة لتقاسم الأعمال المطلوبة. Больши́е да́нные (англ. big data, [ˈbɪɡ ˈdeɪtə]) — обозначение структурированных и неструктурированных данных огромных объёмов и значительного многообразия, эффективно обрабатываемых горизонтально масштабируемыми программными инструментами, появившимися в конце 2000-х годов и альтернативных традиционным системам управления базами данных и решениям класса Business Intelligence. В широком смысле о «больших данных» говорят как о социально-экономическом феномене, связанном с появлением технологических возможностей анализировать огромные массивы данных, в некоторых проблемных областях — весь мировой объём данных, и вытекающих из этого трансформационных последствий. В качестве определяющих характеристик для больших данных традиционно выделяют «три V»: объём (англ. volume, в смысле величины физического объёма), скорость (velocity в смыслах как скорости прироста, так и необходимости высокоскоростной обработки и получения результатов), многообразие (variety, в смысле возможности одновременной обработки различных типов структурированных и полуструктурированных данных); в дальнейшем возникли различные вариации и интерпретации этого признака. С точки зрения информационных технологий в совокупность подходов и инструментов изначально включались средства массово-параллельной обработки неопределённо структурированных данных, прежде всего, системами управления базами данных категории NoSQL, алгоритмами MapReduce и реализующими их программными каркасами и библиотеками проекта Hadoop. В дальнейшем к серии технологий больших данных стали относить разнообразные информационно-технологические решения, в той или иной степени обеспечивающие сходные по характеристикам возможности по обработке сверхбольших массивов данных. Dades massives (o Big Data) és el nom que reben els conjunts de dades, els procediments i les aplicacions informàtiques, que, pel seu volum, la seva naturalesa diversa i la velocitat a què han de ser processades, ultrapassen la capacitat dels sistemes informàtics habituals. Aquest processament de dades massives s'utilitza per a detectar patrons dins seu, podent fer així prediccions vàlides per a la presa de decisions. La disciplina dedicada a les dades massives s'emmarca dins de les tecnologies de la informació i la comunicació. Aquesta disciplina s'ocupa de totes les activitats relacionades amb els sistemes que gestionen grans . Les dificultats més habituals en aquests casos se centren en la captura, l'emmagatzematge, la cerca, la compartició, l'anàlisi, i la seva visualització. La tendència de manipular ingents quantitats de dades es deu a la necessitat, en molts casos, d'incloure aquesta informació per a la creació d'informes estadístics i models predictius emprats en diversos camps, com per exemple de les anàlisis de negoci, publicitat, les dades de malalties infeccioses, l'espionatge i el seguiment de la població o la lluita contra el crim organitzat. El límit superior de la capacitat de processament s'ha anat desplaçant al llarg dels anys, d'aquesta forma els límits que estaven fixats el 2008 rondaven l'ordre de petabytes a zettabytes de dades. Els científics amb certa regularitat troben limitacions a causa de la gran quantitat de dades a analitzar en certes àrees, com ara la meteorologia, la genòmica, les complexes simulacions de processos físics, i les investigacions relacionades amb els processos biològics i ambientals. Les limitacions també afecten els motors de cerca a internet, als sistemes financers i a la informàtica de negocis. El volum del conjunt de dades creixen degut, en part, a la introducció d'informació ubiqua procedent dels sensors sense fils i els dispositius mòbils (per exemple les ), del constant creixement dels històrics d'interaccions d'aplicacions (per exemple processos de registre), càmeres digitals (sistemes de teledetecció), micròfons, lectors de ràdio -. La capacitat tecnològica per càpita a nivell mundial d'emmagatzemar dades es multiplica aproximadament per dos cada quaranta mesos des dels anys vuitanta. S'estima que durant el 2012, cada dia es van crear a prop de 2,5 trilions de bytes de dades (de l'anglès quintillion, 2.5 × 1018). Big data – termin odnoszący się do dużych, zmiennych i różnorodnych zbiorów danych, których przetwarzanie i analiza jest trudna, ale jednocześnie wartościowa, ponieważ może prowadzić do zdobycia nowej wiedzy. Pojęcie dużego zbioru danych jest względne i oznacza sytuację, gdy zbioru nie da się przetwarzać przy użyciu trywialnych, powszechnie dostępnych metod. W zależności od branży i stopnia złożoności algorytmu może to oznaczać rozmiar terabajtów lub petabajtów (np. analiza zderzeń cząstek elementarnych w fizyce wysokich energii), jednak w innych zastosowaniach będą to już megabajty bądź gigabajty (np. porównywanie telefonicznych w telekomunikacji). Big data ma zastosowanie wszędzie tam, gdzie dużej ilości danych cyfrowych towarzyszy potrzeba zdobywania nowych informacji lub wiedzy. Szczególne znaczenie odgrywa wzrost dostępności Internetu oraz usług świadczonych drogą elektroniczną, które w naturalny sposób są przystosowane do wykorzystywania baz danych. Wykorzystanie do analiz dużych zbiorów danych oznacza jednocześnie, że nie trzeba ograniczać się do mniejszych zbiorów określanych za pomocą różnych sposobów doboru próby, co eliminuje związane z tym błędy. Velká data (anglicky big data, česky někdy veledata) jsou podle jedné z možných definic soubory dat, jejichž velikost je mimo schopnosti zachycovat, spravovat a zpracovávat data běžně používanými softwarovýmiprostředky v rozumném čase. Často bývá v textech na dané téma používáno i v češtině přímo big data jako pojem označující technickou kategorii, tedy bez překladu.
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Datu handiak edo datu masiboak (ingelesez: Big data) prozesatzeko oso multzo handia osatzen duten datuak dira, konplexutasun handikoak; ohiko informatika-sistementzat zaila izaten da horrelako datuak prozesatzea. Bere , tratamendu, eskuratze, partekatze eta babeste erronka handiak dira. Gehienetan, egiteko erabiltzen dituzte Interneteko bilakaeran, finantzetan, meteorologian, genetikan eta beste hainbat arlotan. In statistica e informatica, la locuzione inglese big data ("grandi [masse di] dati"), o in italiano megadati, indica genericamente una raccolta di dati informativi così estesa in termini di volume, velocità e varietà da richiedere tecnologie e metodi analitici specifici per l'estrazione di valore o conoscenza. Il termine è utilizzato in riferimento alla capacità (propria della scienza dei dati) di analizzare ovvero estrapolare e mettere in relazione un'enorme mole di dati eterogenei, strutturati e non strutturati (grazie a sofisticati metodi statistici e informatici di elaborazione), allo scopo di scoprire i legami tra fenomeni diversi (ad esempio correlazioni) e prevedere quelli futuri. 빅 데이터(영어: big data)란 기존 데이터베이스 관리도구의 능력을 넘어서는 대량(수십 테라바이트)의 정형 또는 심지어 데이터베이스 형태가 아닌 비정형의 데이터 집합조차 포함한 데이터로부터 가치를 추출하고 결과를 분석하는 기술이다. 다양한 종류의 대규모 데이터에 대한 생성, 수집, 분석, 표현을 그 특징으로 하는 빅 데이터 기술의 발전은 다변화된 현대 사회를 더욱 정확하게 예측하여 효율적으로 작동케 하고 개인화된 현대 사회 구성원마다 맞춤형 정보를 제공, 관리, 분석 가능케 하며 과거에는 불가능했던 기술을 실현시키기도 한다. 이같이 빅 데이터는 정치, 사회, 경제, 문화, 과학 기술 등 전 영역에 걸쳐서 사회와 인류에게 가치 있는 정보를 제공할 수 있는 가능성을 제시하며 그 중요성이 부각되고 있다. 세계 경제 포럼은 2012년 떠오르는 10대 기술 중 그 첫 번째를 빅 데이터 기술로 선정 했으며 대한민국 지식경제부 R&D 전략기획단은 IT 10대 핵심기술 가운데 하나로 빅 데이터를 선정 하기도 했다. 大数据(英語:Big data),又称为巨量资料,指的是在傳統數據處理應用軟件不足以處理的大或複雜的數據集的術語。 大數據也可以定義為来自各種來源的大量非結構化或結構化數據。從學術角度而言,大數據的出現促成廣泛主題的新穎研究。這也導致各種大數據統計方法的發展。大數據並沒有統計學的抽樣方法;它只是觀察和追踪發生的事情。因此,大數據通常包含的數據大小超出傳統軟件在可接受的時間內處理的能力。由於近期的技術進步,發布新數據的便捷性以及全球大多數政府對高透明度的要求,大數據分析在現代研究中越來越突出。 Big data utgörs av digitalt lagrad information av sådan storlek (vanligen terabyte och petabyte), att det är svårt att bearbeta den med traditionella databasmetoder. Big data innefattar tekniker för very large databases (VLDB), datalager (data warehouse) och informationsutvinning (data mining). Termen big data fick sitt genomslag under 2009. Ingen svensk översättning har blivit etablerad, men stora datamängder har använts. Velká data (anglicky big data, česky někdy veledata) jsou podle jedné z možných definic soubory dat, jejichž velikost je mimo schopnosti zachycovat, spravovat a zpracovávat data běžně používanými softwarovýmiprostředky v rozumném čase. Často bývá v textech na dané téma používáno i v češtině přímo big data jako pojem označující technickou kategorii, tedy bez překladu. Le big data /ˌbɪɡ ˈdeɪtə/ (litt. « grosses données » en anglais), les mégadonnées ou les données massives, désigne des ensembles de données devenus si volumineux qu'ils dépassent l'intuition et les capacités humaines d'analyse et même celles des outils informatiques classiques de gestion de base de données ou de l'information. Certains[Qui ?] supposent que le big data pourrait aider les entreprises à réduire leurs risques et faciliter la prise de décision, ou créer la différence grâce à l'analyse prédictive et une « expérience client » plus personnalisée et contextualisée. Больши́е да́нные (англ. big data, [ˈbɪɡ ˈdeɪtə]) — обозначение структурированных и неструктурированных данных огромных объёмов и значительного многообразия, эффективно обрабатываемых горизонтально масштабируемыми программными инструментами, появившимися в конце 2000-х годов и альтернативных традиционным системам управления базами данных и решениям класса Business Intelligence. Los macrodatos,​ también llamados datos masivos, inteligencia de datos, datos a gran escala o big data (terminología en idioma inglés utilizada comúnmente) es un término que hace referencia a conjuntos de datos tan grandes y complejos como para que hagan falta aplicaciones informáticas no tradicionales de procesamiento de datos para tratarlos adecuadamente. Por ende, los procedimientos usados para encontrar patrones repetitivos dentro de esos datos son más sofisticados y requieren un software especializado. En textos científicos en español, con frecuencia se usa directamente el término en inglés big data, tal como aparece en el ensayo de Viktor Schönberger La revolución de los datos masivos.​​ Big Data (megadados o grandes dados em português) é a área do conhecimento que estuda como tratar, analisar e obter informações a partir de conjuntos de dados grandes demais para serem analisados por sistemas tradicionais. Ao longo das últimas décadas, a quantidade de dados gerados tem crescido de forma exponencial.O surgimento da Internet aumentou de forma abrupta a quantidade de dados produzidos, e a popularização da Internet das coisas fez sairmos da era do terabyte para o petabyte. Em 2015, entramos na era do zetabytes, e atualmente geramos mais de 2,5 quintilhões de bytes diariamente. O termo Big Data surgiu em 1997 e seu uso foi utilizado para nomear essa quantidade cada vez mais crescente e não estruturada de dados sendo gerados a cada segundo. Atualmente o big data é essencial nas Dades massives (o Big Data) és el nom que reben els conjunts de dades, els procediments i les aplicacions informàtiques, que, pel seu volum, la seva naturalesa diversa i la velocitat a què han de ser processades, ultrapassen la capacitat dels sistemes informàtics habituals. Aquest processament de dades massives s'utilitza per a detectar patrons dins seu, podent fer així prediccions vàlides per a la presa de decisions. 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. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. When we handle big data, we may not sample but simply observe and track what happens. Therefore, big data often includes data with sizes that excee Der aus dem englischen Sprachraum stammende Begriff Big Data [ˈbɪɡ ˈdeɪtə] (von englisch big ‚groß‘ und data ‚Daten‘, deutsch auch Massendaten) bezeichnet Datenmengen, welche beispielsweise zu groß, zu komplex, zu schnelllebig oder zu schwach strukturiert sind, um sie mit manuellen und herkömmlichen Methoden der Datenverarbeitung auszuwerten. ビッグデータ (英: big data)とは、一般的なデータ管理・処理ソフトウエアで扱うことが困難なほど巨大で複雑なデータの集合を表す用語である。 ビッグデータを取り巻く課題の範囲は、情報の収集、取捨選択、保管、検索、共有、転送、解析、可視化等多岐にわたる。これら課題を克服しビッグデータの傾向をつかむことで「ビジネスに使える発見、疾病予防、犯罪防止、リアルタイムの道路交通状況判断」に繋がる可能性がある。 用語自体はデータマイニングで一般的に使われてきたが、2010年代に入ってある種のトレンドを示すキーワードとして、新聞・雑誌などでも広く取り上げられるようになってきた。 Big data – termin odnoszący się do dużych, zmiennych i różnorodnych zbiorów danych, których przetwarzanie i analiza jest trudna, ale jednocześnie wartościowa, ponieważ może prowadzić do zdobycia nowej wiedzy. البيانات الضخمة مصطلح يشير إلى مجموعة بيانات تستعصي لضخامتها أو تعقيدها على التخزين أو المعالجة بإحدى الأدوات أو التطبيقات المعتادة لإدارة البيانات. أو ببساطة لتقريب الأفهام، لا يُمكن التعامل معها على حاسوب عادي بمفرده من خلال قاعدة بيانات بسيطة. ومن سمات مجال «البيانات الضخمة» استعمال حواسب عديدة لتقاسم الأعمال المطلوبة. Mahadata, lebih dikenal dengan istilah bahasa Inggris big data, adalah istilah umum untuk segala himpunan data (data set) dalam jumlah yang sangat besar, rumit dan tak terstruktur sehingga menjadikannya sukar ditangani apabila hanya menggunakan perkakas manajemen basis data biasa atau aplikasi pemroses data tradisional belaka. Mahadata juga dapat diartikan sebagai pertumbuhan data dan informasi yang eksponensial dengan kecepatan dalam pertambahannya dan memiliki data yang bervariasi sehingga menyebabkan tantangan baru dalam pengolahan sejumlah data besar yang heterogen dan mengetahui bagaimana cara memahami semua data tersebut.
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Velká data Macrodatos Big Data Big data Big data Dades massives Big data Большие данные ビッグデータ بيانات ضخمة Mahadata 빅 데이터 Big data Big data Big data Datu handiak Big data 大數據
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foaf:depiction
n13:png
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November 2018 November 2019 March 2018
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category:Data_management category:Big_data category:Data_analysis category:Databases category:Distributed_computing_problems category:Technology_forecasting category:Transaction_processing
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n103:Big_data
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dbpprop:reason
www.forbes.com/sites by contributors rather than staff are blogs, not reliable sources for facts. What is 'desktop statistics'?