About: Data stream mining

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Data Stream Mining (also known as stream learning) is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data.Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.

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rdf:type	Election
rdfs:label	Data stream mining (en) Fouille de flots de données (fr) Data stream mining (pt)
rdfs:comment	La fouille de flots de données (« Data stream mining ») est le processus d'extraction des connaissances de flux de données continus (pas nécessairement ou uniquement dans le big data). Un flux/flot de données est une séquence ordonnée d'instances lisibles une seule fois — ou un nombre de fois très faible — dans un système limité en capacité mémoire et en capacité de stockage. Les flux sont continus, illimités, arrivent avec une grande rapidité, et ont une distribution qui change avec le temps. Le trafic réseau, les conversations téléphoniques, les transactions ATM, les recherches sur le web, et les données des capteurs sont des flux/flots de données. (fr) Data Stream Mining (also known as stream learning) is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data.Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery. (en) Data Stream Mining é o processo de extrair estruturas de conhecimento de registros de dados rápidos e contínuos. Uma data stream é uma sequência ordenada de instâncias que, em muitas aplicações de data stream mining, pode ser lida apenas uma vez ou poucas vezes, usando recursos limitados de computação e armazenamento. Exemplos de data streams incluem o computador de tráfego de rede, conversas por telefone, transações em ATM, pesquisas na web e dados de sensor.Data stream mining pode ser considerada um subcampo de data mining, machine learning, e descoberta de conhecimento. (pt)
dcterms:subject	Business intelligence Data mining
Wikipage page ID	1760301 (xsd:integer)
Wikipage revision ID	1115967416 (xsd:integer)
Link from a Wikipage to another Wikipage	Scikit-multiflow Berlin Hong Kong Incremental learning MOA (Massive Online Analysis) Online Machine Learning Germany Concept drift Machine learning Stream processing Data mining Data stream Weka (machine learning) Lambda architecture Data Mining Regression analysis Business intelligence Data mining Big data Data clustering Korea RapidMiner Seoul Wireless sensor network Statistical classification Knowledge discovery Streaming Algorithm Sequence mining dbr:Random_radius_based_functions dbr:Rotating_hyperplane
Link from a Wikipage to an external page	http://huawei-noah.github.io/streamDM/ http://ijcai13.org/ http://www.comp.hkbu.edu.hk/~wii06/icdm/ http://www.ecmlpkdd2006.org/ http://www.machine-learning.eu/iwkdds-2006/ http://www.acm.org/conferences/sac/sac2007/ http://www.liaad.up.pt/udm/ http://www.liaad.up.pt/~iwkduds/ https://web.archive.org/web/20070312173756/http:/www.niaad.liacc.up.pt/~jgama/SAC07/ https://web.archive.org/web/20120216115711/http:/www.liaad.up.pt/~iwkduds/ https://web.archive.org/web/20130223085808/http:/www.liaad.up.pt/udm/ https://mitpress.mit.edu/books/machine-learning-data-streams https://www.springer.com/us/book/9781441980199 https://www.springer.com/us/book/9783540736783 https://www.springer.com/us/book/9783642180866 http://ameblo.jp/kkonkatsu/ https://www.crcpress.com/Knowledge-Discovery-from-Data-Streams/Gama/p/book/9781439826119 https://www.crcpress.com/Knowledge-Discovery-from-Sensor-Data/Ganguly-Gama-Omitaomu-Gaber-Vatsavai/p/book/9781420082326 http://www.csse.monash.edu.au/~mgaber/iwmesd/ https://github.com/greenfish77/gaenari
sameAs	Data stream mining Data stream mining Data stream mining Data stream mining Data stream mining Data stream mining
dbp:wikiPageUsesTemplate	dbt:Cite_book dbt:Webarchive
date	2012-02-16 (xsd:date) 2013-02-23 (xsd:date)
url	https://web.archive.org/web/20120216115711/http:/www.liaad.up.pt/~iwkduds/ https://web.archive.org/web/20130223085808/http:/www.liaad.up.pt/udm/
has abstract	Data Stream Mining (also known as stream learning) is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream.Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion.Often, concepts from the field of incremental learning are applied to cope with structural changes, on-line learning and real-time demands. In many applications, especially operating within non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time. This problem is referred to as concept drift. Detecting concept drift is a central issue to data stream mining. Other challenges that arise when applying machine learning to streaming data include: partially and delayed labeled data, recovery from concept drifts, and temporal dependencies. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data.Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery. (en) La fouille de flots de données (« Data stream mining ») est le processus d'extraction des connaissances de flux de données continus (pas nécessairement ou uniquement dans le big data). Un flux/flot de données est une séquence ordonnée d'instances lisibles une seule fois — ou un nombre de fois très faible — dans un système limité en capacité mémoire et en capacité de stockage. Les flux sont continus, illimités, arrivent avec une grande rapidité, et ont une distribution qui change avec le temps. Le trafic réseau, les conversations téléphoniques, les transactions ATM, les recherches sur le web, et les données des capteurs sont des flux/flots de données. (fr) Data Stream Mining é o processo de extrair estruturas de conhecimento de registros de dados rápidos e contínuos. Uma data stream é uma sequência ordenada de instâncias que, em muitas aplicações de data stream mining, pode ser lida apenas uma vez ou poucas vezes, usando recursos limitados de computação e armazenamento. Em muitas aplicações de data stream mining, seu objetivo é prever a classe ou valor das novas instâncias da data stream dado um conhecimento sobre membros de classe e valores anteriores da data stream. As técnicas de machine learning podem ser utilizadas para a previsão de tarefas a partir de exemplos de rotulados de forma automatizada. Muitas vezes, os conceitos do campo da aprendizagem incrementais para lidar com alterações estruturais, aprendizado on-line e demandas em tempo real. Em muitas aplicações, especialmente de operação não estacionárias, uma distribuição subjacente pode ser usada como uma regra para a sua rotulagem, mudando ao longo do tempo. Este problema é conhecido como conceito de deriva. Exemplos de data streams incluem o computador de tráfego de rede, conversas por telefone, transações em ATM, pesquisas na web e dados de sensor.Data stream mining pode ser considerada um subcampo de data mining, machine learning, e descoberta de conhecimento. (pt)
gold:hypernym	Process
prov:wasDerivedFrom	wikipedia-en:Data_stream_mining?oldid=1115967416&ns=0
page length (characters) of wiki page	12859 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Data_stream_mining
is Link from a Wikipage to another Wikipage of	Scikit-multiflow Massive Online Analysis Glossary of artificial intelligence Concept drift Stream (computing) Stream processing Auroop Ratan Ganguly Hancock (programming language) Data mining Neuro-fuzzy Streaming algorithm Evolving classification function Symmetric hash join
is foaf:primaryTopic of	wikipedia-en:Data_stream_mining

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