Questões de Concurso

Atenção: As questões de números 1 a 4 referem-se ao texto abaixo.

    Após a década de 1950, as palavras que dominavam as sociedades de consumo ocidentais não eram mais as de escritores seculares, mas as marcas comerciais de produtos ou do que se podia comprar. As imagens que se tornaram ícones de tais sociedades eram as das diversões e consumo de massa: astros e latas. Não surpreende que na década de 1950, no coração da democracia de consumo, a principal escola de pintores abdicasse diante de fabricantes de imagens tão mais poderosas que a arte anacrônica. A arte pop passava o tempo reproduzindo, com tanta exatidão e insensibilidade quanto possível, os badulaques do comercialismo americano: latas de sopa, bandeiras, Marilyn Monroe.
    Insignificante como arte (no sentido que o século XIX deu à palavra), essa corrente, apesar disso, reconhecia que o triunfo do mercado de massa se baseava, de modo bastante profundo, na satisfação das necessidades tanto espirituais quanto materiais dos consumidores, fato do qual as agências de publicidade há muito tinham consciência quando destinavam suas campanhas a vender não o sabonete, mas o sonho de beleza, não as latas de sopa, mas a felicidade familiar. O que se tornou cada vez mais claro foi que isso tinha o que se podia chamar de uma dimensão estética, uma criatividade de base, ocasionalmente ativa mas sobretudo passiva, que os produtores tinham de competir para oferecer. Como dizia o populismo partilhado pelo mercado, o importante não era distinguir entre bom e ruim, elaborado e simples, mas no máximo entre o que atraía mais ou menos pessoas. Isso não deixava muito espaço para o clássico conceito das artes.

(Adaptado de Eric Hobsbawm. Era dos Extremos. Trad. Marcos
Santarrita. São Paulo, Cia. das Letras, 2006, p. 496)

Atenção: As questões de números 1 a 4 referem-se ao texto abaixo.

    Após a década de 1950, as palavras que dominavam as sociedades de consumo ocidentais não eram mais as de escritores seculares, mas as marcas comerciais de produtos ou do que se podia comprar. As imagens que se tornaram ícones de tais sociedades eram as das diversões e consumo de massa: astros e latas. Não surpreende que na década de 1950, no coração da democracia de consumo, a principal escola de pintores abdicasse diante de fabricantes de imagens tão mais poderosas que a arte anacrônica. A arte pop passava o tempo reproduzindo, com tanta exatidão e insensibilidade quanto possível, os badulaques do comercialismo americano: latas de sopa, bandeiras, Marilyn Monroe.
    Insignificante como arte (no sentido que o século XIX deu à palavra), essa corrente, apesar disso, reconhecia que o triunfo do mercado de massa se baseava, de modo bastante profundo, na satisfação das necessidades tanto espirituais quanto materiais dos consumidores, fato do qual as agências de publicidade há muito tinham consciência quando destinavam suas campanhas a vender não o sabonete, mas o sonho de beleza, não as latas de sopa, mas a felicidade familiar. O que se tornou cada vez mais claro foi que isso tinha o que se podia chamar de uma dimensão estética, uma criatividade de base, ocasionalmente ativa mas sobretudo passiva, que os produtores tinham de competir para oferecer. Como dizia o populismo partilhado pelo mercado, o importante não era distinguir entre bom e ruim, elaborado e simples, mas no máximo entre o que atraía mais ou menos pessoas. Isso não deixava muito espaço para o clássico conceito das artes.

(Adaptado de Eric Hobsbawm. Era dos Extremos. Trad. Marcos
Santarrita. São Paulo, Cia. das Letras, 2006, p. 496)

Atenção: As questões de números 53 a 60 referem-se ao texto abaixo.

Data mining
From Wikipedia, the free encyclopedia
Jump to: navigation, search
Not to be confused with information extraction.

    Data mining is the process of extracting patterns from data. Data mining is seen as an increasingly important tool by modern business to transform data into an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery.
    The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques on sample portions of the larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered (see also data-snooping bias). These techniques can, however, be used in the creation of new hypotheses to test against the larger data populations.

    [edit] Background

    The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks, clustering, genetic algorithms (1950s), decision trees (1960s) and support vector machines (1980s). Data mining is the process of applying these methods to data with the intention of uncovering hidden patterns. It has been used for many years by businesses, scientists and governments to sift through volumes of data such as airline passenger trip records, census data and supermarket scanner data to produce market research reports. (Note, however, that reporting is not always considered to be data mining.)
    A primary reason for using data mining is to assist in the analysis of collections of observations of behaviour. Such data are vulnerable to collinearity because of unknown interrelations. An unavoidable fact of data mining is that the (sub-)set(s) of data being analysed may not be representative of the whole domain, and [CONJUNCTION] may not contain examples of certain critical relationships and behaviours that exist across other parts of the domain. To address this sort of issue, the analysis may be augmented using experiment-based and other approaches, such as Choice Modelling for humangenerated data. In these situations, inherent correlations can be either controlled for, or removed altogether, during the construction of the experimental design.

Atenção: As questões de números 53 a 60 referem-se ao texto abaixo.

Data mining
From Wikipedia, the free encyclopedia
Jump to: navigation, search
Not to be confused with information extraction.

    Data mining is the process of extracting patterns from data. Data mining is seen as an increasingly important tool by modern business to transform data into an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery.
    The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques on sample portions of the larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered (see also data-snooping bias). These techniques can, however, be used in the creation of new hypotheses to test against the larger data populations.

    [edit] Background

    The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks, clustering, genetic algorithms (1950s), decision trees (1960s) and support vector machines (1980s). Data mining is the process of applying these methods to data with the intention of uncovering hidden patterns. It has been used for many years by businesses, scientists and governments to sift through volumes of data such as airline passenger trip records, census data and supermarket scanner data to produce market research reports. (Note, however, that reporting is not always considered to be data mining.)
    A primary reason for using data mining is to assist in the analysis of collections of observations of behaviour. Such data are vulnerable to collinearity because of unknown interrelations. An unavoidable fact of data mining is that the (sub-)set(s) of data being analysed may not be representative of the whole domain, and [CONJUNCTION] may not contain examples of certain critical relationships and behaviours that exist across other parts of the domain. To address this sort of issue, the analysis may be augmented using experiment-based and other approaches, such as Choice Modelling for humangenerated data. In these situations, inherent correlations can be either controlled for, or removed altogether, during the construction of the experimental design.

Atenção: As questões de números 53 a 60 referem-se ao texto abaixo.

Data mining
From Wikipedia, the free encyclopedia
Jump to: navigation, search
Not to be confused with information extraction.

    Data mining is the process of extracting patterns from data. Data mining is seen as an increasingly important tool by modern business to transform data into an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery.
    The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques on sample portions of the larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered (see also data-snooping bias). These techniques can, however, be used in the creation of new hypotheses to test against the larger data populations.

    [edit] Background

    The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks, clustering, genetic algorithms (1950s), decision trees (1960s) and support vector machines (1980s). Data mining is the process of applying these methods to data with the intention of uncovering hidden patterns. It has been used for many years by businesses, scientists and governments to sift through volumes of data such as airline passenger trip records, census data and supermarket scanner data to produce market research reports. (Note, however, that reporting is not always considered to be data mining.)
    A primary reason for using data mining is to assist in the analysis of collections of observations of behaviour. Such data are vulnerable to collinearity because of unknown interrelations. An unavoidable fact of data mining is that the (sub-)set(s) of data being analysed may not be representative of the whole domain, and [CONJUNCTION] may not contain examples of certain critical relationships and behaviours that exist across other parts of the domain. To address this sort of issue, the analysis may be augmented using experiment-based and other approaches, such as Choice Modelling for humangenerated data. In these situations, inherent correlations can be either controlled for, or removed altogether, during the construction of the experimental design.