Questões de Concurso

Atenção: As questões de números 53 a 56 referem-se ao texto abaixo.

    Managing government has never been more challenging. Public sector leaders face increasing expectations from citizens, businesses, elected officials, and employees. Pressed to provide services and information more quickly and cost effectively than [ADVERB], they are often saddled with legacy information systems that fail to leverage current technology or provide an enterprise-wide view of government.
    To meet these challenges, Oracle introduces Oracle iGovernment, a platform for innovative, integrated, and intelligent operations that lets you tear down silos and link frontand back-office operations while reducing costs and increasing responsiveness. With Oracle iGovernment you can modernize your IT infrastructure, increase efficiency and transparency, and improve service delivery.
    Oracle is the world's largest business software company, with more than 345,000 customers. And only Oracle delivers a complete platform of database, middleware, and applications—all based on open standards—to transform your organization.
    Oracle has reinforced and extended its public sector offerings with the acquisition of Sun, the leading provider of open-standards based Solaris and Linux platforms, architect of the award-winning SPARC processor family, and a key founding member of the Java community. Oracle's Sun solutions for the public sector provide a comprehensive set of capabilities for mainframe rehosting and migration and enterprise consolidation, enhancing our complete, open, and integrated solutions that support Oracle's iGovernment vision. Sun's advanced systems, storage, and services are designed to give you the value you expect, the quality you need, and the expertise you require to fully modernize your IT infrastructure.

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.

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.