Questões de Concurso

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.

Atenção: As questões de números 56 a 60 referem-se ao texto abaixo

Windows 7 gets the basics right. Here's what you need to know
about the new OS.
Harry McCracken, PC World
Monday, October 19, 2009 2:00 pM

    The Windows experience occurs mainly in its Taskbar - especially in the Start menu and System Tray. Vista gave the Start menu a welcome redesign; in Windows 7, the Taskbar and the System Tray get a thorough makeover.
    Windows 7's revamped Taskbar introduces several new features and gives users much more control over how it looks.
    The new Taskbar replaces the old small icons and text labels for running apps with larger, unlabeled icons. If you can keep the icons straight, the new design painlessly reduces Taskbar clutter. If you don't like it, you can shrink the icons and/or bring the labels back.
    In the past, you could get one-click access to programs by dragging their icons to the Quick Launch toolbar. Windows 7 eliminates Quick Launch and folds its capabilities into the Taskbar. Drag an app's icon from the Start menu or desktop to the Taskbar, and Windows will pin it there, so you can launch the program without rummaging around in the Start menu. You can also organize icons in the Taskbar by moving them to new positions.
    To indicate that a particular application on the Taskbar is running, Windows draws a subtle box around its icon - ...56... subtle, in fact, that figuring out [CONJUNCTION] the app is running can take a moment, especially if its icon sits between two icons for running apps.
    In Windows Vista, hovering the mouse pointer over an application's Taskbar icon produces a thumbnail window view known as a Live Preview. But when you have multiple windows open, you see only one preview at a time. Windows 7's version of this feature is slicker and more efficient: Hover the pointer on an icon, and thumbnails of the app's windows glide into position above the Taskbar, so you can quickly find the one you're looking for. (The process would be even simpler if the thumbnails were larger and easier to decipher.)
    Also new in Windows 7's Taskbar is a feature called Jump Lists. These menus resemble the context-sensitive ones you get when you right-click within various Windows applications, except that you don't have to be inside an app to use them. Internet Explorer 8's Jump List, for example, lets you open the browser and load a fresh tab, initiate an InPrivate stealth browsing session, or go directly to any of eight frequently visited Web pages. Non-Microsoft apps can offer Jump Lists, too, if their developers follow the guidelines for creating them.
    Other Windows 7 interface adjustments are minor, yet so sensible that you may wonder why Windows didn't include them all along. Shove a window into the left or right edge of the screen and it'll expand to fill half of your desktop. Nudge another into the opposite edge of the screen, and it'll expand to occupy the other half. That makes comparing two windows' contents easy. If you nudge a window into the top of the screen, it will maximize to occupy all of the display's real estate.

(Adapted from http://www.pcworld.com/article/172602/windows_7_review.html)