The disk cleanup manager is part of the operating system. It displays the dialog box shown in the preceding illustration, handles user input, and manages the cleanup operation. The actual selection and cleanup of unneeded files is done by the individual disk cleanup handlers shown in the disk cleanup manager's list box. The user has the option of enabling or disabling individual handlers by selecting or clearing their check box in the disk cleanup manager's UI.
Each handler is responsible for a well defined set of files. For example, the selected handler in the illustration is responsible for cleaning up downloaded program files.
The handler selected in the illustration also provides a View Files button. By clicking the button, the user can request that the handler display a UI typically a Windows Explorer window that allows the user to specify which files or classes of files to clean. Although Windows comes with a number of disk cleanup handlers, they aren't designed to handle files produced by other applications. Instead, the disk cleanup manager is designed to be flexible and extensible by enabling any developer to implement and register their own disk cleanup handler.
Any developer can extend the available disk cleanup services by implementing and registering a disk cleanup handler. All applications that produce temporary files can and should implement and register a disk cleanup handler. Doing so gives users a convenient and reliable way to manage the application's temporary files.
When you implement the handler, you can decide which files are affected and determine how the actual cleanup happens. Windows provides an existing handler object called the DataDrivenCleaner for your use. You can also opt to implement a handler yourself for more flexibility. These objects then allow you to specify how to select files, free disk space, and, in the case of an implemented handler, display the optional UI for more granular control. This section addresses the matter of implementing your own handler.
The History of Data Cleaning
Unless your handler is intended for only one of these operating systems, it should export both interfaces. To export these interfaces, you must implement these methods corresponding to the five basic tasks. The two initialization methods, which are quite similar, are called when the Disk Cleanup utility is run. Initialize if IEmptyVolumeCache2 is not exposed by the handler. The disk cleanup manager passes information to the method, such as the handler's registry key and the disk volume that is to be cleaned.http://kuzminblog.com/wp-content/2019-09-10/best-mobile-tracking-app-for-android.php
Creating a Disk Cleanup Handler | Microsoft Docs
Either method can return various display strings and set one or more flags. The primary difference between the two methods is how the text displayed in the disk cleanup manager is handled. The following three strings are affected. The pdwFlags parameter found in both initialization methods recognizes the same set of flags. Two of these flags are passed to the method by the disk cleanup manager. Because there is no opportunity for user feedback, only those files that are extremely safe to clean up should be touched. You should ignore the initialization method's pcwszVolume parameter and clean unneeded files regardless of what drive they are on.
The handler should be aggressive about deleting files, even if it results in a performance loss. However, the handler obviously should not delete files that would cause an application to fail or the user to lose data. The remaining flags are set by the disk cleanup handler and returned to the disk cleanup manager. For more information, see the method reference pages for IEmptyVolumeCache:: Display the handler in the disk cleanup manager's list box only if the value returned by GetSpaceUsed indicates that the handler can free some disk space.
Specifies that the handler is enabled by default. It will run every time a disk cleanup takes place unless the user disables it by clearing its check box in the disk cleanup manager's list of handlers. Set this flag if your handler has a UI to display. In response, the disk cleanup manager displays a button when that handler is selected in the list box. If that button is clicked, the disk cleanup manager calls ShowProperties. Delete the handler's name from the list of available handlers after the handler has been run once. The handler's registry information is also deleted.
The disk cleanup manager calls this method to determine how much space a disk cleanup handler can potentially free. The disk cleanup manager then displays that value to the right of the handler's name in the list box. This operation is performed on all of the handlers registered with the disk cleanup manager when the manager is launched and before the manager's main UI is displayed.
When GetSpaceUsed is called, the handler should scan the files that it is responsible for, determine which of them are cleanup candidates, and return the amount of disk space that it can free. Because scanning can be a lengthy process, the disk cleanup manager uses this method's picb parameter to pass a pointer to an IEmptyVolumeCacheCallBack interface. ScanProgress , which serves two purposes. Before starting cleanup, the handler can display a UI typically in the form of a Windows Explorer window that allows the user to see a list of files or classes of files selected for cleanup by the handler.
The button text varies from handler to handler, but "View Files," "View Pages," and "Options" are common labels. When the button is clicked, the disk cleanup manager calls ShowProperties to prompt the handler to display the UI. The UI should be created as a child of the window whose handle is passed in the ShowProperties method's hwnd parameter.
The disk cleanup manager calls the handler's Purge method to set the cleanup in motion. As with the GetSpaceUsed method, the handler should use the callback interface periodically to report its progress and query the disk cleanup manager whether the user has clicked Cancel. PurgeProgress , not ScanProgress.
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The Deactivate method is called when the disk cleanup manager is preparing to shut down. The handler should perform any needed cleanup tasks and return. If this flag is set, the disk cleanup manager removes the handler from its list and deletes the handler's registry entries.
You must re-add the registry entries to run the handler again. This flag is typically used for handlers that are run only once. To add a handler to the disk cleanup manager's list, certain keys and values must be added to the Windows registry. You can also register an icon that is displayed next to the handler's name in the disk cleanup manager's list box, but this is optional.
The following example shows the keys, values, and data involved. To complete the registration, a handler must add a key holding its specifics as shown here. The remainder of this section discusses the contents of this key. In general, the name of the key holding a handler's particulars is named for the type of file that it handles, such as Downloaded Program Files , but this is not a requirement. The following table details the possible values found under this key.
Specifying display text in the registry can make it difficult to localize software. InitializeEx is exposed by the handler. Those values, containing properly localized text, are provided to the disk cleanup manager when it calls InitializeEx. A basic disk cleanup handler, called the DataDrivenCleaner, is provided by the operating system. When declaring those strings, be aware that this could cause localization issues. Localized text can be provided through the PropertyBag value. The AdvancedButtonText value is ignored since no UI, and thus no button to display it, is available for this handler.
The following shows an example registration for a disk cleanup handler implemented by The Phone Company. Only those files with. The feedback system for this content will be changing soon. Old comments will not be carried over. If content within a comment thread is important to you, please save a copy. For more information on the upcoming change, we invite you to read our blog post. Disk space can be freed using a variety of means, including the following: Moving files to a backup medium.
Transferring files to a remote server. Files that are good candidates for cleanup include: Files that the user will never need again. Temporary files that exist only for performance reasons. Files that can be restored, if needed, from an installation CD. Data files that have possibly been superseded by newer versions, such as old backup files. Older files that have not been used in a long time. The following facets of the Disk Cleanup utility are discussed in this topic. Disk cleanup can be initiated in three ways. The system notifies the user with a message box that unused disk space has reached critical mode.
The critical mode threshold for a drive larger than 2. Subsequent warnings are given at 80, 50, and 1 MB. After measurement, research data undergo repeated steps of being entered into information carriers, extracted, transferred to other carriers, edited, selected, transformed, summarized, and presented. It is important to realize that errors can occur at any stage of the data flow, including during data cleaning itself.
Table 1 illustrates some of the sources and types of errors possible in a large questionnaire survey. Most problems are due to human error. Inaccuracy of a single measurement and data point may be acceptable, and related to the inherent technical error of the measurement instrument. Hence, data cleaning should focus on those errors that are beyond small technical variations and that constitute a major shift within or beyond the population distribution. In turn, data cleaning must be based on knowledge of technical errors and expected ranges of normal values.
Some errors deserve priority, but which ones are most important is highly study-specific. In most clinical epidemiological studies, errors that need to be cleaned, at all costs, include missing sex, sex misspecification, birth date or examination date errors, duplications or merging of records, and biologically impossible results.
For example, in nutrition studies, date errors lead to age errors, which in turn lead to errors in weight-for-age scoring and, further, to misclassification of subjects as under- or overweight. Errors of sex and date are particularly important because they contaminate derived variables. Prioritization is essential if the study is under time pressures or if resources for data cleaning are limited.
When screening data, it is convenient to distinguish four basic types of oddities: Screening methods need not only be statistical. Many outliers are detected by perceived nonconformity with prior expectations, based on the investigator's experience, pilot studies, evidence in the literature, or common sense. Detection may even happen during article review or after publication. What can be done to make screening objective and systematic? To allow the researcher to understand the data better, it should be examined with simple descriptive tools. Standard statistical packages or even spreadsheets make this easy to do [ 20 , 21 ].
For identifying suspect data, one can first predefine expectations about normal ranges, distribution shapes, and strength of relationships [ 22 ]. Second, the application of these criteria can be planned beforehand, to be carried out during or shortly after data collection, during data entry, and regularly thereafter.
Third, comparison of the data with the screening criteria can be partly automated and lead to flagging of dubious data, patterns, or results. A special problem is that of erroneous inliers, i. Erroneous inliers will often escape detection. Sometimes, inliers are discovered to be suspect if viewed in relation to other variables, using scatter plots, regression analysis, or consistency checks [ 23 ].
One can also identify some by examining the history of each data point or by remeasurement, but such examination is rarely feasible. Useful screening methods are listed in Box 2. In this phase, the purpose is to clarify the true nature of the worrisome data points, patterns, and statistics.
Possible diagnoses for each data point are as follows: Some data points are clearly logically or biologically impossible. Hence, one may predefine not only screening cutoffs as described above soft cutoffs , but also cutoffs for immediate diagnosis of error hard cutoffs [ 10 ]. Figure 2 illustrates this method. Sometimes, suspected errors will fall in between the soft and hard cutoffs, and diagnosis will be less straightforward. In these cases, it is necessary to apply a combination of diagnostic procedures. One procedure is to go to previous stages of the data flow to see whether a value is consistently the same.
This requires access to well-archived and documented data with justifications for any changes made at any stage. A second procedure is to look for information that could confirm the true extreme status of an outlying data point. For example, a very low score for weight-for-age e.
Individual patients' reports with accumulated information on related measurements are helpful for this purpose. This type of procedure requires insight into the coherence of variables in a biological or statistical sense. Again, such insight is usually available before the study and can be used to plan and program data cleaning.
A third procedure is to collect additional information, e. Such procedures can only happen if data cleaning starts soon after data collection, and sometimes remeasuring is only valuable very shortly after the initial measurement. In longitudinal studies, variables are often measured at specific ages or follow-up times.
With such designs, the possibility of remeasuring or obtaining measurements for missing data will often be limited to predefined allowable intervals around the target times. Such intervals can be set wider if the analysis foresees using age or follow-up time as a continuous variable.
Finding an acceptable value does not always depend on measuring or remeasuring.
Creating a Disk Cleanup Handler
For some input errors, the correct value is immediately obvious, e. This example again illustrates the usefulness of the investigator's subject-matter knowledge in the diagnostic phase. Substitute code values for missing data should be corrected before analysis. The diagnostic phase is labor intensive and the budgetary, logistical, and personnel requirements are typically underestimated or even neglected at the study design stage. How much effort must be spent?
Cost-effectiveness studies are needed to answer this question. Costs may be lower if the data-cleaning process is planned and starts early in data collection. Automated query generation and automated comparison of successive datasets can be used to lower costs and speed up the necessary steps. After identification of errors, missing values, and true extreme or normal values, the researcher must decide what to do with problematic observations. The options are limited to correcting, deleting, or leaving unchanged. There are some general rules for which option to choose.
Impossible values are never left unchanged, but should be corrected if a correct value can be found, otherwise they should be deleted. For biological continuous variables, some within-subject variation and small measurement variation is present in every measurement. If a remeasurement is done very rapidly after the initial one and the two values are close enough to be explained by these small variations alone, accuracy may be enhanced by taking the average of both as the final value.
What should be done with true extreme values and with values that are still suspect after the diagnostic phase? The investigator may wish to further examine the influence of such data points, individually and as a group, on analysis results before deciding whether or not to leave the data unchanged. Statistical methods exist to help evaluate the influence of such data points on regression parameters. Some authors have recommended that true extreme values should always stay in the analysis [ 25 ].
In practice, many exceptions are made to that rule. The investigator may not want to consider the effect of true extreme values if they result from an unanticipated extraneous process. Alternatively, it may be that the protocol-prescribed exclusion criteria were inadvertently not applied in some cases [ 26 ]. Data cleaning often leads to insight into the nature and severity of error-generating processes. The researcher can then give methodological feedback to operational staff to improve study validity and precision of outcomes. It may be necessary to amend the study protocol, regarding design, timing, observer training, data collection, and quality control procedures.
In extreme cases, it may be necessary to restart the study. The sensitivity of the chosen statistical analysis method to outlying and missing values can have consequences in terms of the amount of effort the investigator wants to invest to detect and remeasure. It also influences decisions about what to do with remaining outliers leave unchanged, eliminate, or weight during analysis and with missing data impute or not [ 27—31 ]. Study objectives codetermine the required precision of the outcome measures, the error rate that is acceptable, and, therefore, the necessary investment in data cleaning.
Longitudinal studies necessitate checking the temporal consistency of data. Plots of serial individual data such as growth data or repeated measurements of categorical variables often show a recognizable pattern from which a discordant data point clearly stands out. In clinical trials, there may be concerns about investigator bias resulting from the close data inspections that occur during cleaning, so that examination by an independent expert may be needed. In small studies, a single outlier will have a greater distorting effect on the results.
Some screening methods such as examination of data tables will be more effective, whereas others, such as statistical outlier detection, may become less valid with smaller samples. The volume of data will be smaller; hence, the diagnostic phase can be cheaper and the whole procedure more complete. Smaller studies usually involve fewer people, and the steps in the data flow may be fewer and more straightforward, allowing fewer opportunities for errors. In intervention studies with interim evaluations of safety or efficacy, it is of particular importance to have reliable data available before the evaluations take place.
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There is a need to initiate and maintain an effective data-cleaning process from the start of the study. Good practice guidelines for data management require transparency and proper documentation of all procedures [ 1—4 , 30 ]. Data cleaning, as an essential aspect of quality assurance and a determinant of study validity, should not be an exception. We suggest including a data-cleaning plan in study protocols. This plan should include budget and personnel requirements, prior expectations used to screen suspect data, screening tools, diagnostic procedures used to discern errors from true values, and the decision rules that will be applied in the editing phase.
Proper documentation should exist for each data point, including differential flagging of types of suspected features, diagnostic information, and information on type of editing, dates, and personnel involved. In large studies, data-monitoring and safety committees should receive detailed reports on data cleaning, and procedural feedbacks on study design and conduct should be submitted to a study's steering and ethics committees.
Guidelines on statistical reporting of errors and their effect on outcomes in large surveys have been published [ 31 ]. We recommend that medical scientific reports include data-cleaning methods. These methods should include error types and rates, at least for the primary outcome variables, with the associated deletion and correction rates, justification for imputations, and differences in outcome with and without remaining outliers [ 25 ]. Detecting, diagnosing, and editing data abnormalities. PLoS Med 2 National Center for Biotechnology Information , U.
Published online Sep 6. The authors have declared that no competing interests exist. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
This article has been cited by other articles in PMC. The History of Data Cleaning With Good Clinical Practice guidelines being adopted and regulated in more and more countries, some important shifts in clinical epidemiological research practice can be expected. Terms Related to Data Cleaning Data cleaning: Changing the value of data shown to be incorrect. Data value falling within the expected range. Data value falling outside the expected range. Data Cleaning as a Process Data cleaning deals with data problems once they have occurred. Open in a separate window.
A Data-Cleaning Framework Illustration:
Related Cleanup: Volume V: The Observation Collection
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