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AN ALERT MANAGEMENT APPROACH TO DATA QUALITY LESSONS LEARNED FROM THE VISA DATA AUTHORITY P

AN ALERT MANAGEMENT APPROACH TO DATA QUALITY LESSONS LEARNED FROM THE VISA DATA AUTHORITY P
AN ALERT MANAGEMENT APPROACH TO DATA QUALITY LESSONS LEARNED FROM THE VISA DATA AUTHORITY P

A N A LERT M ANAGEMENT A PPROACH TO D ATA Q UALITY:

L ESSONS L EARNED FROM THE

V ISA D ATA A UTHORITY P ROGRAM

(Completed Research Paper)

Joseph Bugajski

Visa International

JBugajski@https://www.docsj.com/doc/819479285.html,

Robert L. Grossman1

Open Data Group

rlg1@https://www.docsj.com/doc/819479285.html,

Abstract: We introduce an end-to-end framework for data quality that integrates business strategy, data quality models, and supporting investigative and governance processes. We also describe a supporting IT architecture for this framework. Finally, we describe how this framework was implemented at Visa International Service Association (“Visa”)and some of the lessons learned during its use over the past three years.

Key Words: Data Quality, Information Quality, Baselines, Change Detection, Alert Management Systems

I NTRODUCTION

There is now a relatively mature model, which is usually called the dimensional model of data quality, for characterizing data quality problems in terms of dimensions, such as accuracy, completeness, consistency, timeliness, etc [3]. On the other hand, there is not yet an end-to-end framework that includes the necessary strategic, IT, investigative and governance processes that are required to turn the monitoring of data quality dimensions into a successful data quality program.

In practice, the limiting factor for many data quality programs is the time and costs required to validate data quality problems, to identify root causes, and to ameliorate the problems identified. Because of the time and costs involved, the framework we introduce directly manages data quality alerts and associated investigative processes. We call this approach an alert management approach for data quality.

In short, the challenge is that dimensional models of data quality do not adequately integrate the business objectives and investigative processes that are part of most successful data quality programs.

We believe that this paper makes the following three contributions:

1.We introduce an approach for data quality that integrates business strategy, data quality models,

and the supporting investigative and governance processes.

2.We describe an IT architecture that supports this framework.

3.We describe some of the lessons learned from a data quality program at Visa International

Service Association (“Visa”) that utilizes this framework.

1 Robert L. Grossman is also a faculty member at the University of Illinois at Chicago.

A N A LERT M ANAGEMENT A PPROACH FOR D ATA Q UALITY

An alert management approach for data quality uses statistical and rules-based models to screen event data, update profiles that contain statistical summaries of business entities of interest, and generate alerts that are then investigated by analysts to monitor data quality.

The alert management approach for data quality we introduce in this paper consists of five main steps. We now describe these steps in general and then for concreteness discuss them in the context of one of the case studies described below. In this case study, a Canadian bank had a coding error in their card payment processing software that for certain transactions incorrectly coded the country where the transaction took place. This resulted in some of these transactions being declined.

The first step is to identify an appropriate business problem or opportunity. In this step, we also need to define an appropriate measure that can be used to quantify progress. In the example, the appropriate measure is the approval rate for valid transactions. As mentioned, an incorrectly coded country code field can result in valid transactions being declined. When this occurs, the “interoperability of data” is suspect as that data moves from one processing systems in the financial network to another. We define data quality and information quality problems that result in adverse business outcomes to be “Data Interoperability” issues.

The second step is model development. The alert management framework we propose employs three different types of models:

a)The first type of models are statistical models that quantify the relation between fields that can be

monitored for data quality and the business measure of interest, which in our example, is the decline rate.

b)The second type of models are rules-based models that are used to apply business rules to the

outputs of statistical models in order to adjust the number of alerts and increase their business relevance and value.

c)The third type of models are architecture models, in particular, architecture models that map

business requirements into technical specifications and technical specifications to data attributes in the data being monitored.

For the implementation of this framework at Visa, the statistical model we developed was designed to detect correlations between monitored fields and the decline rate. As described in [1], we in fact developed not just one statistical model, but thousands of them, since in this way, we were able to use statistical models for relatively homogeneous subsets of data. This approach to using multiple models is sometimes called model segmentation in the statistical community. The Visa implementation also made use of rules so that the scores produced by the statistical models could reflect specific business requirements, for example, different regions and different sizes of the banks processing the transactions. The Visa implementation also made extensive use of architecture models, since by directly using architecture models that linked data attributes to high leveler constructs, such as face-to-face transactions or e-commerce transactions, reduced the likelihood of data interoperability issues arising in the first place [2].

The third step is to deploy the models developed. One approach to deploying statistical and rule-based

models is to use what are sometimes called scoring engines. These are applications that monitor operational data using XML-based description of statistical models [4] so that changes can be quickly detected and acted on. A monitor employing a scoring engine has two important components. The first component is the scoring engine itself that uses the statistical model produced in the prior step to assign scores to operational data. A threshold is then used to identify data most likely to be poor in data quality and relevant to the measure. This results in alerts. The second component applies business rules to further restrict the number of alerts and to increase the likelihood that an alert results in something actionable and having a significant business impact.

The effective use and deployment of architecture models by developers requires that behavior be changed. For this reason, the successful deployment of architecture models is closely tied to governance. For more details about architecture models and data quality, see [2].

By using appropriate standards, the cost to implement and deploy models can be reduced. In other words, the time and effort to go from Step 2 to Step 3 can be reduced. For this reason, standards are an important component of this framework.

The fourth step is a manual process that investigates the alerts. This begins with a quick assessment of the alert to decide whether further investigation is required. If so, the alert is assigned to one or analysts for a more detailed examination.

The final component is the governance and oversight process. Without an effective governance process, it is unlikely that the alerts identified will be acted on and lead to measurable value. Part of the oversight process is the updating of the dashboard as progress is made towards the identified problem or opportunity.

A summary of the process is below. Figure 1 also contains a flow chart describing the process. The process can be remembered using the mnemonic IMDIG from the first letter of the five key steps – Identify, Model, Deploy, Investigate and Govern.

1.Identify the business problem or opportunity.

1.1.Determine how to measure it.

1.2.Create a dashboard to track progress using previously agreed measures.

2.Model – build statistical models that monitor the measures identified; build rule-based models;

develop architecture models

2.1.Collect the appropriate data.

2.2.Analyze the data.

https://www.docsj.com/doc/819479285.html,e the data to build a statistical model.

2.4.Update the statistical model as required.

2.5.Develop and maintain rules-based models.

2.6.Develop architecture models that link the business requirements and the underlying data

fields.

3.Deploy – implement the models in operational environments.

https://www.docsj.com/doc/819479285.html,e the statistical model to score the operational data. Data may be processed event by

event; or, if required, by maintaining state information using signatures.

3.2.Process events/signatures whose scores are above a threshold using rules.

3.3.Events/signatures whose scores are still above a predetermined threshold are then processed

using rules.

3.4.Events/signatures that pass the rules are then issued as candidate alerts.

3.5.Monitor any previously identified alerts

3.6.Deploy architecture models so that data quality and data interoperability problems are less

likely to occur in the future.

4.Investigate the candidate alerts.

4.1.Perform a preliminary investigation to quickly triage alerts.

4.2.If warranted, perform an in-depth investigation to identify root causes.

4.3.Ameliorate any issues identified.

4.4.Establish a value for the alert and report the value using the dashboard.

https://www.docsj.com/doc/819479285.html,ern – support the process with appropriate governance and oversight.

5.1.Obtain alignment of strategic objective, operational oversight, and investigation process.

5.2.Report upwards on the meeting of strategic objective and the value generated using a

dashboard.

5.3.Refine operational thresholds, rules, and number of statistical models used to adjust alert

workflow.

5.4.Establish a formal reference model describing the program and approach and a process for

reviewing and updating the reference model.

Table 1. This table describes the main steps of the IMDIG process.

Figure 1. This flowchart provides an overview of the IMDIG process.

An Alert Management Architecture to Support Data Quality

In this section, we describe an IT system architecture to support the IMDIG framework. See Figure 2. Model Producers and Consumers

The main two system components are a model producer and a model consumer. Briefly, a model producer estimates the parameter of a statistical model and is generally used by someone familiar with data and comfortable working with it. A model consumer is designed so that it can be integrated easily with operational systems and is designed to be operated by someone familiar with IT systems. A model producer and a model consumer communicate though an XML file using a standard called the Predictive Model Markup Language or PMML [4].

Separating the system into two components in this way simplifies deployment since once a model consumer is integrated with an operational system, updating or refreshing statistical models simply requires reading an XML file.

The model producer extracts event data from a project data mart, computes derived attributes and state information from the events (which are sometimes called signature or profiles), persists the signatures, and then uses the information to estimate the parameters of a separate model for each segment.

The model consumer first reads a PMML file specifying the segmentation and the model parameters for each segment. The model consumer than processes a stream of event data and produces a stream of scores. Sometimes a PMML consumer is called a scoring engine since it processes input data using a PMML file to produce a stream of output scores.

Rules Engine

A rules engine is used so that business rules can be used to reduce the number of alerts produced by the Model Consumer. The number of alerts can be controlled in a variety of ways, including: ?by modifying rules or adding new rules;

?by changing the thresholds used in the rules.

As a simple example, a dollar threshold that estimates the approximate business value of the alert can be raised to reduce the number of alerts.

Report Processing Engine

The Report Processing Engine takes the alerts that satisfy all the required rules and arranges them into a report. Another mechanism for managing alerts is to use rules for ordering, formatting and arranging alerts in the reports. For example, alerts can be ordered by the measures used in the dashboard. In our example, we implemented the Report Processing Engine using XSLT so that it was relatively easy to change the format and structure of reports.

Metadata Repository

We stored the various metadata for the project, including the PMML models and rules in a repository. This was important since it was a project requirement that we be able to retrieve the specification for any baseline that had been computed by the project.

Figure 2. This figure shows a system architecture that supports a IMDIG Program.

The VDA Program at Visa

In this section, we describe the Visa Data Authority (VDA) Program at Visa that is based upon an alert management framework for data quality.

The VDA Program had three phases: architecture, development, and production. Prior to the start of the program three years ago, there was a two-year research effort. One of the main conclusions of this research effort was that data architecture was a primary contributor both to data quality issues and to data interoperability problems. By data architecture in this context we mean the mapping of business requirements into technical specifications, and the mapping of technical specifications to the data elements that eventually appear in the transactional data. The VDA Program approach to data architecture and architectural modeling is described in the paper [2].

Strategic Objective. Critical to the success of the VDA program was identifying its strategic objective and how to measure it. After much discussion, the following objective was agreed upon: identify and ameliorate data quality and data interoperability issues in order to maintain and improve 1) approval rates for valid transactions; 2) disapproval rates for invalid or potentially fraudulent transactions; and 3) correct coding of transactional data. The first two objectives increase the satisfaction of card holders and member banks, while the third objective lowers the overall cost and increases the efficiency of transaction processing. The success of the program was then tracked by a dashboard that monitored the additional dollars processed and the savings resulting from direct actions of the program.

Governance. The Visa Data Interoperability Program was established in 2004 by the global council of the CIO’s of Visa’s operating units. The program is governed by a global subcommittee of the council of CIOs. This group comprises business executives, business analysts and technical experts who set rules, procedures and processes for the program. Early on, regular meetings were established between the executives in charge of the program and those in operations so that procedures could be developed that assured that data quality and interoperability problems identified in the program were acted upon. Developing the baseline model.Given the amount of data, our approach was not to develop a single baseline model but rather tens of thousands of them, one for each cell in a multidimensional data cube. For the VDA Alerts described below, we defined a data cube with the following dimensions:

1.The geographical region, specifically the US, Canada, Europe, Latin America, Asia Pacific,

Middle East/Africa, and other.

2.The field value or combination of values being monitored.

3.The time period, for example monthly, weekly, daily, hourly, etc.

4.The type of baseline report, for example a report focused on declines or a report describing the

mixture of business for a merchant.

Today (June, 2007), for each of 324 field values times 7 regions times 1 time period times 3 report types, we estimate a separate baseline, which gives 324 x 7 x 1 x 3 = 6816. In addition, for 623 field values times 7 regions times 1 time period times 2 report types, we estimate a separate baseline, which gives an additional 623 x 7 x1 x 2 = 8726 separate baseline models. So in total, we are currently estimating 15,542 (=6816+876).

Actually, the description above is a simplified version of what actually takes place. For example, the 6816 baselines mentioned arise from 324 x 7 = 2272 different field values, but the 2272 different field values are not spread uniformly across the 7 regions as indicated, although the total is correct.

Deploying the baseline model. A system supporting data cubes of baseline models was designed and developed using the ideas described above to monitor transactional payment data. For simplicity, call this system the Monitor. The Monitor receives daily samples of tens of millions of authorization messages and clearing transactions from a central ETL facility inside VisaNet. Statistically significant deviations from baselines that are associated with high business value generate what are called Baseline Threshold Alerts.

Investigating Candidate Alerts. Baseline Threshold Alerts are screened by an analyst to produce what are called Baseline Candidate Alerts. Candidate alerts are then analyzed by business analysts and other subject matter experts to understand the issues that led to the candidate alert and to more carefully estimate the business value being lost. If the program team believes that an issue was valid and sufficiently valuable that the cost of repair may be recovered through recaptured revenue or lower processing costs, and, furthermore, they believe that the issue is sufficiently clear that it may be explained accurately to operations and business executives at two or more different firms, they send a Program Alert to the customer relationship manager at the Visa operating region that is closest to the external party that best available evidence indicates is likely the owner of problem. This may be a third party payment processor, an acquiring or issuing bank, or a VisaNet technical group.

The customer relationship manager works with the program analysts to explain the problem identified by the Program Alert to the bank or merchant and to work with them to estimate the cost required to fix the problem. The program team meanwhile reviews measurements to determine when and if the problem is

resolved. If the data measurements indicate a resolution, then the business that effected the change is contacted once again to validate recovery of revenue or loss avoidance.

Reference Model. The governance council adopted technical standards for how alerts are generated and business process standards for how alerts are investigated. These rules are recorded in a Reference Model that is maintained by the Program and updated at least twice each year.

Standards. Over time, we developed an XML representation for baseline models and for segmentation. We worked with the PMML Working Group and this work has not contributed to the PMML Baseline and Change Detection Model, which is currently in a RFC status. Over the long term, this should reduce the total costs of the system by enabling the use of third party tools that support this RFC draft standard. Balancing Manageable vs. Meaningful Alerts. We conclude this section by listing some of the ways that we reduced the number of alerts (so that the alerts become more manageable) or increased the number of alerts (so that the alerts become more meaningful). One of the advantages of the IMDIG framework, is the flexibility provided to adjust the number of alerts.

1.The easiest way to adjust the number of alerts is to adjust the thresholds of the rules in the Rules

Engine.

2.Another simple way to adjust the number of alerts is to adjust the number of segments used (in

this framework, each segment is associated with one model).

3.The thresholds and parameters of the various statistical models can also be adjusted.

4.The number of alerts can also be managed by changing the rules or adding new rules.

5.Finally, the investigation of alerts often can be effectively controlled by changing the reports, for

example, by ordering the alerts in different ways, or highlighting alerts satisfying certain properties.

Figure 3. This figure is an overview of the framework used by the VDA Program at Visa.

S OME T YPICAL VDA A LERTS

Overview

Since the program began, Visa and its trading partners have fixed 70 data interoperability issues. The improvement in annual card sales volume realized thereby is about $2 billion, which is nonetheless significant although it is a small number compared with Visa’s global annual card sales volume of $4.6 trillion. These “fixed Alerts” comprise 25% of data interoperability issues presently being investigated. In this section, we describe several of these alerts.

Customer Satisfaction Issue : Chip Card Terminal Coding Error

Payment cards with embedded microprocessors (“chip cards”) are widely used around the world but standards for chip cards and terminals can be incompatible from one region of the world to another. Chip cards and terminals also differ functionally. If a cardholder uses a chip card at terminal built to an incompatible standard, it usually will not work. The merchant then likely will accept that card for payment by swiping the magnetic stripe on the back of the card across a magnetic strip reading terminal.

A more subtle problem occurs when chip cards and terminals follow the same standard but differ in

functionality. In this case, the merchant’s terminal will read the chip card but the wrong codes will be sent to the cardholder’s bank. This will cause the transaction to be declined unless the cardholder or merchant takes extra time and inconvenience to telephone their respective banks. In either case, the payment experience will not meet Visa’s standards of excellence, and if the problem were to persist, it could lead to lower total sales using chip cards.

A chip card to chip terminal functionality difference was detected in November 2005 by Visa’s VDA Program as a data coding error coming from several merchants serviced by the same US bank. The data analyst who researched the issue noted that most international payments at these merchants were being declined because the chip cards and chip terminals were functionally incompatible. The manager responsible for assisting the merchant’s bank enquired about the coding problem and determined that the source of the problem was an ambiguity in a written specification for the chip terminal. A technical letter was sent to all banks that prevented a localized problem from becoming a serious issue.

Lost Revenue Issue : Incorrect Country Code Error

Many risk control systems for electronic payments use time and location information, among a number of other facts, to assess the authenticity of a transaction; e.g., purchase date and time, postal code, city name, country code. For instance, a cardholder who had just used her card at a department store in Edinburgh is not likely to also be using her card at a jewelry store in Beijing. One of these transactions may represent an attempted fraudulent use of the cardholder’s information. This case presents a classic dichotomy. The card issuing bank risks losing the entire value of the transaction if they were to accept a payment that later proves to have been inauthentic, but otherwise, it was submitted legitimately by the merchant through their acquiring bank. If the issuer declines the payment then they risk inconveniencing their customer. The only way to be certain that the cardholder made both purchases is to contact the cardholder and enquire about the suspect payment activity. If the bank determines that the location of one of the two payments is invalid, or if reliable supporting information is unavailable, then banks may decide to decline a transaction request. When this happens, and if issuer guessed incorrectly that the transaction was authentic, the cardholder may use another brand of payment card, not complete their purchase, or use cash, thus losing the value of the sale to Visa and the member banks participating in the processing of the transaction.

A Canadian bank had a coding error in their card payment processing software that routinely entered invalid country codes into transactions being accepted by several thousand merchants who banked with them. These errors are quite difficult to detect because every transaction processed by that software has the same error. But, card issuing banks routinely want to know the country where a transaction is occurring in keeping with the rules they established for risk controls. In the absence of valid information, banks often decline such payment requests.

The VDA Program detected the country code error and also showed that the error was accompanied by usually high rates of declined payment authorization requests. These errors came from many merchants who were customers of the same bank whereas similar merchants, who were customers of other banks, did not have the same country code error in their transactions. After the Visa manager contacted the bank, the source of the error was found and the rate at which payments were being approved increased dramatically.

Overpayment Issue : Incorrectly Coded Sales Channel

Banks charge fees to merchants who legitimately accept Visa card payments in return for services provided, additional customer traffic that results from card acceptance, and a guarantee of payment. This last benefit is provided by the card issuing bank that pays for the transaction even if it is found later to have been made fraudulently. In return for accepting this risk, the bank that issued the card receives a small portion of the payment to offset risks of accepting electronic payments. The amount of risk associated with an electronic payment varies according to the nature of the item purchased; e.g., jewelry is fungible where groceries are less so; the location where the card payment was accepted; and sales channel; e.g., store front merchants versus those who do business solely through the Internet. If the payment transaction incorrectly represents any of this information, payments may be declined more often than not, or the merchant or the merchant’s bank may pay higher fees to issuing banks than they otherwise would have.

After a Scandinavian bank upgraded their software, many of their transactions were incorrectly identified as higher risk than was actually the case. This resulted in steadily increasing risk offset costs for that bank. The baseline system simultaneously detected a sudden decrease in sales through lower risk channels and a corresponding increase in sales from higher risk sales channels. The data analyst found that the payment data was inconsistent with the true nature of the sales channel for the subject transactions. A meeting with the bank operations team quickly turned this around and kept a problem from growing into a serious issue for the acquirer.

R ELATED W ORK

Perhaps the most common approach to data quality is to introduce various dimensions of data quality and to measure the adherence of data with respect to these dimensions [3]. The table below contrasts the alert management approach to data quality with the more traditional approach of measuring data along the various dimensions of data quality.

Dimensions of Data Quality Alert Management System Approach

to Data Quality

Strategic Alignment Not specifically noted Identify one or more strategic objectives

and corresponding measures to

determine progress in achieving strategic

objectives.

Model Establish rules to monitor various

dimensions of data quality:

?Inaccurate

?Incomplete

?Invalid

?Inconsistent

?etc. Correlation Models:

?Develop correlation models to measure correlation of events

and signatures with identified

measures.

?Develop rules designed to

reduce number of alerts and to

prioritize them.

Deployment Apply rules to operational data

and report on percentage of rules

that are satisfied. Create initial set of alerts when scores from models are above a threshold. Create reduced set of alerts by applying rules to initial set of alerts.

Investigative Process Not specifically noted Escalate through series of investigations

Oversight & Governance Not specifically noted Identify strategic objective, business

measures, and dashboard. Set up

governance process to oversee.

Table 2. This table compares the approach to data quality described in [3] to the approach proposed here.

S UMMARY AND C ONCLUSION

In this paper, we have introduced an alert management approach to data quality called IMDIG. The essential steps in the IMDIG framework are:

Identify – The first step is to identify an appropriate business problem or opportunity related to data quality and an associated measure.

Model– The second step is to build statistical model, rules-based models, and architectural models that relate possible data quality issues to the measure.

Deploy– The next step is to deploy the statistical model, associated rules engine, and architectural models.

Investigate– The fourth step is to investigate the alerts produced by the statistical model and associated rules engine.

Govern– The final component of the framework is to set up an appropriate governance and

oversight process.

We described how this framework has been used as a basis for a successful program that identifies and ameliorates data quality and data interoperability problems at Visa.

In future work, we plan to develop algorithms that can set automatically some of the model parameters when more alerts are desired so that the alerts become more meaningful or when fewer alerts are desired so that the alerts become more manageable.

R EFERENCES

[1] Joseph Bugajski, Robert L. Grossman, Eric Sumner and Steve Vejcik, Monitoring Data Quality for Very High

Volume Transaction Systems, Proceedings of the 11th International Conference on Information Quality, 2006.

[2] Joseph Bugajski and Philippe De Smedt, Assuring Data Interoperability Through the Use of Formal Models of

Visa Payment Messages, submitted for publication.

[3] Leo L. Pipino, Yang W. Lee and Richard Y. Wang, Data Quality Assessment, Communications of the ACM,

Volume 45, 2002, pages 211-218.

[4] The Predictive Model Markup Language (PMML), https://www.docsj.com/doc/819479285.html,.

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如何将微信聊天记录导入电脑?以上就是分享给大家的备份教程了。开心手机恢复大师除了可以备份微信聊天记录,还可以恢复通讯录、照片、QQ聊天记录等17种数据哦,有需要的小伙伴们赶紧去免费下载试试吧。备份数据很重要,这样就不用担心数据丢失了。

三招彻底清理微信,但聊天记录全部保留!

现在手机的存储内存越来越大,基本都是64GB起步,若是高端一些的机型基本都是128GB 起步。但你有没有发现,手机内存越来越大的同时,手机里各式各样的App占用空间也越来越大,其中的罪魁祸首就是微信! 由于微信是我们日常生活工作当中重要的社交pp,其中许多的聊天记录、微信中保存的文档对我们来说都非常重要,因此大多数人很少会去清理微信的空间。正因如此,长年累月下来,微信从一个不到100MB的精简App变成了一个十几GB、甚至几十GB的庞然大物…… 那有没有办法保留聊天记录,保留我们保存的文件,同时还能清理微信空间呢?当然可以,请往下看! 保留聊天记录,清理微信缓存 随着我们频繁的使用,微信内的数据越来越多,这部分数据除了我们的聊天记录(包括聊天记录中的视频、图片、文档)外,还有很大一部分在于我们的朋友圈缓存!微信会自动把你浏览过的朋友圈图片、小视频都保留下来,所以你会发现,即使你现在去翻某个朋友2~3年前的朋友圈小视频,它也是不用加载的!因为它已经被保存到了你的微信里! 假设你朋友圈里有2000个好友,平均每个好友每5天发一个小视频,一年差不多70个小视频,2年就是140个,2000个好友就是28万个!如果你曾经看过其中的1/3的小视频,也就是近9万个小视频保存在你的微信当中……如果你再算上图片……现在你应该知道微信占用空间为什么那么大了吧,缓存就是罪魁祸首!你的朋友圈每天都在更新!你的微信缓存每天都在壮大! 如何清理微信缓存?其实vivo手机内置的应用管理就可以清除微信缓存,只是它隐藏得有点深……在「设置→更多设置→应用管理→微信→存储→清除缓存」中可直接清理微信缓存,清理完成之后,就会把你朋友圈缓存的图片、小视频都清除! 关闭自动保存功能 除了微信朋友圈缓存外,微信在聊天记录中自动保存的视频、图片也是占用了一部分空间。不过我们这次要清除的并不是别人发给你的视频和图片,而是你给别人发的视频和图片。 微信默认会将你从微信中拍摄的视频、图片,发给他人的视频、图片、文件都二次保存在你的微信当中,注意!这是“你发给别人的图片、文件、视频”,也就是说你的手机当中原本就有这些东西,而通过微信发送后,微信又帮你把这些文件二次保存了!这又无形中占用了手机的一部分空间。那么如何清理呢? 由于这些图片、视频、文件其实是保存在聊天记录当中的,而清理缓存的步骤是不会清理到聊天记录的,想要清理这些已经保存的文件,只能清理相应的聊天记录,但是!我们可以把

制作vcd视频文件的方法

制作vcd视频文件的方法 描述: 如果想把avi,mepeg,wmv,rm等影音文件,做成普通的vcd碟片,就需要把这些影音文件转换成dat格式的文件,再用这个dat格式的文件替换vcd碟片中Mpegav文件夹(每一个VCD光盘里面都必定有的文件夹)中的文件。 下面介绍我做VCD的全过程: 第一步:先将你想制作的VCD原文件(它们是avi,wmv,rm等影音文件)转换成DA T 格式的文件,保存在你的电脑硬盘里面。我用的是软件是WinA VI Video Converter V7.7汉化版,感觉这个软件比较好用,你安装后照着提示完成文件的格式转换。注:这个软件是把avi,wmv,rm等文件转换成MPG格式,直接转换成DA T格式的软件不好找,再说这个软件转换成MPG格式的速度很快(600M的WMV格式转换成MPG的只用了30分钟)。当你的文件变成MPG格式后,直接把文件的后缀名改为DA T格式就完成了转换(注:其他格式不能直接该后缀名变成DA T格式,只能是MPG格式才可以直接改)。第二步:制作VCD,以下介绍两种方法。 方法一:制作视频光盘: 方法:直接将你已经制作好的DA T文件做成VCD。在你点击制作光盘软件时,选择制作VCD视频光盘,然后添加你制作好的DA T文件,点击下一步,直到光盘制作完,这样你就完成了VCD的制作。说明:此方法是最简单,它在制作视频数据时将自动升成很多文件(包括Cdda,Cdi,Karaoke,Mpegav,Vcd文件夹)。注意,上面的DA T 文件的文件名必须依次是Avseq01.dat,Avseq02.dat,Avseq03.dat……,不能用其他的文件名,否则将不能播放。 方法二:制作数据光盘: 方法:任意找一张VCD光盘,把它里面的所有文件都拷贝到你的电脑硬盘上(必须具备Cdda,Cdi,Karaoke,Mpegav,Vcd文件夹),放在一个文件夹里面(比如叫VCD文件夹里面),然后将Mpegav文件夹里面内容删除,将你制作好的DAT文件放在Mpegav文件夹里面,将这个DA T文件的文件名改为Avseq01.dat,如果有多个文件,将其他的分别命名为Avseq02.dat,Avseq03.dat……放在Mpegav文件夹,它们将依次播放。注意:这里的DA T文件必须依次为Avseq01.dat,Avseq02.dat,Avseq03.dat……,不能用其他的文件名,否则将不能播放。然后将Cdda,Cdi,Karaoke,Mpegav,Vcd 这些文件夹制作成数据光盘(注意:不能把这几个文件夹放在一个大的文件夹里面,否则将不能播放)。VCD制作完成。 第三步:测试阶段: 在刻录以前可以用虚拟光驱测试,把D:\vcd文件夹的所有文件和文件夹用虚拟光驱压缩成一个虚拟光驱文件,然后把这个文件装入虚拟光驱中,用超级解霸播放指定盘,看看效果如何。 另外也可以做一个dat格式的片头文件,这个片头文件EO Video软件就可以做成,具体的过程是:用Photoshop等图像处理软件把有关的片头信息做成Jpeg格式的图片,然后EO Video把这个图片按照上面的方法转换成mpeg的影音文件,把转换后的片头文件拷贝到d :\vcd\mpegav文件中,改名为Avseq01.dat,其他的文件依次命名为Avseq02,Avseq03.......这样属于个人的一张音像制品就做成了。

matlab建立dat文件简介

% Step 1: Read image Read in RGB = imread('pic4.jpg'); RGB=imresize(RGB,[1029,1029]); % Step 2: Convert image from rgb to gray GRAY = rgb2gray(RGB); % Step 3A:After eliminating the noise s=fftshift(fft2(GRAY)); [a,b]=size(GRAY); n=3;%Here take the order n comparison,n=1,2,3... d0=30; %Here d0 is the cutoff frequency,d0=10,20,30,... n1=fix(a/2); n2=fix(b/2); for i=1:a for j=1:b d=sqrt((i-n1)^2+(j-n2)^2); h=1/(1+0.414*(d/d0)^(2*n)); s(i,j)=h*s(i,j); end end s=uint8(real(ifft2(ifftshift(s)))); subplot(121),imshow(GRAY); title('Grayscale'); subplot(122),imshow(s); title('Image eliminated noise'); % Step 3B: Threshold the image Convert the image to black and white in order % to prepare for boundary tracing using bwboundaries. figure,imhist(s); title('Histogram'); [CStad,xs]=imhist(s); hold on; plot(xs,CStad,'r'); % save('Cstad.dat', 'CStad'); % close all; %Step3C: Two values, determined in accordance with 70/255 threshold, %dividing the target and background Inew=im2bw(s,70/255); figure;imshow(Inew); title('Image after thresholding'); fid = fopen('CStad.dat','wt'); fprintf(fid,'%g\n', CStad); %\n 换行 fclose(fid);

dat文件制作教程详解

Dat文件就是把DOS命令写在一个文本文件里面,然后保存的时候保存成"所有文件", 文件名是名字.bat 就可以了。 不过BAT文件有很多特殊命令... 批处理制作教程 批处理文件是无格式的文本文件,它包含一条或多条命令。它的文件扩展名 为 .bat 或 .cmd。在命令提示下键入批处理文件的名称,或者双击该批处理文件,系统就会调用Cmd.exe按照该文件中各个命令出现的顺序来逐个运行它们。 在无盘运用及Hack入侵过程中,经常都会用到。 一.简单批处理内部命令简介 1.Echo 命令 打开回显或关闭请求回显功能,或显示消息。如果没有任何参数,echo 命令将显示当前回显设置。 语法 echo [{on off}] [message] Sample:@echo off / echo hello world 在实际应用中我们会把这条命令和重定向符号(也称为管道符号,一般用> >> )结合来实现输入一些命令到特定格式的文件中.这将在以后的例子中体现出来。

2.@ 命令 表示不显示@后面的命令,在入侵过程中(例如使用批处理来格式化敌人的硬盘)自然不能让对方看到你使用的命令啦。 Sample:@echo off @echo Now initializing the program,please wait a minite... @format X: /q/u/autoset (format 这个命令是不可以使用/y这个参数的,可喜的是微软留了个autoset这个参数给我们,效果和/y是一样的。) 3.Goto 命令 指定跳转到标签,找到标签后,程序将处理从下一行开始的命令。 语法:goto label (label是参数,指定所要转向的批处理程序中的行。)Sample: if {%1}=={} goto noparms if {%2}=={} goto noparms(如果这里的if、%1、%2你不明白的话,先跳过去,后面会有详细的解释。) @Rem check parameters if null show usage :noparms echo Usage: monitor.bat ServerIP PortNumber goto end 标签的名字可以随便起,但是最好是有意义的字母啦,字母前加个:用来表示这个字母是标签,goto命令就是根据这个:来寻找下一步跳到到那里。最好有一些说明这样你别人看起来才会理解你的意图啊。

CCS中的.dat文件

详解CCS中的.dat文件 CCS支持的.dat文件的格式为: 定数数据格式起始地址页类型数据块大小 1651 其后是文件内容,每行表示一个数据。 定数固定为“1651”,数据格式可以选择“1”(十六进制整型)、“2”(十进制整型)、“3”(十进制长整型)、“4”(十进制浮点型)。起始地址为存储的地址,页类型和标示为程序或者数据。比如一个.dat文件: 1651 1 800 1 10 0x0000 0x0000 0x0000 0x0000 0x0000 0x0000 0x0000 0x0000 0x0000 0x0000 0x0000

0x0000 0x0000 制作.dat 文件的方法也很简单,可以用VC++或者MATLAB来实现。比如hellodsp的网友lwxing提供的使用matlab创建.dat文件的一个实例: matlab向dsp传递.dat文件 x=2*sin(2*pi*100*m*dt); for m=1:200; if x(m)>=0 y(m)=x(m); else y(m)=4+x(m); end; end; y=y*16384; fid=fopen('input.dat','w');%打开文件,'w'是将此文件定义为可 写的,fid是此文件的整数标示 fprintf(fid,'1651 1 0 1 0\n');%输出文件头,文件头必须是dsp 所能识别的,就如此句程序所设定的 fprintf(fid,'0x%x\n',round(y));%输出y数组,并写到与fid标示符相同的文件,即yinput.dat文件里。round是取y值的最近的数,即如果是1.2,就取1,如果1.6,就取2. fclose(fid); %关闭fid标示符的文件。 fid=fopen('input.dat','w');%打开文件,属性设置为写

maskout方法的使用以及maskout文件制作

maskout文件制作[复制链接] 电梯直达 本帖最后由传说中的谁于 2011-7-9 22:45 编辑 用GrADS作图时,常会遇到要把图形控制字在一定地理边界内的情况(例如,某个省或某个区域);用站点资料绘制等值线或填色图时,也通常会把地理边界以外的信息屏蔽掉。我在另一个帖子《GrADS中Basemap方法的应用》中提到了用basemap方法来解决这个问题,但basemap有其缺陷,就是你用basemap屏蔽掉边界以外的信息以后,再画下一个变量的时候边界以外的区域同样不能显示。现在介绍另一种方法:maskout。maskout是将一个变量在边界内的部分保留原来的值,而在边界外则一概赋给一概新的值:0,因此最后的结果就是只在边界内有值,从而达到边界控制的效果。下面详细介绍maskout方法的使用。 首先,要使用maskout就得有maskout文件,这里介绍的使用MeteoInfo 来制作maskout文件。下载MeteoInfo,安装后启动MeteoInfo。 第一步,添加图层,在弹出对话框中选择“b ou2_4p.shp”→“打地图文件”,将地图文件加载到程序界面中。可以通过缩放来调节地图到你想要的大小。 第二步,获取底图ID。在工具栏中选择“图元属性”,这时候鼠标指针会变

成一个“ i ”的形状。单击你要选择的区域,比如广东省,会弹出一个图元属性的窗口,index后面的数字就是你所选择区域的图元ID,如下图右中的“897”。 第三步,制作maskout文件。选择菜单栏中的“工具”→“输出地图数据”,弹出输出数据的对话框。选择你要的图元,设置输出格式为GrADS Maskout File,然后点“输出”。

DAT 31-.《纸质档案数字化规范》之欧阳家百创编

目录 欧阳家百(2021.03.07)前言4 引言6 纸质档案数字化规范7 1 范围7 2 规范性引用文件7 3 术语和定义7 4 总则8 5.组织与管理9 5.1 机构及人员9 5.2 基础设施10 5.3 工作方案10 5.4 管理制度11 5.5 工作流程控制12 5.6 工作文件管理12 5.7 档案数字化外包13 6 档案出库13 7 数字化前处理14 7.1 确定扫描页14 7.2 编制页号14

7.3 目录数据准备15 7.4 拆除装订15 7.5 技术修复15 8 目录数据库建立15 9 档案扫描16 9.1 基本要求16 9.2 扫描设备17 9.3 扫描色彩模式17 9.4 扫描分辨率18 9.5 存储格式18 9.6 图像命名19 10 图像处理19 10.1 图像拼接19 10.2 旋转及纠偏19 10.3 裁边20 10.4 去污20 10.5 图像质量检查20 11 数据挂接20 12 数字化成果验收与移交21 12.1 验收方式21 12.2 验收内容21 12.3 验收指标22 12.4 验收结论22

12.5 移交23 13 档案归还入库23 前言 本标准按照GB/T 1.1-2009给出的规则起草。 本标准替代DA/T 31-2005《纸质档案数字化技术规范》。 本标准与DA/T 31-2005相比,主要技术变化如下: ——标题进行了修改; ——增强组织与管理部分的内容,完善数字化工作中管理相关要求; ——增强数字化前处理部分的内容,包括对实体档案保护和档案规范化管理方面的要求; ——增加数字化过程中元数据采集的要求; ——修改了档案扫描部分参数要求; ——修改了图像处理部分内容,更加强调保持档案原貌的要求; ——细化了数字化成果验收的内容; ——删除原标准数据备份和数字化成果管理相关内容。 本标准由国家档案局提出并归口。 本标准起草单位:国家档案局档案科学技术研究所、国家档案局信息管理中心、国家档案局技术部。 本标准主要起草人:王良城、马淑桂、郝晨辉、程春雨、杜琳琳、蔡伟、宋涌、王大众、田军、曹燕、李华峰。

视频剪辑制作教程

电影魔方使用教程 电影魔方是一款品质优秀、功能强大、操作简单的多媒体数字视频编辑软件,非常的专业。 主要功能: 界面:自由组合的窗口模式,使用方便的项目及素材管理器,输入、输出双监视窗口;四个编辑轨道的时间轴。 预览:时间码准确定位;双监视窗口可同时预览或操作;在滑块拖动中实时预览;多级变速播放和逆向播放。 字幕:独立的字幕编辑器,快捷的字幕合成方式;丰富的图形绘制功能;16种字幕动态效果。 编辑:直观灵活的素材拖放操作;实用高效的编辑工具箱;支持音频视频同步调整;精确到每帧的编辑精度。 转场:多种精彩转场特效;轻松调整转场长度;任意设定转场参数;提供音频转场效果。 输出:可输出MPEG-1、MPEG-2、VCD、SVCD等视频文件。 支持格式视频:mpg、mpeg、mpv、dat、vob、ts、avi。 音频:mp1、mp2、mp3、AC3、wav。 图像:bmp、jpg、jpeg、gif、ico、wmf. 创作出各种不同用途的多媒体影片,并且可以刻录成VCD或DVD光盘。 下面一起来学习一下该软件。 一、软件下载和安装 通过https://www.docsj.com/doc/819479285.html,/download.asp 软件下载下来是一个压缩文件,解压后开始安装,软件的安装过程十分的简单,只要一路点击“下一步”即可安装完成。如图1所示。

图1 安装完成以后,首次运行时需要输入注册码,用户可到官方网站上免费申请。通过以下网址进行免费申请(https://www.docsj.com/doc/819479285.html,/download.asp)。如图2所示。 图2

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