Question 1: What is data-driven? It's like a medium, for example, if you drink from a bottle, the bottle is the medium, and the water is the data

Question 2: What is data-driven? It's like a medium, for example, if you drink from a bottle, the bottle is the medium, and the water is the data

Question 3: What is data-driven programming? Data-driven development means that it is based on the design of the persistence layer to complete the entire module development process. The Java community in this regard is also attracted attention, the introduction of the bstek, is in the java side of the data-driven to achieve the realization.

Data-driven development by the definition of the persistence layer (data table or persistence layer objects), create a data set (query statement) and bind the data set to the corresponding presentation layer components (tree, table) three steps to complete, this time based on the data set display, maintenance, querying, etc. have been realized

Question 4: Introduction to data-driven testing Data-driven testing, that is, black-box testing ( Black-box Testing), also known as black-box testing, also known as black-box testing. Black-box Testing), also known as functional testing, is to consider the test object as a black box. When utilizing the black-box testing method for dynamic testing, you need to test the functionality of the software product without testing the internal structure and processing of the software product. Data-driven testing focuses on testing the functional requirements of the software, i.e., data-driven testing enables the software engineer to derive the input conditions for performing all the functional requirements of the program. Data-driven testing is not a substitute for white-box testing, but is used to assist white-box testing in finding other types of errors.

Question 5: How does big data-driven operate You can go to the big podium to take a look, there are big data Hadoop tutorials, Spark tutorials and so on in the station, I hope it can help you.

Question 6: What is a data-driven model Using vfw should work

Question 7: Metadata Driver What is a metadata driver This should mean

Continuously improve the existing technical metadata and business metadata by using metadata.

The process of refinement guides the creation of the entire data warehouse.

The metadata-driven data warehouse development process can be subdivided into the following phases:

1) Establishing metadata

a. Define the data source of metadata;

b. Define the content and attributes of metadata;

c. Define the rules for using metadata;

d. Declare the rules for joint use of metadata;

.

Advantages of a metadata-driven, spiraling approach to data warehouse development include:

1. Establishment of a unified view of the data in a centralized data warehouse system;

2. Unified management of metadata;

3. Flexible architecture;

4. Scalable architecture;

In contrast to the "Model Driven Architecture" (MDA), which is a software development framework defined by OMG.The key aspect of MDA is that models play a very important role in the software development process. In MDA, the software development process is driven by the act of modeling the software system.

Question 8: Data-driven performance, what data is critical to business decisions Data is important, of course, and backups are even more important. But unfortunately, many business administrators have been repeating the wrong operations. To backup better and improve the efficiency of backup, there are some key points to master. HELIJI experts in the disaster recovery and backup industry give you some great suggestions: 1. What you must consider when backing up your data The first thing to do when backing up your data is to copy our data to backup media, such as hard disk, optical disk (CD-R or DVD-R) or storage devices on LAN or even servers of cloud backup service, and of course there are some enterprises copying data to tapes. We'll discuss tape backup later, as this backup solution is relatively generic. For now, it's important to consider the two most important factors in data backup: capacity and time. Capacity: If you want to back up your system via tape, then you need more space. For example, if you use an 80GB DLT tape drive to back up your system, then you can only back up less than 80GB of data per day. If the amount of data exceeds this value, we have to consider another option. Of course, there are many options available, and the most common rules are: don't do a full backup of the system, but use incremental backup, so that only less space is needed to back up the data that has changed; if the tape is full, replace the tape with a new one to back up the data; use a machine that automatically switches tapes, so that when you back up the data in the middle of the night, it will automatically change the tapes or switch to the other side to back up the data; The incremental backup scheme should be used with caution, if this scheme is used, then we will need more tapes when restoring the data to achieve a full recovery. There are two ways to perform incremental backups: one way is to back up the data after the last addition or modification multiple times, plus the tape for a full backup, which could potentially require more tapes. For example, if we have one full backup and six incremental backups, we would need seven tapes to meet the demand. Another way is to back up only the data that has been added or modified since the last time, so that we only need one full backup and 1 incremental backup, and only 2 tapes are needed to meet the demand. Time: Here we consider two situations: one is the one that only needs a few hours to complete the backup, and the other is the one that needs several days to complete the backup. For the former case, we don't need to worry at all. Even if you are backing up over the network, don't worry about how much of an impact it will have on the performance of your company's network. However, if it takes several days to back up your data, you'd better set up a new LAN, which can also be realized by setting up a virtual LAN, i.e. VLAN, which we call BAN (Backup Area Network). Even if you are a white-collar worker who works from 9 to 5, you still need a BAN because you may need to back up your web server's data, or you may even need to do a Live Backup. If you have to backup data more than 30 hours a day (already more than 24 hours), then you need to consider reducing the amount of backup data, or to separate these data backup. 2. Confirm the validity of the data There is another issue to consider when backing up data, which is to confirm the validity of the data. If time permits, be sure to have the data backup software confirm the validity of the files after they are written, otherwise there is a possibility that the files are not written to the backup media completely and correctly, and cannot be recovered in the future, which would be a disaster. Also, when backing up your data, make sure you don't delete the wrong files, and the date on the server must be correct, otherwise there is a chance that some files may be deleted by mistake because of the incorrect date. It is also important to keep the backup location relatively safe, away from fire and flooding, and to install alarm equipment. At the same time, the data must be backed up in multiple locations, so that even if there is a major disaster such as a house collapse or an earthquake in the area, the data will not be destroyed. Of course, some terrorist attacks can also cause data loss, so be sure to pay attention to the security of your backup data. Finally, it is better to keep our data in a hidden place to prevent it from being stolen and causing huge loss. 3. About Data Recovery Once our data has a problem, we must be able to recover the data at the first time. This means that not only do we need to get the backup data data at the first time when the data problem occurs, but we also need to be able to recover this data at the first time. This may require tools, devices and software such as boot disks, boot CDs, drivers, etc. to ensure that the system can boot up properly and be able to recover the data properly. Also, ...... >>

Question 9: How to become a data-driven enterprise From a market perspective intelligent enterprises are dynamic not static, regardless of the enterprise's strategy, people and processes are constantly changing with the market and customer changes. At the same time, this change is not sudden and uncontrollable, but natural and smooth. The success model of an intelligent enterprise is not exclusive to one employee or one department of the organization, but belongs to the entire organization. When there is a good idea or practice in the enterprise, it can be quickly cloned through the information neural network of the enterprise.

Constructing an intelligent enterprise

Personnel awareness The development of enterprises can not be separated from the people, people are the most flexible in an enterprise is the most critical factor. Therefore, the first step in building a commanding enterprise is to start from the human point of view. Build intelligent enterprise to let the staff have a sense of the overall situation, sense of collaboration, learning and innovation. Employees only have a sense of the overall situation is not to the existence of localism, but also to be able to consider from the company's overall perspective to put forward good suggestions. Employees only have a sense of collaboration enterprise strategy and system can be fully implemented. Enterprises only have a sense of learning, the ability of the enterprise can continue to grow. Only with a sense of innovation can a company take the lead in the competition.

IT tools In today's information society, the development of enterprises certainly can not be separated from the support of IT tools. This is especially true for smart companies.

First of all, there is the wisdom of the intelligent enterprise. I often say that a person is very wise, very smart, that the wisdom of the enterprise from where it comes. There are two main aspects of this one is the internal knowledge of the enterprise, this part of the knowledge mainly exists in the employee's brain. The other is the external knowledge of the enterprise, which mainly comes from industry organizations and competitors. This brings us to the knowledge acquisition and knowledge ****sharing in knowledge management. Knowledge acquisition is to transform external knowledge into internal knowledge, and the process of knowledge **** enjoyment is to transform the knowledge of employees within the enterprise into the organizational knowledge of the enterprise itself.

Another feature of an intelligent enterprise is efficient collaboration, which depends on the ability to collaborate on workflows, and we can greatly improve the operational efficiency of internal processes if we make our daily processes electronic. At the same time can improve the level of collaboration between employees.

Intelligent enterprises in addition to the process and knowledge transfer and communication in addition to a very important point is the promotion of corporate culture. The promotion of corporate culture in addition to relying on human propaganda, but also to create an atmosphere, so that this atmosphere every day around the employees. Enterprise information portal just shoulder this mission. The portal is like the door of the enterprise, employees go to work every day to pass through the door, if the door to carry the dissemination of corporate culture of this work, the corporate culture in the daily life of employees to be subtle.

System and culture In addition to the dissemination of intelligent enterprises through publicity and IT tools to ensure that, at the same time, also need to be strengthened through the system and culture to continue. Intelligent enterprises emphasize the culture of execution, happy work, learning and creativity.

Question 10: Give an example of what data-driven intelligent software is Data-driven

Definition:

A data-driven organization acquires, processes, and uses data in a timely manner

to create efficiencies, iterates and develops new products, and navigates through data.

There are a number of ways to assess whether an organization is data-driven, such as:

1. the amount of data generated

2. the extent to which data is used

3. the process of internalizing the data

The authors believe that using the data effectively is critical.

Business companies have a history of using data to improve effectiveness.

Any good salesperson by nature knows how to recommend purchases to consumers.

Those customers who viewed those items likewise viewed something else ......Amazon moved the technology online.

This simple implementation of collaborative filtering is one of the many features of Amazon.

It's a powerful mechanism for machinations outside of traditional search.

Data products are the heart of social networking sites. Their data is necessarily a huge set of user data that forms a graph. Perhaps the most important product for social networks is some kind of tool that helps users link to each other. Any new user needs to find new pals, acquaintances or contacts. Making users search for their friends is not a good user experience. Like LinkedIn, engineers invented People You May Know (PYMK) to solve this problem. It is indeed easy to accomplish this in theory, and based on the relationship graph that already exists, we can accurately discover the relationship network of new users. It is more efficient to recommend friends in this way than to choose them yourself. Despite its current novelty, PYMK has become a necessary part of every social networking site. not only does Facebook support its own version of PYMK, they also monitor how long it takes for users to acquire friends. Using sophisticated tracking and analytics, they've identified the amount of time and connections that keep a user engaged over time.

If you're slow to link to some friends or add friends, you're not going to be a user who relies on the social network for the long term.

By learning the layers of activity that lead to trust, they have designed the site to effectively reduce the time it takes for a newcomer to add a certain number of friends.

Similarly, the Netflix online movie business accomplishes the same task. When you sign up, they strongly recommend that you add the movie you intend to watch. Their data set has found that once you add more than a certain number of movies, the odds of you becoming a long-term subscriber increase dramatically. With this data, Netflix can construct, test, and monitor product streams to maximize the number of newcomers who turn into long-term customers. They've streamlined their highly optimized signup/trial service to effectively use information like this to quickly and efficiently stick with customers.

Netflix, LinkedIn and Facebook are not alone in using user data to encourage long-term customer engagement. Zynga, for example, doesn't just focus on games, but also routinely monitors user identities and their behavior, generating an incredible amount of big data. By analyzing user interactions in a game over a period of time, they've identified those characteristics that directly lead to successful play. Based on the number of interactions with other users, the number of houses the user built in the previous n days, the number of monsters they killed in the previous m hours, and so on, they were able to tell the change in the probability that the user would become a long-term member. They have found the key points on how to reach the challenge of participation and have designed the product to encourage users to reach these goals. Through ongoing testing and monitoring, they have optimized their understanding of these key points.

Google and Amazon were pioneers in using A/B testing to optimize the presentation of web pages. Throughout the history of the Internet, designers have relied on intuition and instinct to get the job done. There's nothing wrong with that, but if you make a change to a page, you need to make sure that the change is effective. Are you selling more products? How long did it take users to discover what they wanted? How many users gave up and moved on to other sites? These are questions that can only be done with the help of experimentation, collecting and analyzing data, which are the second characteristic of a data-driven company.

Yahoo has made many important contributions to data science. After seeing Google use MapReduce to analyze massive amounts of data, they recognized their need for similar tools to accomplish their own affairs This is Hadoop, now one of the single most important tools for data scientists.Hadoop has been developed by ...... >>