The process of establishing database entities and their relationships in visio or erwin after transforming the conceptual model abstracted from system analysis into a physical model? (Entities are generally tables).
baike.baidu/view/ 1452242
Question 2: What does it mean to build a data-based analysis model? This is the general idea of data analysis.
However, before the analytical model is usually established, there are generally pre-assumptions. For example, I assume that the education, work experience, salary and age of salespeople will have an impact on their sales.
Then I will collect data according to my hypothesis, and then analyze the data to find a suitable data model, such as linear regression if it is a linear model, and establish a corresponding nonlinear model if it is a nonlinear model. Then through model creation, we can verify which assumptions are correct and find out the influence of influencing factors.
Question 3: What is a data model? Data is a symbolic record that describes things. A model is an abstraction of the real world. Data model is the abstraction of data characteristics and the framework of database management teaching form.
The description of data model includes three parts: data structure, data operation and data constraint.
1) data structure: The data structure in the data model mainly describes the types, contents, attributes and relationships of data. Data structure is the basis of data model, and data operations and constraints are based on data structure. Different data structures have different operations and constraints.
2) Data operation: The data operation in the data model mainly describes the operation type and operation mode on the corresponding data structure.
3) Data constraints: Data constraints in the data model mainly describe the syntax, semantic relations, constraints and dependencies between data in the data structure, as well as the rules of data dynamic change, so as to ensure the correctness, validity and compatibility of data.
According to different application levels, data models can be divided into three types: conceptual data model, logical data model and physical data model.
1. Conceptual data model: referred to as conceptual model, it is a model for database users to know the world. It is mainly used to describe the conceptual structure of the world. It enables database designers to get rid of the specific technical problems of computer system and DBMS at the initial stage of design and concentrate on analyzing the relationship between data, which has nothing to do with the specific database management system (DBMS). Conceptual data model must be replaced by logical data model in order to be realized in DBMS.
2. Logical Data Model: referred to as data model for short, this is the model that users see from the database and the data model supported by a specific DBMS, such as network data model and hierarchical data model. The model should be user-oriented and system-oriented, mainly used for the implementation of database management system (DBMS).
3. Physical Data Model: referred to as physical model for short, it is a model for computer physical representation, describing the organizational structure of data on storage media, which is not only related to the specific DBMS, but also related to the operating system and hardware. Each logical data model has a corresponding physical data model when it is implemented. In order to ensure the independence and portability of DBMS, most physical data models are automatically implemented, while designers only design special structures such as index and aggregation.
The most commonly used conceptual data models are E-R model, extended E-R model, object-oriented model and predicate model. The most commonly used logical data types are hierarchical model, grid model and relational model.
Question 4: What is the meaning of data model? Why to build a data model is an abstraction of the real world. In database technology, the model representing entity types and the relationship between entity types is called "data model". Data model is a teaching framework of database management, which is used to describe the concept and definition of a group of data, including three aspects: 1, conceptual data model: this is number-oriented. ...
Question 5: What does modeling mean? Classification is mathematical modeling or three-dimensional modeling.
Personal understanding of mathematical modeling is to establish relationships: for example, a function sum (a) = a * a;
The function can be a model, with input a and output a * a
Three-dimensional modeling is to make some virtual and visible models, such as a virtual cup and a table. ....
Question 6: What does mathematical modeling mean? Mathematical model is the mathematical expression of practical problems. Specifically, a mathematical model is an abstract and simplified mathematical structure of some real worlds for a certain purpose. More precisely, a mathematical model is a mathematical structure obtained by simplifying some necessary assumptions and using appropriate mathematical tools according to its unique internal laws for specific objects and specific goals. Mathematical structures can be mathematical formulas, algorithms, tables, charts, etc. Mathematical modeling is to establish a mathematical model, and the process of establishing a mathematical model is the process of mathematical modeling (see the flow chart of mathematical modeling process). Mathematical modeling is a mathematical thinking method, and it is a powerful mathematical means to describe and solve practical problems by using mathematical language and methods through abstraction and simplification.
Question 7: What is data modeling? What are the advantages and disadvantages of data modeling? The elements in the virtual database can indeed be referenced. One advantage of this is that it can reduce the difficulty of development, because developers can develop without knowing the data structure, which also improves the development efficiency in disguise. The second is to separate the business layer from the physical layer, that is, demand and storage, which makes the system architecture more readable and reasonable.
Disadvantages, because there is one more layer, there will be one more layer of analysis when the system is running, which will reduce the speed of the system in theory, but it has little effect in practice. Secondly, this method is suitable for the development of large and medium-sized systems, and it is inevitably redundant for small systems with simple data structure and only a few database tables.
Post a picture of the data modeling I am doing.
Question What is modeling in java? Modeling 1, using computers to describe the behavior of a system. For example, spreadsheet programs can be used to process financial data and represent company behavior; Make a business plan; Evaluate the possible impact of the company's business changes. See simulation, spreadsheet program. Use users to describe the behavior of the system. For example, spreadsheet programs can be used to process financial data representing company activities; Make business forecast; Or evaluate the impact of the proposed changes on the company's operations.
2. Use computers to describe objects and their spatial relationships mathematically. For example, a computer-aided design (CAD) program can generate objects on the screen, use equations to generate lines and shapes, and accurately place them according to their relationship with each other and their two-dimensional or three-dimensional space.
3. Application and data modeling is the process of determining, recording and realizing the data and process requirements of the application. This includes looking at existing data models and processes to determine whether they can be reused, and creating new data models and processes to meet the unique needs of applications.
The main activities in the modeling process include:
Determine the data and its related processes (in fact, the salesperson needs to check the online product catalog and submit a new customer order).
Define data (such as data type, size and default value).
Ensure data integrity (using business rules and validation checks).
Define operating procedures (such as security check and backup).
Select a data storage technology (such as relational, hierarchical or indexed storage technology).
It is important to know that modeling usually involves the management of a company in unexpected ways. For example, when new ideas emerge about which data elements should be maintained by which organizations, data ownership (and the implied responsibility for data maintenance, accuracy and timeliness) is often questioned. Data design often makes companies realize how interdependent enterprise data systems are, and encourages companies to seize the efficiency improvement, cost saving and strategic opportunities brought by collaborative data planning.
At the end of modeling, you have completely defined the requirements of the application, identified the data and services that may be reused by other enterprise applications, and laid a solid foundation for future expansion.