Categories of Data Models
Introduction
In a database management system (DBMS), a data model is a concept of tools designed to condense the database's description. Data models give us an open picture of the data, which facilitates the creation of a real database. It demonstrates everything from the data's design to its appropriate application.
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<p>Organizations can set objectives, benchmarks, and baselines using quality data to continue progressing. Data must be arranged using data semantics, data description, and consistency requirements for this measurement to be possible. This abstract model, known as a data model, enables the establishment of links between data objects and the subsequent construction of conceptual models.Even if an organization has a sizable data repository, it will be useless if there is no standard to guarantee the data's fundamental accuracy and interpretability. Adequate data modeling ensures that downstream results can be put into practice, that the optimal tools for accessing the data are known, and that best practices are followed.</p>
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In software engineering, data modeling is the practice of using specific formal techniques to simplify a software system's diagram or data model. It entails using language and symbols to represent data and information. The data model provides the blueprint for creating a new database or reengineering outdated applications.
Given the preceding, it is the first crucial step in determining the structure of accessible data. Data modeling is the process of describing data linkages and limitations through the creation of data models, which are then coded for reuse. It uses language, symbols, or graphs to conceptually express facts and visualize the relationship. Thus, data modeling contributes to improved security, rules, semantics, and naming consistency. Thus, data analytics is enhanced. The focus is on the necessity of data availability and organization, regardless of how it is used.
Importance of Data Modeling
It should be obvious by now that data modeling is essential preparatory work. It facilitates data storage in databases and has a favorable effect on data analytics. It is also essential for data intelligence, data governance, and data management.
- It entails improved data source documentation, better quality, a more defined scope of data use, faster performance, and fewer errors.
- Data modelling guarantees that an organization complies with relevant industry standards and governmental laws, as viewed through the lens of regulatory compliance.
- It gives staff members the authority to develop plans and decisions based on facts.
- It improves upon business intelligence by enabling the discovery of fresh prospects through the expansion of data capabilities.
Types of Data Modelling based on their Abstraction Levels
It can be broadly categorized into three types:
- Conceptual Data Model
- Representational Data Model
- Physical Data Model
1. Conceptual Data Model
The conceptual data model is helpful in understanding the needs and requirements of the database since it provides a high-level description of the database. This approach is employed during the requirement-gathering phase, which is to say, prior to the Database Designers beginning to create a specific database. The entity/relationship model (ER model) is one such well-liked model. With regard to this idea, stakeholders and users who are not technical or who do not understand computer science can nevertheless have a conversation and have their needs recognized.
Model of Entity-Relationship (ER): A high-level data model is employed to specify the data and their relationships. It is essentially a conceptual design for any database that makes creating a data view simple.
Elements of the ER Model:
- Entity: A real-world thing is referred to as an entity. It could be a class, name, location, or item. In an ER Diagram, these are shown by a rectangle.
- Attributes: An entity's description is what constitutes an attribute. In an ER Diagram, these are represented by an ellipse. For a student, it could be their age, roll number, or marks.
- Relationship: Relationships are what establish the connections between various entities. Rhombuses and diamonds represent relationships.
Qualities of an Abstract Data Model
- Provides coverage of the business concepts for the entire organization.
- Data models of this kind are created with a corporate audience in mind.
- The conceptual model is created without regard to software or hardware requirements, such as DBMS vendor and technology, or data storage capacity or location. Representing data as a user would see it in the "real world" is the main goal.
Domain models and conceptual data models establish fundamental concepts and scope that all stakeholders can use to develop a shared vocabulary.
2. Representational Data Model
This kind of data model just represents the database's logical structure; it does not represent the database's physical structure. We are able to concentrate mostly on the database's design thanks to the representative data model. A relational model is a common type of representational model. Relational Calculus and Relational Algebra make up the Relational Model. Tables are essentially used in the Relational Model to represent our data and the relationships among them. It is a theoretical idea that is put into practice using a physical data model.
One benefit of employing a representational data model is that it offers a framework upon which the physical model may be built.
3. Physical Data Model
The Relational Data Model is realistically implemented using the Physical Data Model. In the end, every piece of data within a database is physically kept on discs and tapes or other secondary storage devices. Files, records, and a few more types of data structures are used to store this. It contains all the details on the file formats, database structures, external data structures, and how they relate to one another. In this case, tables are essentially saved in memory for effective access. Better work on the relational model is necessary before we can develop a good physical model. Relational algebra is realistically implemented using Structured Query Language (SQL).
This data model explains WHY a particular DBMS system would be used to implement the system. Developers and DBAs usually construct this model. The goal is the actual database implementation.
Features of a physical data model include:
- Depending on the scope of the project, the physical data model may be combined with other physical data models, but it explains the data requirements for a specific project or application.
- The relationships in the data model between the tables address the cardinality and nullability of the linkages.
- Designed for a particular DBMS version, site, data storage capacity, or project-specific technology.
- Default values, length assignments, and precise datatypes should all be present in columns.
- Defined are views, authorizations, access profiles, indexes, primary and foreign keys, etc.
Data Modeling Examples and Applications
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1. Hierarchical Model
One of the earliest data models is the hierarchical model, which IBM created in the 1950s. Data are seen as a group of tables or as segments that make up a hierarchical relation in a hierarchical model. This shows the data arranged in a structure resembling a tree, with numerous children and one parent record for each record. The immediate structure can be a fan structure with several branches, even if the segments are linked together logically to form a chain-like structure. The irrational relationships are referred to as directed associations.
2. Network Model
In the 1960s, the Database Task Group formalized the Network Model. The hierarchical model is generalized in this model. There is a logical relationship between the segments that belong to any level, even if this model can have several parent segments. These segments are categorized as levels. Most of the time, any two segments have a many-to-many logical relationship.
3. Object-Oriented Data Model
The term "object" in the Object-Oriented Data Model refers to a single structure that contains data and their relationships. This represents real-world issues as objects with various properties. Every object has several connections with other objects. In essence, it is a hybrid of a relational database model with object-oriented programming.
4. Float Data Model
In essence, the float data model is made up of a two-dimensional array of data models without any duplicate parts. There is one limitation with this data model: the tables cannot be too large in order to hold a lot of data.
5. Context Data Model
The Context data model is a collection of multiple data models. The Context Data Model, for instance, includes the ER Model and the Object-Oriented Data Model, among others. Users of this model can perform multiple tasks that are possible for each particular data model.
6. Semi-Structured Data Model
Semi-structured data models handle the information in an adaptable manner. Certain entities can have more qualities than others and vice versa. Basically, this is a flexible place to express data.
Advantages of Data Model
- The primary objective of designing a data model is to ensure that the data objects provided by the functional team are appropriately represented.
- The data model must have sufficient detail to be applied when constructing the actual database.
- The data in the data model can be used to define table relationships, primary and foreign keys, and stored procedures.
- Data models facilitate communication between businesses both inside and between organizations.
- Data models aid in the documentation of data mappings during the ETL process.
- Assist in identifying reliable data sources to fill the model.
Disadvantages of Data Model
- It requires knowledge of the physical properties of data storage to build.
- This navigation system generates sophisticated application development and administration, necessitating an understanding of the biographical truth.
- Even minor structural changes necessitate changing the application as a whole.
- DBMSs do not have a standard language for manipulating data.