Physical abstraction in data abstraction refers to hiding the implementation details of how data is stored and accessed. It allows programmers to work with high-level concepts of data without needing to understand the underlying physical structures. This separation enhances modularity and simplifies the complexity of software systems.
No, data does not have mass. Data is information stored electronically and does not have physical weight like a physical object.
In data science, the keyword "physical information" refers to data that is directly measurable or observable in the real world. This type of data is crucial for making accurate predictions and drawing meaningful insights from datasets. By incorporating physical information into data analysis, data scientists can better understand patterns, relationships, and trends in the data. This helps in making informed decisions and solving complex problems in various fields such as healthcare, finance, and technology.
Logical data independence refers to the ability to modify the conceptual schema without changing the external schemas or application programs. In contrast, physical data independence allows changes to the internal schema – like indexes and storage structures – without affecting the conceptual or external schemas.
A logical database refers to the conceptual schema or model of data relationships and structures, independent of how data is stored or accessed. On the other hand, a physical database involves the actual implementation of the database on a specific hardware system, detailing how data is stored and accessed. The logical database design focuses on the organization of data, while the physical database design focuses on optimizing performance and storage efficiency.
A logical database refers to the way data is organized, modeled, and accessed by users, focusing on the structure and relationships of data. In contrast, a physical database relates to how data is actually stored on disks, including indexes, partitions, and access paths designed for efficient data retrieval and storage.
how data are stored would be in a physical layer
abstraction is show nonessential data to user ,
Data abstraction is the reduction of a body of data to a simplified representation of the whole. Data abstraction is usually the first step in database design in order to create a simplified framework that can be added to in order to complete the database.
The three levels of data abstraction in a DBMS are physical, logical, and view. Physical level: Describes how data is stored in the database, including details like data storage and access paths. Logical level: Focuses on the structure of the data in the database, including schemas, tables, and relationships. View level: Represents how users view the data, providing a customized and simplified representation of the data to different user groups.
no
health
Abstraction: Abstraction refers to removal/reduction of irrelevant data or unnecessary data or confidential data from a Class. Data hiding: Data hiding is a feature provided by the abstraction for hiding the data from the class.
select*from stludent
Data Abstraction in DBMS means hiding implementation details(i.e. high level details) from end user.e.g. In case of storage of data in database user can only access the database, but implementation details such as how the data is stored phisically onto the disc is hidden from user.
Purpose of database users with an abstract view of the data that is system hides certain details of how the data are stored and maintained. It gives an architecture is to separate the user applications and the physical database.
physical
Data Encapsulation, Abstraction, Inheritance, Polymorphism