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Yes, two schemas can have some data items in common. This typically occurs when they share similar entities or attributes, allowing for overlap in the data they represent. For example, both schemas may include a "Customer" entity with attributes like "CustomerID" and "Name." However, the overall structure and other data items in the schemas may differ based on their specific purposes.
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What is the defemition of common attribute?
A common attribute refers to a characteristic or quality that is shared by multiple entities or items within a given context. In data management and analysis, it often denotes a specific property that can be used to group or categorize similar items, facilitating comparisons or relationships among them. For example, in a dataset of students, "age" could be a common attribute as it applies to all individuals in that dataset.
What data is used in systematics that stresses both common ancestry and the amount of change observed among groups?
The data that is used in systematics that stresses both the common ancestry and the amount of change that is observed among groups is cladistic. Cladistic is the classification in which items are grouped together.
Find the mean of the data?
The mean of a set of data is the sum of that data divided by the number of items of data.
What is the quoten found by diving the sum of a set of data by the number of items of daa?
The mean or average is the quotient found by dividing the sum of a set of data by the number of items of data.
What are three items used for collecting data?
Three common items used for collecting data are surveys, which gather information through questionnaires; sensors, which collect quantitative data from the environment, such as temperature or motion; and interviews, which provide qualitative insights through direct conversations with participants. Each of these tools serves to capture different types of information, tailored to the specific needs of the research.
Why are data schemas important?
Data schemas are important because they define the structure and organization of the data, ensuring consistency, accuracy, and integrity. They help in understanding the relationships between different data elements and provide a blueprint for how data is stored and accessed within a database or system. Properly designed data schemas also promote data quality, facilitate data integration, and support efficient querying and analysis.
Explain the difference of internal external and conceptual schemas how are these schema layers related to the concept of logical and physical data independence?
External schemas allows data access to be customized (and authorized) at the level of individual users or groups of users. Conceptual (logical) schemas describes all the data that is actually stored in the database. While there are several views for a given database, there is exactly one conceptual schema to all users. Internal (physical) schemas summarize how the relations described in the conceptual schema are actually stored on disk (or other physical media). External schemas provide logical data independence, while conceptual schemas offer physical data independence.
What schemas do you use in Multidimensional Modelling?
In Multidimensional Modelling, common schemas used are Star Schema and Snowflake Schema. Star Schema involves a central fact table connected to multiple dimension tables, while Snowflake Schema normalizes the dimension tables by further breaking them down into sub-dimension tables. These schemas help organize data hierarchically for efficient querying and analysis in multidimensional databases.
How are the internal external and conceptual schemas related to concept of logical and physical data independence?
The internal schema represents the physical storage structure of data, the external schema represents how different users view the data, and the conceptual schema defines the logical structure of the entire database. Logical data independence means that the conceptual schema can change without affecting the external schemas, while physical data independence means that changes in the physical storage structures do not affect the conceptual or external schemas.
Description of database subschema?
What are the purpose of developing a sub-schema in database? In database management, the Subschema pronounced "sub-skee-mah." is an individual user's partial view of the database while the schema is the entire database. It is the applications programmer's view of the data within the database pertinent to the specific application. A subschema has access to those areas, set types, record types, data items, and data aggregates of interest in the pertinent application to which it was designed. Naturally, a software system usually has more than one programmer assigned and includes more than one application. This means there are usually many different sub schemas for each schema. The following are a few of the many reasons sub schemas are used: # Sub schemas provide different views of the data to the user and the programmer, who do not need to know all the data contained in the entire database. # Sub schemas enhance security factors and prohibit data compromise. # Sub schemas aid the DBA while assuring data integrity. Each data item included in the subschema will be assigned a location in the user working area (UWA). The UWA is conceptually a loading and unloading zone, where all data provided by the DBMS in response to a CALL for data is delivered. It is also where all data to be picked up by the DBMS must be placed.
What Logical data independence and physical data?
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.
The collection of data The sum of the data items divided by the number of data items?
The Mean
What is meant by Actuate Encyclopedia Volume and what is its significance?
The volume is where your reports are stored. You need to understand that a volume can also contain many "folders" which are tied to separate database schemas. Think of an encyclopedia volume as a reports database, and the folders as database schemas, and you begin to understand how Actuate is organizing your reports, metadata, and Actuate system data. -C The volume is where your reports are stored. You need to understand that a volume can also contain many "folders" which are tied to separate database schemas. Think of an encyclopedia volume as a reports database, and the folders as database schemas, and you begin to understand how Actuate is organizing your reports, metadata, and Actuate system data. -C
How data is represented in dbms?
Data is represented/organized in a dbms in the form of Schemas, tables, rows and columns One DBMS may have multiple Schemas One Schema may have multiple tables One table may have multiple rows One row may have multiple columns If these tables are related to one another it forms a RDBMS - A Relational DBMS
What is the defemition of common attribute?
A common attribute refers to a characteristic or quality that is shared by multiple entities or items within a given context. In data management and analysis, it often denotes a specific property that can be used to group or categorize similar items, facilitating comparisons or relationships among them. For example, in a dataset of students, "age" could be a common attribute as it applies to all individuals in that dataset.
What is a collection of unprocessed items such as text numbers and images?
A collection of unprocessed items is known as data.
What is Schema overlap?
Schema overlap refers to the degree to which different data schemas share common elements or structures. It occurs when multiple databases or datasets have similar attributes, fields, or relationships, facilitating interoperability and data integration. Understanding schema overlap is crucial for tasks like data merging, migration, or analytics, as it helps identify compatible data sources and reduces redundancy. By leveraging schema overlap, organizations can enhance their data management and analysis capabilities.
