This document discusses different types of data models, including object based models like entity relationship and object oriented models, physical models that describe how data is stored, and record based logical models. It specifically mentions hierarchical, network, and relational models as examples of record based logical data models. The purpose of data models is to represent and make data understandable by specifying rules for database construction, allowed data operations, and integrity.
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
Data Warehouse : Dimensional Model: Snowflake Schema In the snowflake schema, dimension are present in a normalized from in multiple related tables.
The snowflake structure materialized when the dimensions of a star schema are detailed and highly structured, having several levels of relationship, and the child tables have multiple parent table.
An Introduction to Architecture of Object Oriented Database Management System and how it differs from RDBMS means Relational Database Management System
Know different types of tips about Importance of dataware housing, Data Cleansing and Extracting etc . For more details visit: http://www.skylinecollege.com/business-analytics-course
Data Warehouse : Dimensional Model: Snowflake Schema In the snowflake schema, dimension are present in a normalized from in multiple related tables.
The snowflake structure materialized when the dimensions of a star schema are detailed and highly structured, having several levels of relationship, and the child tables have multiple parent table.
An Introduction to Architecture of Object Oriented Database Management System and how it differs from RDBMS means Relational Database Management System
Know different types of tips about Importance of dataware housing, Data Cleansing and Extracting etc . For more details visit: http://www.skylinecollege.com/business-analytics-course
A database model refers to the structure of a database and determines how the data within the database can be organized and manipulated. Let’s explore some common types of database models:
Relational Model: The most popular example, the relational model, organizes data into tables (also known as relations). Each table contains rows representing records and columns representing attributes. Relationships between tables are established using keys.
Hierarchical Model: Developed by IBM for IMS (Information Management System), this model arranges data in a tree-like structure. Each record is a tree node, and relationships follow a one-to-many pattern. It’s predictable and efficient for data access.
Network Model: This model allows many-to-many relationships between records. It’s more flexible than the hierarchical model but less common.
Entity–Relationship Model (ER Model): It represents entities, their attributes, and the relationships between them. ER diagrams visually depict these components.
Object Model: Used in object-oriented databases, it treats data as objects with properties and methods. It’s suitable for complex data structures.
Document Model: Commonly used in NoSQL databases, it stores data as documents (e.g., JSON or XML). Each document can have varying attributes.
Entity–Attribute–Value (EAV) Model: A flexible model where data is stored in a sparse matrix. It’s useful for handling dynamic attributes.
Star Schema: Primarily used in data warehousing, it simplifies complex data structures into a central fact table connected to dimension tables.
Metadata: Towards Machine-Enabled Intelligence dannyijwest
World Wide Web has revolutionized the means of data availability, but with its current structure model , it
is becoming increasingly difficult to retrieve relevant information, with reasonable precision and recall,
using the major search engines. However, with use of metadata, combined with the use of improved
searching techniques, helps to enhance relevant information retrieval .The design of structured,
descriptions of Web resources enables greater search precision and a more accurate relevance ranking of
retrieved information .One such efforts towards standardization is , Dublin Core standard, which has been
developed as Metadata Standard and also other standards which enhances retrieval of a wide range of
information resources. This paper discuses the importance of metadata, various metadata schemas and
elements, and the need of standardization of Metadata. This paper further discusses how the metadata can
be generated using various tools which assist intelligent agents for efficient retrieval.
Metadata: Towards Machine-Enabled Intelligencedannyijwest
World Wide Web has revolutionized the means of data availability, but with its current structure model , it is becoming increasingly difficult to retrieve relevant information, with reasonable precision and recall, using the major search engines. However, with use of metadata, combined with the use of improved searching techniques, helps to enhance relevant information retrieval .The design of structured, descriptions of Web resources enables greater search precision and a more accurate relevance ranking of retrieved information .One such efforts towards standardization is , Dublin Core standard, which has been developed as Metadata Standard and also other standards which enhances retrieval of a wide range of information resources. This paper discuses the importance of metadata, various metadata schemas and elements, and the need of standardization of Metadata. This paper further discusses how the metadata can be generated using various tools which assist intelligent agents for efficient retrieval
Student POST Database processing models showcase the logical s.docxorlandov3
Student POST:
Database processing models showcase the logical structure of a database. The most commonly used model is the Relational database model that sorts the data in a table that consist of rows and columns. The column holds the attributes of the entity and rows hold the data of a particular instance of the entities. The major advantage of the Relational model is that it is in the table form and hence easier for users to understand, manage and work with the data. And, with the primary key and foreign key concepts, the data can be uniquely identified, stored in different entities and retrieved effectively with the relationships. The other advantage is that with the relational model, SQL language can be used to work with the data which is simple to understand and most widely used. The disadvantage of relational model could be the financial cost that is higher in comparison as the specific software needs to be in place and the regular maintenance needs to be performed that requires highly skilled manpower. And, the complexity of the database can be further increased when the volume of the data keep in increasing. Also, there is the limitation in the length of fields stored as different data types in relational model (Joseph & Paul, 2009).
The other processing model is the Object-oriented model that depicts database as the collection of objects. The advantage of this model is that it is compatible to work with complex data sets with the use of Object IDs and object-oriented programming. It’s disadvantage is that object databases are not commonly used and the complexity can hamper the performance of database. The other type of database model is the Entity-Relationship model which is mostly used for the conceptual design of database. It pictures the entities, several attributes that falls within the domain of that entity and the cardinality of relationship between them. It’s advantage is that the E-R diagram is easily understandable by the users at the first glance and thus can effectively work with the data in no time and can point out the discrepancies in the data. The other advantage is that it can be easily converted to other models if required by the business. The disadvantage of Entity-Relationship is that the industry standard notations for the diagram is not defined and thus can create confusion to the users. This model is only suitable for high-level database design (S.J.D.,2020).
2Nd Student POST :
Database models or commonly referred to as schemas help represent the structure of a database and its format which is run by a DBMS. Database model uses vary depending on user specifications.
Types of database models
1.
Network model
This network model uses a structure similar to that of a hierarchical model. The model permits multiple parents, which is a tree-like structure model. This model emphasizes two basic concepts; records and sets. Records hold file hierarchy and sets define the many-to-many relationship .
2. MODEL
A Model is representation of reality, ’real world’
objects and events and their associations.
A database model is an organizing principle that
specifies particular mechanism of data storage and
retrieval.
The model explains, in terms of services available to
an interfacing application, how to access a data
element when other related elements are known.
Maninder Kaur www.eazynotes.com 2
3. Components of Data Models
Structure Part:
Consisting of set of rules according to which databases
can be constructed.
Manipulative Part:
Define the types of operation that are allowed on the
data.
Set of Integrity Rules:
Which ensures that data is accurate.
Maninder Kaur www.eazynotes.com 3
4. Purpose of Data Model
To represent data.
To make the data understandable.
Maninder Kaur www.eazynotes.com 4
5. Types of Data Models
Object Based Data Models
Physical Data Models
Record Based Logical Data Models
Maninder Kaur www.eazynotes.com 5
6. Object Based Data Models
It use concepts such as entities, attributes and
relationships.
Types of Object Based Data Models:
* Entity Relationship
* Object Oriented
* Semantic
*Functional
Maninder Kaur www.eazynotes.com 6
7. Physical Data Models
It describe how data is stored in the
computer, representing information such as
record structures, record ordering and access
paths.
Less no. of models are there.
Maninder Kaur www.eazynotes.com 7
8. Record Based Logical Data Models
o It is used to specify the overall logical structure
of the database and to provide a higher-level
description of the implementation.
o Structured database in fixed formats.
Maninder Kaur www.eazynotes.com 8
9. Types of Record Based Data Models
Hierarchical Model
Network Model
Relational Model
Maninder Kaur www.eazynotes.com 9
10. Types of Record Based Data Models
Hierarchical Model
Network Model
Relational Model
Maninder Kaur www.eazynotes.com 9