The document discusses different data models including hierarchical, network, relational, object-oriented, and object-relational models. It provides details on each model's structure and advantages and disadvantages. It also discusses using the relational model for a database to manage information for the Fly High Airlines, including passenger, payment, and seat information. The relational model is justified as the best fit due to its ability to efficiently query and join table data while ensuring data integrity.
Database systems that were based on the object data model were known originally as object-oriented databases (OODBs).These are mainly used for complex objects
Database normalization is the process of refining the data in accordance with a series of normal forms. This is done to reduce data redundancy and improve data integrity. This process divides large tables into small tables and links them using relationships.
Here is the link of full article: https://www.support.dbagenesis.com/post/database-normalization
● 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).
Database systems that were based on the object data model were known originally as object-oriented databases (OODBs).These are mainly used for complex objects
Database normalization is the process of refining the data in accordance with a series of normal forms. This is done to reduce data redundancy and improve data integrity. This process divides large tables into small tables and links them using relationships.
Here is the link of full article: https://www.support.dbagenesis.com/post/database-normalization
● 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).
The normal forms (NF) of relational database theory provide criteria for determining a table’s degree of vulnerability to logical inconsistencies and anomalies.
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
What is Normalization in Database Management System (DBMS) ?
What is the history of the system of normalization?
Types of Normalizations,
and why this is needed all details in the presentation.
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 .
The normal forms (NF) of relational database theory provide criteria for determining a table’s degree of vulnerability to logical inconsistencies and anomalies.
DDBMS, characteristics, Centralized vs. Distributed Database, Homogeneous DDBMS, Heterogeneous DDBMS, Advantages, Disadvantages, What is parallel database, Data fragmentation, Replication, Distribution Transaction
What is Normalization in Database Management System (DBMS) ?
What is the history of the system of normalization?
Types of Normalizations,
and why this is needed all details in the presentation.
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 .
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]Usman Tariq
In this PPT, you will learn:
• About data modeling and why data models are important
• About the basic data-modeling building blocks
• What business rules are and how they influence database design
• How the major data models evolved
• About emerging alternative data models and the needs they fulfill
• How data models can be classified by their level of abstraction
Author: Carlos Coronel | Steven Morris
DBMS - Database Management System, Introduction, Data and Database, DBMS meaning, Why DBMS?, History of DBMS, Characteristics of DBMS, Types of DBMS- Hierarchical DBMS, Network DBMS, Relational DBMS, Object-oriented DBMS, Applications of DBMS, Popular DBMS Software, Advantages of DBMS, disadvantages of DBMS.
Introduction to Data and Information, database, types of database models, Introduction to DBMS, Difference between file management systems and DBMS, advantages & disadvantages of DBMS, Data warehousing, Data mining, Applications of DBMS, Introduction to MS Access, Create Database, Create Table, Adding Data, Forms in MS Access, Reports in MS Access.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. Content
Introduction to data models
Different data models
Fly High Airlines
Comparison- data models
Justification
Conclusion
2Thakshayini. C J/IT/16/09/01
3. Data
Data are the unique variables of information or
distinct pieces of information to form information
data should process in distinct way.
Data model
• Description of logical structure of database.
• Basic entities to initiate abstraction
• Explains the connections within different dat.
• Express how the data stored.
• Establishment of data process
• Flat model is the very first data model.
3Thakshayini. C J/IT/16/09/01
4. Importance of data models
Data structure available manually or in real world are
quiet complex and hard to retrieve distinct data.
Using data models data structures can be explained in
simple graphical representation.
Understandable representation of data structure. Data
models act as communication tools between end users,
data administrator and others who refers.
4Thakshayini. C J/IT/16/09/01
5. Data models
Types of data models
Network
model
Representation
model
Hierarchical
model
Object
relational
model
Relational
model
Object
oriented
data
model
Thakshayini. C J/IT/16/09/01 5
6. Hierarchical model
One to many relationship
Once built no further changes can be done.
Tree like structure
Advantages
Specification
Only one parent record
type.
Disadvantages
No changes made at the
middle of the process.
Complex problems may
arise.
7. Network model
A system demonstrate is a database display that is outlined as an adaptable way to deal with speaking to
objects and their connections.
An exceptional component of the system demonstrate is its outline, which is seen as a diagram where
relationship composes are circular segments and protest composes are hubs.
Dissimilar to other database models, the system model's diagram isn't restricted to be a cross section or
pecking order; the progressive tree is supplanted by a chart, which takes into account more fundamental
associations with the hubs
Advantages
Basic Concept: Similar to the various leveled demonstrate, this model is
basic and the usage is easy.
Capacity to Manage More Relationship Types: The system show can
oversee balanced (1:1) and additionally many-to-many (N: N) connections.
Simple Access to Data: Accessing the information is less complex when
contrasted with the various leveled show.
8. Advantages(Network model)
Information Independence: Data autonomy is better in arrange models instead of the progressive models
Information Integrity: In a system demonstrate, there's dependably an association between the parent and
the kid fragments since it relies upon the parent-kid relationship
Disadvantages (Network model)
Framework Complexity: Each and every record must be kept up with the assistance of
pointers, which influences the database to structure more intricate.
Utilitarian Flaws: Because an extraordinary number of pointers is basic, inclusion, updates,
and erasure turn out to be more perplexing.
Absence of Structural Independence: An adjustment in structure requests an adjustment in
the application also, which prompts absence of auxiliary autonomy.
9. Object oriented data model
Inspire by object oriented programming languages.
Uses object oriented concepts.
Capable of storing large objects
Binary Large Objects like images, multi media are facilitate.
Advantages
Simplified
Lack constrained between objects.
Reduce the semantic gaps.
Easy to get connect with object oriented programming
languages
10. Disadvantages
Object oriented model
Model is often provided through object oriented
language such as (C++, Java).
Practically very complex and in applicable in many
times.
11. Relational model
Simplifies the database structure (data represented) by the used of tables and columns.
In use popularly amongst industry.
Advantages
Relational Algebra: Relational database supports relational algebra and
relational operations of the set theory
Dynamic views: In relational database model, view is not past of physical
schema it is always dynamic.
Excellent data Security : Support the concept of users rights, meets security
of databases.
Scaling up : Can be scatted up to new hardware technology
Inconsistency & data redundancy are considered as drawbacks
of the model
12. Object relational model
A relational database model that accepts object oriented concepts as well.
Constructs to deal with added data types.
Allow attributes of tuples to have complex types, including non-atomic values such as nested relations
Preserve relational foundations, in particular the declarative access to data, while extending modeling
power
Upward compatibility with existing relational languages.
13. Fly HighAirlines - Database
Managing 25 international & local airplanes.
Assigning seats & arrangements when seat cancellation.
Categorizing seats according to age groups.
The following passenger information should be maintained:
Name
Address
Contact information
NIC NO
Passport No.
Cash or credit payment details should be real time management.
15. Selected model - Justification
Relational database model is best suited model for the Fly High Airlines data base
management system.
An efficient procedure exists for guaranteeing a social database configuration is free of
peculiarities that may affect the trustworthiness and precision of the database.
Strong data typing and validity checks ensure data fall within acceptable ranges, and
required data are present.
Users can query any table in the database, and combine related tables using special join
functions to include relevant data contained in other tables in the results
16. Conclusion
A Database model defines the logical design and structure of a database and defines
how data will be stored, accessed and updated in a database management system
The hierarchy starts from the Root data, and expands like a tree, adding child nodes to
the parent nodes
This model efficiently describes many real-world relationships like index of a book,
recipes etc.
Network model data is organized more like a graph, and are allowed to have more than
one parent node.
Relational model data is organized in two-dimensional tables and the relationship is
maintained by storing a common field
17. 17Thakshayini. C J/IT/16/09/01
References
cs, 2014. Course central. [Online]
Available at: https://www.cs.sfu.ca/CourseCentral/354/zaiane/material/notes/Chapter8/node3.html
[Accessed 16 08 2018].
tutorialspoint, 2016. Tutorials point. [Online]
Available at: https://www.tutorialspoint.com/dbms/dbms_data_independence.htm
[Accessed 12 08 2018].
