1. INTEGRATION OF AI &
DBMS
PRESENTED BY:
UMAR ALI
JAVARIYA NADEEM
AHSAN ASLAM
SAIRA ARSHAD
EBAD UL HAQ
2. INTRODUCTION
• Introduction about AI:
• AI is a set of tools such as algorithms, model
training etc
• Abilities of computers to perform task which
would mostly require human intelligence.
• Field of computer science that created
intelligent machines that can operate
automatically.
• Divided into 2 categories: Narrow AI & General
AI
• Systems design for specific task refers to
Narrow one.
• Systems that can perform wide range or tasks
3. INTRODUCTION
• Introduction about DB:
• Collection of saturated data.
• Stored in a way that makes it accessible &
retrievable.
• Used in wide range of applications from
personal database to large enterprise level
database.
• Many types of databases:
• Relational Database: Based on relational model
& use SQL.
• Distributed Database: Data is stored and
managed across multiple computers in a
network. Having more advantages over
traditional database but with challenges
4. INTRODUCTION
• They will promises to play a significant role in the future of
technology.
• It will helps to improve the technology of DBMS.
• Access to large amount of shared data for knowledge
processing.
• Due to intelligent processing of data, design of intelligent
database is formed.
• Deals with how data will be intelligent.
• AI is more powerful when it will be integrated in Database.
• Enhance the capabilities of Systems.
5. AI DATABASE
• It integrates AI technology.
• Using machine learning algorithms to automate &
optimize.
• Provide extra features than traditional databases.
• It offers full text search & text analytics capabilities.
• It improves overall performance, reduced downtime,
enhanced security.
• Main key point is to reduce operational cost.
6. FEATURES OF AI DATABASE
• Self-Tuning:
• AI database has the ability to automatically tune itself for optimal
performance.
• Self-Securing:
• It automatically detect threats by analyzing usage patterns and irrelevant
activity.
• Predictive Analytics:
• It uses predictive analytics to make predictions about future database
usage patterns.
• Scalability:
• AI database has the ability to automatically tune itself for optimal
performance.
7. WORKING OF AI DATABASE
• Data Collection:
• Collect Data according to usage, response time, resource consuming
• Data Analysis:
• Analyze that Collect Data to identify trends and patterns.
8. WORKING OF AI DATABASE
• Predictive Modeling:
• AI database uses predictive modeling algorithms to create models of
database usage patterns.
• Design making:
• Based on predictions made by the upper step, it decide how to optimize
operations.
9. WORKING OF AI DATABASE
• Self Tuning:
• Adjusts its own settings to optimize performance & enhance operation
of DB.
• Continuous Monitoring:
• It continuous monitors usage patterns to ensure that the predictions are
running smoothly and efficient.
10. WORKING OF AI DATABASE
• Continuous Improvements:
• It continuous improves their decision making process.
• Updating their predictive models.
• Accepting & optimizing new data & update itself
11. ADVANTAGES OF AI DATABASE
• Improved Accuracy:
• AI algorithms can analyze large amounts of data
stored in databases and identify patterns and
relationships that might not be easy for humans.
• Automated Processes:
• AI can be integrated into databases to automate
routine and repetitive tasks, such as data entry,
validation, and cleaning.
• Predictive Analytics:
• AI algorithms can be used to make predictions
about future trends, customer behavior, and other
factors based on data stored in databases.
12. ADVANTAGES OF AI DATABASE
• Improved Performance:
• AI can be used to optimize the performance of
databases by dynamically adjusting the distribution
of data and workload across multiple nodes..
• Enhanced Decision Making:
• By providing real-time data analysis and
recommendations, AI databases can support
informed decision making.
• Cost Saving:
• Automating manual tasks and reducing the need
for manual data entry can result in significant cost
savings.
13. LIMITATIONS OF AI DATABASE
• Data Quality:
• If a user can train model on false data, AI can
generate false result according to data that fed into
the system.
• Human Interpretation:
• AI algorithms often require human interpretation to
understand their results and make decisions based
on them.
• Lack of Transparency:
• The workings of many AI algorithms can be
complex and difficult to understand, making it
difficult to verify their results and ensure they are
14. LIMITATIONS OF AI DATABASE
• Dependence on Algorithms:
• AI databases are dependent on the algorithms used
to analyze data.
• Lack of Creativity:
• AI databases are limited to the data and algorithms
they have been trained on.
• Technical Challenges:
• Implementing and maintaining an AI database can
require significant technical expertise.
15. APPLICATIONS OF AI DATABASE
• Healthcare:
• AI databases are used for medical imaging analysis,
patient data management, and drug discovery.
• Finance:
• AI databases are utilized for fraud detection, risk
management, and customer behavior analysis.
• Retail:
• AI databases are applied for product
recommendations, price optimization, and supply
chain management.
16. APPLICATIONS OF AI DATABASE
• Manufacturing:
• AI databases are used for predictive maintenance,
supply chain optimization, and quality control.
• E-commerce:
• AI databases are applied for personalization,
customer behavior analysis, and fraud detection.
• Energy & Utilities:
• AI databases are utilized for energy consumption
analysis, predictive maintenance, and network
optimization.
17. AI DATABASE & TRADITIONAL DATABASE
• Focus:
• AI databases have a focus on machine learning and artificial intelligence.
traditional databases focus on data storage and retrieval.
• Data type:
• AI databases are designed to handle unstructured data, while traditional uses
structured data like tables and numbers.
• Scalability:
• AI databases are designed to handle large data sets, whereas traditional
databases may struggle with scalability.
18. AI DATABASE & TRADITIONAL DATABASE
• Speed:
• All of us know that the speed of AI Database is much faster than the
traditional DB.
• Cost:
• AI databases may be more expensive than traditional databases due to the
advanced technologies and algorithms used.
• User Skills:
• AI databases may require a higher level of technical skills compared to
traditional databases.
19. INTEGRATION OF AI &
DBMS
SUBMITTED TO:
MS MANAL UMER
THANK YOU
ANY QUESTION
