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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
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Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
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Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
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Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
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8 Ways to Improve Accuracy of Machine Learning Models
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Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Functional Dependency in DBMS

K
K.C. Sabreena Basheer Last Updated : 01 Jul, 2024
3 min read

Introduction

It is important for anybody working in the field of data science to know how databases work. My name is Sabreena. Functional dependency is one of the most basic concepts to understand when it comes to database management. It helps in the design, normalization, and optimization of relational databases, which makes it pivotal for database management systems (DBMS). In this article, we will delve deep into what functional dependency is, how it works, and what role it plays in DBMS. We will also learn about Armstrong’s Axioms and ways to identify these dependencies.

Functional Dependency in DBMS | Armstrong's Axioms

Overview

  • Understand what functional dependency is in DBMS.
  • Get to know the different types of functional dependencies.
  • Understand the importance of functional dependencies in database design.
  • Familiarize with Armstrong’s Axioms.
  • Learn the methods of identifying functional dependencies.

Table of Contents

What is Functional Dependency in DBMS?

A functional dependency is a relationship between two sets of attributes in a relational database. Generally speaking, if a set of attributes X can uniquely determine another set of attributes Y, we say that Y is functionally dependent on X. This is denoted as X → Y.

For example, consider a Student table with the following attributes: StudentID, StudentName, Course, and Instructor. If each course is taught by only one instructor, then the attribute Course can determine the attribute Instructor.

Course→Instructor\text{Course} \rightarrow \text{Instructor}Course→Instructor

This means if we know the course, we can determine the instructor uniquely.

Importance of Functional Dependencies in Database Design

Functional dependencies are essential for database normalization. They aim to reduce redundancy and improve data integrity in databases. They also prevent anomalies during database updation. More importantly, they help in identifying the correct schema design by ensuring that each attribute is stored in the appropriate table.

Functional dependencies in database design

Types of Functional Dependencies

There are 3 types of functional dependencies in DBMS.

1. Trivial Functional Dependency

A functional dependency X → Y is trivial if Y is a subset of X.

Example: StudentID, Course→Course\text{StudentID, Course} \rightarrow \text{Course}StudentID, Course→Course

2. Non-Trivial Functional Dependency

A functional dependency X → Y is non-trivial if Y is not a subset of X.

Example: Course→Instructor\text{Course} \rightarrow \text{Instructor}Course→Instructor

3. Completely Non-Trivial Functional Dependency

A functional dependency X → Y is completely non-trivial if X and Y have no attributes in common.

Example: StudentID→StudentName\text{StudentID} \rightarrow \text{StudentName}StudentID→StudentName

What are Armstrong’s Axioms?

Armstrong’s Axioms are a set of rules used to infer all the functional dependencies on a relational database. They are:

  • Reflexivity: If Y is a subset of X, then X → Y.
  • Augmentation: If X → Y, then XZ → YZ for any Z.
  • Transitivity: If X → Y and Y → Z, then X → Z.

Additional derived rules include:

  • Union: If X → Y and X → Z, then X → YZ.
  • Decomposition: If X → YZ, then X → Y and X → Z.
  • Pseudo Transitivity: If X → Y and YZ → W, then XZ → W.

How to Identify Functional Dependencies?

To identify functional dependencies, you can:

  1. Analyze Data: Look for patterns and relationships in sample data.
  2. Understand Business Rules: Comprehend the business rules and constraints governing the data.
  3. Consult Documentation: Use ER diagrams and schema definitions to identify potential dependencies.
  4. Use SQL Queries: Write queries to check if certain attributes consistently determine others.

Conclusion

Functional dependencies are a must for effective database design and the normalization of relational databases. Data scientists and database administrators can ensure data integrity, reduce redundancy, and optimize database performance by applying them. The process of identifying and managing these dependencies becomes more systematic with tools like Armstrong’s Axioms. In short, whatever you have learned in this article about functional databases can help you create an efficient and reliable database system.

Frequently Asked Questions

Q1. What is a functional dependency in DBMS?

A. A functional dependency is a constraint between two sets of attributes in a database where one set can uniquely determine another.

Q2. Why are functional dependencies important in database design?

A. They are crucial for normalization, which reduces redundancy and ensures data integrity.

Q3. What are Armstrong’s Axioms?

A. Armstrong’s Axioms are a set of rules used to infer all functional dependencies in a relational database.

Q4. What is an example of a functional dependency?

A. In a Student table, Course → Instructor means that each course is taught by a single instructor. This is an example of a functional dependency.

Q5. How can you identify functional dependencies in a database?

A. By analyzing data, understanding business rules, consulting documentation, and using SQL queries.

BeginnerdatabasedatabasesDBMSdependenciesGuidenormalization
K
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passioante about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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