| Journal of Information and Communications Technology:

Algorithms, Systems and Applications

Received: 01 May 2026; Revised: 11 June 2026; Accepted: 15 June 2026; Published Online: 23 June 2026.

J. Inf. Commun. Technol. Algorithms Syst. Appl., 2026, 2(2), 26307 | Volume 2 Issue 2 (June 2026) | DOI: https://doi.org/10.64189/ict.26307

This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)

SeatMatrix: A Cloud-Based Intelligent Examination

Management System with Real-Time Seat Mapping

Kashish Reshamwala,

1,*

Savitri Chougule

and Mohd. Shafi Pathan

Department of Computer Science & Engineering, MIT Art, Design and Technology University, Pune, Maharashtra, 412201, India

Anjuman-I-Islam M. H. Saboo Siddik College of Engineering, Mumbai, Maharashtra, 400008, India

*Email: kash7405@gmail.com (Kashish Reshamwala)

Abstract

Managing examination seating in academic institutions remains a complex and error-prone process, often

relying on manual planning or static digital tools that lack flexibility and real-time accessibility. These traditional

approaches frequently result in inefficient seat allocation, student confusion, and increased administrative

workload. To address these limitations, this paper proposes SeatMatrix, a hybrid cloud-enabled examination

management system designed to automate seat allocation while providing real-time, visually intuitive seating

information. The concept of SeatMatrix is inspired by modern movie theatre booking systems, where users can

easily identify and select seats through interactive layouts. Adapting this idea to the academic domain, the

proposed system introduces an intelligent seat mapping mechanism that generates structured, conflict-free

seating arrangements and presents them through graphical representations of examination halls. Unlike

existing systems that primarily rely on text-based outputs, SeatMatrix integrates dynamic visualization,

enabling students to quickly locate their assigned positions and reducing congestion during examinations. The

system leverages a Python-based backend, a Streamlit web interface, and a Firebase Realtime Database to

ensure synchronized, multi-user access and instant data updates. A rule-based allocation engine ensures

fairness and eliminates duplicate assignments, while the visualization module enhances usability through

spatial seat mapping. Experimental evaluations demonstrate that the proposed system significantly improves

operational efficiency, reduces manual effort, and enhances the overall student experience. By combining cloud

scalability, real-time interaction, and visual intelligence, SeatMatrix presents a modern and practical solution

for transforming examination management processes in educational institutions.

Keywords: Automated seat allocation; Cloud-based systems; Examination management, Hybrid cloud computing;

Real -Time systems; Seat mapping; Visualization.

1. Introduction

The management of examination processes in academic institutions is a critical administrative task that directly

impacts operational efficiency and student experience. Among these processes, the arrangement of seating for

examinations remains one of the most challenging and error-prone activities. Traditionally, institutions rely on

manual methods or basic spreadsheet-based systems to allocate seats, which often leads to inefficiencies such

as duplicate assignments, improper utilization of space, and increased administrative workload. These

limitations have been consistently highlighted in existing studies on examination seating systems.

[1,2]

In recent

years, several attempts have been made to automate examination hall allocation and seating arrangements

using rule-based or semi-automated approaches. While such systems reduce manual effort to some extent, they

largely depend on static data processing and generate text-based outputs, making it difficult for students to

interpret their seating positions effectively.

[3]

Furthermore, many of these solutions do not provide real-time

updates or interactive interfaces, which are essential in dynamic academic environments where last-minute

changes are common.

[4]

With the rapid advancement of digital technologies, particularly cloud computing, there

is a growing opportunity to modernize examination management systems. Cloud-based systems enable

centralized data storage, scalability, and real-time synchronization across multiple users and devices.

[5]

According to the definition provided by the National Institute of Standards and Technology (NIST), cloud

computing allows on-demand network access to shared computing resources, which can be rapidly provisioned

with minimal management effort.

[6]

This paradigm is especially beneficial in educational institutions where

multiple stakeholders including administrators, staff, and students require simultaneous access to updated

information.

Streamlit, a Python-based web application framework, has emerged as a powerful tool for building interactive

and data-driven interfaces with minimal development effort.

[7]

It allows developers to create responsive

dashboards and visualization modules, making it particularly suitable for applications that require dynamic

data representation. In the context of examination systems, an intuitive interface can greatly simplify the

process of accessing and understanding seating arrangements. Despite these technological advancements, most

existing examination management systems still lack effective visualization mechanisms. Many solutions present

seating information in the form of lists or tables, which can be difficult for students to interpret, especially in

large examination halls. Visualization techniques, on the other hand, provide a spatial representation of seating

layouts, enabling users to quickly identify their positions and navigate the environment more efficiently.

[8]

According to Burke and Petrovic, information visualization have demonstrated that graphical representations

significantly improve comprehension and reduce cognitive load compared to textual data.

[9]

To address these

limitations, this research proposes SeatMatrix, a hybrid cloud-enabled examination management system that

integrates automated seat allocation with real-time data access and graphical visualization. The concept of

SeatMatrix is inspired by modern movie theatre booking systems, where users can view and select seats through

interactive layouts. By adapting this approach to examination management, the system introduces a visual seat-

mapping module that allows students to easily locate their assigned seats within a classroom layout. Unlike

existing systems that focus primarily on automation or scheduling, SeatMatrix combines multiple features into

a unified platform. It incorporates a rule-based allocation engine to ensure conflict-free seat assignments, a

cloud-based data layer for real-time synchronization, and a visualization module for intuitive seat

representation. The system is designed to support multiple user roles, including administrators, staff, and

students, each with dedicated functionalities to streamline examination workflows. The primary contribution

of this research is the development of a unified examination management platform that combines automated

seat allocation, cloud-based data synchronization, and graphical seat visualization. Unlike conventional

examination systems that focus only on scheduling or seat assignment, the proposed SeatMatrix framework

integrates backend automation with user-centric visualization, thereby improving both administrative

efficiency and student accessibility. The proposed approach offers several advantages over traditional and

existing digital systems. First, it significantly reduces manual effort by automating the seat allocation process.

Second, it enhances transparency and accessibility by providing real-time updates through a cloud-based

platform. Third, it improves the overall student experience by introducing graphical seat maps that simplify

navigation within examination halls. Finally, the system is scalable and adaptable, making it suitable for

institutions of varying sizes and requirements.

2. Literature survey

The automation of examination management systems has been an active area of research, particularly focusing

on seating arrangement, hall allocation, and scheduling optimization. Early studies primarily addressed the

challenge of manual seat planning by introducing basic computerized solutions. For instance, systems proposed

in focused on automating seat allocation using predefined rules and structured datasets, significantly reducing

manual workload.

[1,2]

However, these systems were largely limited to generating static seating lists without

incorporating user-friendly interfaces or visualization capabilities. Subsequent research introduced web-based

solutions to improve accessibility and usability. The work presented in Subhashini et al., developed an online

examination seating system that allowed administrators to manage seating data digitally.

[3]

Although this

approach improved efficiency, it still relied heavily on textual outputs, making it difficult for students to interpret

their seating positions within large examination halls. Similarly, the system in integrated SMS notifications to

inform students about their seating details, enhancing communication but lacking real-time interaction and

graphical representation.

[4]

To address optimization challenges, researchers explored algorithmic approaches such as genetic algorithms

and heuristic techniques. Dener et al., study demonstrated the effectiveness of genetic algorithms in solving

large-scale exam scheduling problems by optimizing seat distribution and minimizing conflicts.

[5]

While these

methods improved allocation efficiency, they were primarily focused on backend computation and did not

consider user experience or visualization aspects. With the emergence of cloud computing technologies, more

scalable and distributed solutions were developed. The system proposed in Savakar et al., study utilized cloud

infrastructure to manage examination seating data, enabling centralized storage and improved accessibility.

[6]

Similarly, works in Sangeetha et al., and Onyedeke et al., introduced automated hall allocation systems that

leveraged digital platforms to streamline administrative processes.

[7,10]

Despite these advancements, most of

these systems lacked real-time synchronization and interactive interfaces, limiting their practical usability in

dynamic environments. Further developments in examination management systems emphasized integrated

platforms that combine multiple functionalities. The Online Hall Allocation System (OHAS) presented in

improved scheduling efficiency by optimizing resource utilization across examination centers.

[6]

Additionally,

earlier automation efforts in Burke et al., study laid the foundation for digitizing examination processes but did

not incorporate modern technologies such as cloud-based synchronization or visualization modules.

[9]

Beyond

domain-specific systems, broader research in timetabling and scheduling has contributed valuable insights.

Babaei et al., and George et al., studies explored various optimization techniques for academic scheduling,

including integer programming and heuristic methods.

[11,12]

Another important dimension is data visualization.

Research in Shneiderman et al., Ware et al., Heer et al., studies demonstrates that graphical representations

significantly enhance user comprehension compared to traditional text-based outputs.

[13-15]

Despite this, most

existing examination systems fail to integrate effective visualization techniques, resulting in poor user

experience, especially for students navigating large examination halls.

Over time, examination management systems have evolved across several key parameters, as illustrated below:

• Manual → Automated Systems: Early systems focused on replacing manual processes with basic automation.

[1,2]

• Static → Web-Based Platforms: Introduction of online systems improved accessibility but remained largely

static.

[3,4]

• Basic Logic → Optimization Algorithms: Advanced techniques such as genetic algorithms improved

allocation efficiency.

[5,11]

• Standalone → Cloud-Based Systems: Cloud integration enabled scalability and centralized data management.

[6]

• Text-Based → Visual Interfaces: Recent trends emphasize graphical visualization for better usability.

[13,14]

• Offline → Real-Time Systems: Modern systems require real-time synchronization for dynamic updates.

[16]

Despite significant progress in automating examination systems, several critical gaps remain:

1. Lack of Visualization: Most existing systems rely on textual seating lists, which are difficult to interpret and

do not provide spatial context.

[3,4]

2. Limited Real-Time Capabilities: Many solutions do not support real-time updates, leading to inconsistencies

when changes occur during examination preparation.

[

6,16]

3. Fragmented System Design: Existing approaches often focus on isolated functionalities such as scheduling

or allocation without integrating them into a unified platform.

[5,8]

4. Poor User Experience: Minimal emphasis on user interface design results in systems that are not intuitive

for students or staff.

5. Lack of Inspiration from Modern Systems: Current solutions do not leverage proven interaction models (e.g.,

movie theatre seat visualization), which could significantly improve usability.

To address the limitations identified in existing examination management solutions, the proposed SeatMatrix

framework integrates automated seat allocation, cloud-based data management, real-time synchronization, and

graphical visualization within a unified platform. By combining these capabilities, the system aims to improve

administrative efficiency while providing an enhanced user experience for students and examination staff.

Inspired by modern seat-booking applications, SeatMatrix introduces an intuitive graphical seat-mapping

mechanism that simplifies seat identification and navigation. Recent studies have highlighted the growing

importance of cloud-based technologies in educational management systems. Sikarwar et al. conducted a

comprehensive review of cloud-based learning management systems and reported that cloud infrastructures

significantly improve scalability, accessibility, collaboration, and centralized data management in educational

environments.

[17]

Their findings emphasize the role of cloud computing in supporting large-scale academic

operations while reducing infrastructure constraints. However, the study primarily focused on learning

management environments and did not address examination-specific allocation and visualization challenges.

The adoption of cloud technologies has also influenced the development of digital examination platforms. Singh

and Mansotra proposed a cloud-based architecture for online examination management and demonstrated how

cloud infrastructures can reduce operational costs, improve accessibility, and support dynamic resource

allocation.

[18]

Their work established the feasibility of cloud-enabled examination services but concentrated

mainly on examination delivery rather than seat allocation and classroom visualization.

Modern educational management systems increasingly emphasize centralized information management and

real-time data accessibility. UNESCO's work on next-generation educational management information systems

highlighted the importance of integrated digital platforms capable of supporting data sharing, administrative

coordination, and informed decision-making across educational institutions.

[19]

These systems improve

organizational efficiency but generally focus on institutional data management rather than examination seating

operations. Recent research has further demonstrated the value of intelligent educational platforms that

combine data analytics and user-centered interfaces. Zhang et al. proposed an advanced learning management

system capable of extracting meaningful insights from student data while improving system usability and

decision support.

[20]

Their findings reinforce the importance of integrating analytics and interactive interfaces

into educational applications. Nevertheless, the proposed framework does not address examination hall

management, seat allocation, or graphical classroom visualization. In addition, studies in information

visualization consistently demonstrate that graphical interfaces improve user comprehension, reduce cognitive

effort, and support faster decision-making when compared with purely text-based information presentation.

Visual representations are particularly beneficial in environments involving spatial information, where users

must quickly identify locations and interpret complex layouts. These findings provide strong support for

incorporating graphical seat-mapping mechanisms into examination management systems. The review of

recent literature indicates that existing solutions have achieved considerable progress in cloud-based

deployment, educational information management, and digital examination services. However, automated seat

allocation, real-time synchronization, and graphical seat visualization are often implemented as separate

functionalities. Very limited research has addressed the integration of these components within a single

examination management platform. This research gap motivates the development of SeatMatrix, which

combines automated seat allocation, cloud-based synchronization, and graphical seat visualization to enhance

both operational efficiency and user experience. A comparative analysis of existing examination management

systems and the proposed SeatMatrix framework is presented in Table 1, highlighting the key differences in

automation, cloud integration, visualization, scalability, and real-time functionality.

Table 1: Comparison of existing systems.

Ref.

System

Automated

Allocation

Cloud

Support

Real-

Time

Updates

Visualization

Multi-

User

Support

Scalability

Major

Limitation

[1]

Basic Seating

System

Yes

Low

Static

output

[3]

Online

Seating

System

Yes

Partial

Limited

Partial

Medium

Text-based

interface

[4]

SMS-Based

System

Yes

Limited

Medium

No graphical

view

[5]

Genetic

Algorithm

Based

Yes

High

Backend-

focused

[6]

Cloud-Based

Seating

System

Yes

Limited

Partial

High

Limited user

interaction

[7]

Automated

Hall

Allocation

Yes

Partial

Medium

visualization

Proposed

SeatMatrix

Yes

High

None

3. Proposed architecture

The overall architecture of the proposed SeatMatrix system is illustrated in Fig. 1. The architecture

demonstrates the interaction between the user interface, application modules, seat allocation engine,

visualization layer, and cloud database components. The system has been developed using a combination of

modern programming and database technologies:

• Python 3.11 - Implementation & Backend Logic

• Streamlit to build the web-driven user interface

• Firebase Realtime Database: For data synchronization and storing on the cloud.

• Pandas for handling Excel input

• Matplotlib for generating seat visualization maps

• JSON files that store metadata of classroom layouts

The operational workflow of SeatMatrix is shown in Fig. 2, illustrating the interaction among administrators,

staff members, students, and the automated seat allocation process. Key source files include:

• Seating_dashboard.py – handles login, seat assignment, and student information display

• Seat_visualizer.py – generates graphical layouts of classroom seating

• Classrooms.json - defines seating structures like rows and columns.

• Firebase_admin.py – handles the communication and authentication with Firebase

4. System overview

The platform consists of five major components that work together to support end-to-end examination

management:

Fig. 1: Proposed Architecture.

Fig. 2: System workflow.

4.1 Administrator interface

This module provides facilities to upload student lists, arrange classroom layouts, and generate seating plans. It

simplifies administrative work by reducing manual tasks and introduces order into managing examination

logistics.

4.2 Allocation engine

Deterministic seat assignment logic inside the engine will avoid duplicate placements in ensuring an optimized

manner of distribution, orderly assignment inside, and conflict-resolution

techniques for consistency and fair play while allocating the seats.

4.3 Firebase data management layer

This module keeps all user dashboards and the database in real-time sync. This ensures that whenever an admin

or staff makes some modifications, it reflects instantly on all dashboards to manage exams smoothly and in sync.

4.4 Student portal

Students can log in securely to get their seating information such as seat number, hall name, and details about

the examination. The seating arrangement diagram is also provided within the portal for easy familiarization

by the students.

4.5 Visualization module

The visualization component uses Matplotlib to create classroom seat maps. It emphasizes the student's

assigned seat and shows the surrounding seating context for a clear, user-friendly visual representation of the

room.

5. Methodology

The proposed system, SeatMatrix, follows a structured and modular methodology to automate examination seat

allocation while ensuring fairness, efficiency, and real-time accessibility. The methodology integrates data

preprocessing, constraint-based allocation, and visualization into a unified workflow.

The overall workflow consists of four sequential stages:

1. Data Acquisition and Preprocessing

2. Classroom Configuration

3. Intelligent Seat Allocation

4. Visualization and Data Retrieval

Each stage is designed to operate independently while maintaining seamless integration through a centralized

cloud database.

5.1 Data acquisition and preprocessing

The process begins with the collection of student data, which includes attributes such as student ID, subject,

examination date, and session. This data is uploaded in structured formats (e.g., Excel files) and processed using

data-handling libraries.

Let the student dataset be defined as:

S={



,



,



,…,



} (1)

where each 



represents an individual student record.

Preprocessing ensures:

• Removal of duplicate entries

• Validation of required fields

• Grouping of students based on examination parameters

5.2 Classroom configuration model

Each examination hall is modeled as a grid-based structure defined by rows and columns.

Let a classroom be represented as:

C = (R,K)

where:

  number of rows

  number of columns

Total seating capacity is given by:

Cap = R x K

The system stores classroom configurations in structured JSON format, enabling flexibility in modifying layouts

dynamically.

5.3 Seat allocation algorithm

The core of the system is a deterministic allocation algorithm designed to assign seats without duplication while

maintaining an ordered distribution.

1) Problem definition

Given:

• A set of students 

• A set of classrooms 











Objective:

• Assign each student a unique seat such that:

• No seat is assigned to more than one student

• Classroom capacity constraints are satisfied

2) Allocation function

The seat allocation can be defined as a mapping function:

  󰇛󰇜 (2)

Where,

= classroom

= row index

= column index

Seat allocation can be formally represented as a one-to-one mapping function:

A : S → P

where S represents the set of students and P represents the set of available seats.

The allocation must satisfy the following constraints:

1. Uniqueness Constraint

A(si) ≠ A(sj), ∀ i ≠ j

ensuring that no two students are assigned the same seat.

2. Capacity Constraint

|S| ≤ Σ Capk

where Capk denotes the capacity of classroom k.

3. Validity Constraint

A(si) ∈ P

ensuring that every assigned seat belongs to a valid classroom layout.

The optimization objective is to maximize seat utilization while maintaining conflict-free allocation:

Maximize U = Assigned Seats / Total Available Seats

3) Algorithm steps

Step 1: Sort students based on predefined criteria (e.g., subject or registration number).

Step 2: Iterate through classrooms sequentially.

Step 3: For each classroom, assign students in row-major order:

󰇛󰇜  󰇡

󰇵





󰇶



 󰇢 (3)

where is the index of the student in the ordered list.

Step 4: Ensure constraint satisfaction:

If capacity is exceeded, move to the next classroom. Avoid duplicate assignments by maintaining a tracking

structure

Step 5: Store allocation results in the cloud database

4) Conflict avoidance

To prevent allocation conflicts, a constraint function is applied:

󰇛







󰇜  

 if seat is available

 otherwise

(4)

Only seats satisfying 󰇛







󰇜  are assigned.

5) Real-Time data synchronization

The system employs a cloud-based NoSQL database to maintain synchronization across all users. Any update in

seating allocation is immediately propagated to connected clients.

Let the system state be represented as:

󰇛󰇜 (5)

where denotes time. Updates follow:

󰇛  󰇜  󰇛󰇜   (6)

ensuring consistency across all interfaces.

6) Visualization model

The visualization module converts allocation data into a graphical representation of classroom layouts. Each

seat is mapped to a coordinate:

 

󰇝

󰇛



󰇜

󰇞

(7)

where:

 corresponds to row position

 corresponds to column position

The assigned student seat is highlighted distinctly, enabling easy identification. This approach improves

usability and reduces confusion during examinations.

7) Complexity analysis

The time complexity of the allocation algorithm is:

󰇛󰇜 (8)

where  is the number of students, since each student is assigned a seat exactly once.

Space complexity is also linear due to storage of allocation mappings.

The linear time complexity O(n) demonstrates that the proposed allocation algorithm scales efficiently with

increasing numbers of students, making it suitable for deployment in large academic institutions conducting

examinations for thousands of candidates simultaneously. The detailed time and space complexity analysis of

the proposed allocation algorithm is summarized in Table 2.

Time complexity:-

Step 1: Sort students → O(n log n)

Step 2: Seat assignment → O(n)

Step 3: Store allocations → O(n)

Overall:

T(n) = O(n log n) + O(n) + O(n)

T(n) = O(n log n)

Space Complexity:-

Student records = O(n)

Seat mapping = O(n)

Tracking structure = O(n)

Space Complexity = O(n)

Table 2: Time and space complexity analysis of SeatMatrix.

Operation

Complexity

Student Sorting

O(n log n)

Seat Allocation

O(n)

Database Storage

O(n)

Overall Time Complexity

O(n log n)

Space Complexity

O(n)

8) Summary of methodology

The proposed methodology combines:

• Structured data processing

• Constraint-based seat allocation

• Real-time cloud synchronization

• Graphical visualization

This integrated approach ensures that the system is scalable, efficient, and user-friendly. A comparison between

the capabilities of existing systems and the proposed SeatMatrix framework is provided in Table 3,

demonstrating the advantages of the proposed solution.

Table 3: Comparison between existing system and SeatMatrix.

Feature

Existing Systems

SeatMatrix

Seat Allocation

Semi/Rule-based

Fully automated & optimized

Real-Time Updates

Not supported

Fully supported

Visualization

Text-based output

Graphical seat mapping

User Interaction

Minimal

Interactive dashboards

Multi-User Access

Restricted

Admin, Staff, Students

Scalability

Limited

Highly scalable

6. Results

To evaluate system performance, A test cases was executed:

6.1 Login page

The login interface of SeatMatrix is shown in Fig. 3. The system supports secure authentication for

administrators, staff members, and students through a unified access portal.

Fig. 3: Login interface supporting admin, staff, and student authentication.

Fig. 4: Classroom management dashboard for creating and managing examination halls.

6.2 Admin classroom management page

Fig. 4 presents the classroom management dashboard used by administrators to create examination halls,

define seating layouts, and manage classroom configurations.

6.3 Staff panel (Upload excel / Generate seating)

The staff dashboard shown in Fig. 5 enables examination personnel to upload student records, configure

examination details, and automatically generate seating arrangements.

Fig. 5: Staff interface for student data upload and automated seat allocation.

Fig. 6: Student dashboard displaying assigned seat through graphical visualization.

Student Dashboard: (Visual Seat Map), Fig. 6 illustrates the student dashboard, where users can view their

examination details and identify their assigned seat through an interactive graphical seat map. The

characteristics of the dataset and experimental environment used for system evaluation are summarized in

Table 4.

Table 4: Experimental dataset description.

Parameter

Value

Total students

100

Number of classrooms

Seats per classroom

Total capacity

196

Number of experimental

runs

Database platform

Firebase Realtime Database

Interface framework

Streamlit

7. Statistical performance analysis

To quantitatively evaluate the effectiveness of SeatMatrix, the proposed system was compared with

conventional examination seating management approaches across six performance criteria. Scores were

assigned on a ten-point scale based on functional capability, responsiveness, automation level, and usability

characteristics. The comparative evaluation results are presented in Table 5.

Table 5: Statistical comparison of existing examination management systems and SeatMatrix.

Parameter

Existing Systems

SeatMatrix

Improvement (%)

Automation

50.0

Cloud Support

80.0

Real-Time

Synchronization

125.0

Visualization

200.0

Scalability

80.0

Usability

80.0

The average score achieved by existing systems was 4.67, whereas SeatMatrix achieved an average score of 9.00.

This represents an overall improvement of 92.7%. The highest performance gain was observed in visualization

capability, where the proposed graphical seat-mapping approach improved effectiveness by 200% compared

with traditional text-based seating systems. Real-time synchronization also showed substantial improvement

due to the integration of Firebase Realtime Database, enabling instant propagation of seating updates across all

connected users. The standard deviation of SeatMatrix scores was found to be considerably lower than that of

existing systems, indicating consistent performance across multiple evaluation parameters. The results

demonstrate that the proposed framework not only enhances automation but also provides a balanced

improvement in usability, scalability, and user interaction. These findings validate the effectiveness of

combining cloud computing, automated seat allocation, and graphical visualization within a unified examination

management platform.

8. Experimental validation

To validate the effectiveness of SeatMatrix, experiments were conducted using examination datasets of varying

sizes. The evaluation focused on seat allocation accuracy, processing time, and system scalability. Three datasets

containing 100, 500, and 1000 student records were used. Each dataset was processed five times under identical

conditions, and the average execution time was recorded. The experimental performance results are

summarized in Table 6.

Table 6: Performance evaluation results of the proposed SeatMatrix system.

Number of Students

Allocation Time (s)

Allocation Accuracy (%)

0.12

100

0.46

100

0.91

100

The results indicate that processing time increases gradually with the number of students, while allocation

accuracy remains at 100%. No duplicate seat assignments or capacity violations were observed during testing.

These findings demonstrate that the proposed system maintains high reliability and scalability even when

handling large examination datasets.

To assess the consistency of the proposed system, multiple experimental runs were conducted for each dataset

size. The statistical results obtained from these repeated executions are summarized in Table 7. The observed

standard deviation values were low across all test cases, indicating stable and repeatable allocation

performance. The results demonstrate that the proposed allocation mechanism produces consistent execution

times while maintaining 100% seat allocation accuracy without conflicts or duplicate assignments.

Table 7: Statistical analysis of seat allocation performance.

Students

Run 1

Run 2

Run 3

Run 4

Run 5

Mean Time (s)

Standard Deviation

0.11

0.12

0.13

0.12

0.007

100

0.44

0.46

0.47

0.45

0.48

0.46

0.015

9. Discussion

The system identifies remarkable advancements in three distinct operational areas:

A. Operational Efficiency – Automation takes manual seat planning out of the separate planning process thus,

weekly seat plans can be provided quickly and with a reduction in administrative burden.

B. Student Experience – Visual seat maps help to clarify seat assignments and reduce interactions with staff,

especially during the busy exam period students must be told quickly where to sit on an exam day.

C. Scalability – Being cloud-driven is advantageous to expansion truly and adopting meta-data room

configuration principals for travel makes the operation of the system easy to expand across multiple

departments, buildings, or wrestling with large populations of students.

Fig. 7 visually compares the performance of SeatMatrix with conventional examination management systems

across key evaluation parameters, demonstrating the improvements achieved by the proposed framework.

Fig. 7: Comparison of existing examination systems and the proposed SeatMatrix.

10. Conclusion

This paper presented SeatMatrix, a cloud-based intelligent examination management system designed to

automate seat allocation, provide real-time synchronization, and offer graphical seat visualization for students

and administrators. The proposed framework integrates a rule-based allocation engine, Firebase Realtime

Database, Streamlit-based interfaces, and visualization modules into a unified platform for examination

management. The experimental evaluation demonstrated that the system successfully generated conflict-free

seating arrangements with 100% allocation accuracy across different dataset sizes. Statistical analysis further

indicated significant improvements in automation, usability, visualization, scalability, and real-time accessibility

when compared with traditional examination seating approaches. In addition, the computational complexity

analysis showed that the proposed allocation mechanism operates efficiently with linear space requirements

and O(n log n) time complexity, making it suitable for large-scale academic environments. By combining cloud

computing, automated allocation, and interactive visualization, SeatMatrix addresses several limitations of

existing examination management systems, including manual effort, lack of real-time updates, and poor user

interaction. The proposed solution enhances operational efficiency while improving the overall examination

experience for students and staff. Future work may focus on integrating QR-code-based examination entry

verification, advanced security mechanisms, subject-wise separation constraints, predictive classroom

utilization analytics, and dedicated mobile applications to further improve accessibility and functionality.

CRediT Author Contribution Statement

Kashish Reshamwala: Conceptualization, Data Curation, Experimental Evaluation, Formal Analysis,

Investigation, Methodology, Software, Validation, Visualization, Writing – Original draft, Writing – Review &

editing. Savitri Chougule: Methodology, Supervision, Writing – Review & editing. Mohd. Shafi Pathan:

Supervision, Validation, Writing – Review & editing. All authors have read and agreed to the published version

of the manuscript.

Funding Declaration

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-

profit sectors.

Data Availability Statement

The data used to support the findings of this study consist of examination seating datasets, classroom

configuration records, and experimental evaluation data generated during the development and testing of the

proposed SeatMatrix system. The datasets are not publicly available as they contain institution-specific

information used for research validation. However, anonymized data and additional implementation details can

be made available by the corresponding author upon reasonable request for academic and research purposes.

The source code, classroom layout configurations, and system implementation details described in this paper

are available from the authors upon request, subject to institutional policies and research usage guidelines.

Conflict of Interest

There is no conflict of interest.

Artificial Intelligence (AI) Use Disclosure

The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,

grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical

content, experimental implementation, results, and interpretations were independently developed and verified

by the authors.

Supporting Information

No Applicable.

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