🎯 Smart Campus Resource & Energy Analytics System

IIIT Dharwad DA264 Project · Full-Stack DBMS with Data Warehouse, ETL Pipeline & Real-Time Analytics

Overview • Features • Architecture • Dashboard • Database • Quick Start • Highlights

📋 Overview

Smart Campus is a production-grade Business Intelligence system that transforms raw campus operational data into real-time, actionable insights. The platform combines:

12 BCNF-normalized OLTP tables capturing live transactions (energy readings, bookings, maintenance, access logs)
Star Schema OLAP warehouse (515+ fact rows, 97.7% data quality) optimized for analytics
Python ETL pipeline with incremental loading, IQR outlier detection, and surrogate key mapping
Real-time React dashboard (9 modules, 28-second auto-refresh) with interactive OLAP operations
Complete DBMS mastery demonstration: normalization proofs, transaction management (ACID/2PL), query optimization, data quality enforcement

Live Data: The system monitors 6 campus buildings, 16 rooms, 27 energy meters, 200+ bookings, 109 users with full operational dashboards.

🎯 Problem Statement & Solution

The Challenge

Campuses generate massive amounts of operational data—energy readings, room bookings, maintenance requests, access logs—but lack a unified system to:

🚫 Track real-time resource utilization across campus
🚫 Detect anomalies (energy spikes, SLA breaches, security risks)
🚫 Analyze historical trends using clean, warehoused data
🚫 Support data-driven campus management

The Solution: Smart Campus

✅ Single source of truth for all campus operations ✅ Real-time dashboards showing energy consumption, room utilization, maintenance status, security events ✅ Historical analytics via star schema data warehouse with OLAP drilldown ✅ Data quality enforcement with IQR-based anomaly detection (97.7% clean) ✅ Production-grade architecture demonstrating enterprise DBMS patterns

✨ Key Features

📊 Overview Dashboard

8 Live KPI Cards (auto-refreshing every 28 seconds):

Total Energy: 87.3 MWh (+4.2% vs last month)
Campus Users: 109 registered (76 students, 15 faculty, 15 staff, 3 admins)
Rooms Tracked: 16 monitored spaces across campus
Total Bookings: 200 approved bookings (+32 this week)
Open Maintenance Tickets: 18 unresolved issues
Access Events: 208 total logged entries
Active Equipment: 65 devices online
Active Meters: 27 energy sensors live

Campus Health Score (82/100) composite metric:

Energy Efficiency: 78 (proximity to consumption baselines)
Maintenance: 69 (SLA compliance: 20% — see maintenance dashboard)
Space Utilization: 85 (booking density vs room capacity)

Energy + Booking Weekly Trend: Dual-axis chart showing correlation between energy demand and room usage across 10 weeks (bars = energy in MWh, line = bookings count).

⚡ Energy Analysis

Dashboard: Room-Level Drilldown

Four KPI cards:

Total kWh: 1,351 kWh (current period)
Est. Cost: ₹11.5K (at ₹8.5/unit, Indian standard)
CO₂ Footprint: 1,108 kg (using 0.82 kg CO₂/kWh factor)
Avg Voltage: 228.4 V (across all 27 active meters)

Room-Level Energy Breakdown (tagged "DRILL-DOWN"): Ranked by consumption with peak multiplier analysis (SQL LAG window function):

Seminar Hall: 311 kWh (100% baseline, peak 1.72×)
L301 Computer Lab: 248 kWh (80% baseline, peak 1.71×)
L201 Computer Lab: 197 kWh (63% baseline)
C202 Classroom: 183 kWh (55% baseline)

Department Energy Roll-Up (tagged "OLAP ROLL-UP"): Using GROUP BY ROLLUP aggregation:

Computer Science & Engineering: 524 kWh
Electronics & Comm: 281 kWh
Data Science & AI: 200 kWh

Room × Hour Energy Heatmap (tagged "IQ MATRIX"): Color grid (8 AM–6 PM, darker red = higher kWh) showing peak usage hours by room location, sourced from OLAP SLICE query.

Building Energy Analysis & Anomalies

Building Energy Roll-Up (from warehouse):

E Block (Academic): 4,820 kWh (highest, 55%)
PI Block (Admin+Research): 1,230 kWh (14%)
B Block (Boys Hostel): 980 kWh (11%)
G Block (Girls Hostel): 860 kWh (10%)
H Block (Gym+Canteen): 540 kWh (6%)
M Block (Sports): 320 kWh (4%) Peak ratio: 58% peak hours (9 AM–12 PM class startup, 3 PM afternoon peak)

Meter Type Distribution (donut):

Electricity: 11 meters (primary)
Solar: 7 meters (renewable)
Water: 4 meters (utility)
Gas: 5 meters (cooking)

24-Hour Energy Profile (line chart): Double-peak pattern:

Morning peak (~9 AM): Class startup, computer labs boot
Afternoon peak (~3 PM): Afternoon sessions, lab usage

Anomaly Detection & Equipment Status

Solar vs Grid Energy — Monthly (6-month bar chart):

Grid (blue): Primary source, ~350–450 kWh/month
Solar (orange): Renewable, ~100–150 kWh/month (23% coverage)
6-Month Solar Generation: 811 kWh total

CO₂ Footprint Trend (area chart):

Total CO₂: 2,238 kg (6-month period)
vs Last Year: -12% improvement (sustainability effort tracking)

Anomaly Detection — LAG Window Analysis (table): Using SQL LAG() to compare each reading against previous reading for same meter:

M-4 (03:00): 8.7 kWh → 9.2 kWh (+3.6 delta) → 🔴 SPIKE DETECTED
M-14 (12:00): 9.5 kWh → 9.5 kWh (+5.6 delta) → 🔴 SPIKE DETECTED
M-9 (16:00): 9.5 kWh → 9.5 kWh (+4.2 delta) → 🔴 SPIKE DETECTED
Other readings: Normal status (grey)

Equipment Status & Asset Registry (donut + bar):

Active: 65 devices (green)
Inactive: 27 devices (grey)
Under Repair: 18 devices (red) Equipment types breakdown by count.

🏢 Infrastructure

Campus Buildings Overview

6 Campus Buildings (each card shows floors, rooms, build year, utilization bar):

PI Block (Admin+Research): 3 floors, 4 rooms, 2019, 68% util
E Block (Academic): 5 floors, 16 rooms, 2020, 91% util — HIGHEST 🔴
M Block (Sports): 2 floors, 42% util
B Block (Boys Hostel): 4 floors, 78% util
G Block (Girls Hostel): 4 floors, 74% util
H Block (Gym+Canteen): 2 floors, 55% util

Room Type Distribution:

6 Computer Labs
6 Classrooms
3 ECE Labs
3 Seminar Halls Total: 16 monitored rooms

Room Capacity Overview (bars):

Total Seats: 1,860 across campus
Average: 116 seats/room
Largest Room: 240 seats (Seminar Hall)

Equipment Analytics & IoT Network

Equipment Analytics (ASSET REGISTRY): Horizontal bar by count + installed power capacity breakdown:

Air Conditioners: 42.0 kW
GPU Servers: 24.0 kW
Solar Inverters: 75.0 kW
Projectors: 5.0 kW
Desktop PCs: 6.3 kW
Other: Additional infrastructure Total Installed Capacity: 165 kW

Smart Sensors Network (4 KPI cards):

27 Energy Meters (Electricity, Solar, Water, Gas monitoring)
16 Access Readers (RFID + Biometric + PIN multi-factor auth)
12 HVAC Sensors (Temperature + Humidity control)
24 IP Cameras (24×7 surveillance, all green online status)

📅 Room Bookings

Booking Status & Trends

Status KPI Cards (from BOOKING table GROUP BY status):

Approved: 7 bookings ✅
Pending: 1 booking ⏳
Cancelled: 1 booking ❌
Rejected: 1 booking ❌
Completed: 2 bookings ✔️

Monthly Booking Trend (6-month grouped bar chart): Approval patterns across 6 months:

Green (Approved) dominates
Amber (Pending) small bars
Red (Rejected) minimal
Grey (Cancelled) rare Shows consistent ~75% approval rate.

Room Booking Status PIVOT Table (tagged "SLICE & DICE"): SQL PIVOT cross-tabulation (rooms × status):

Seminar Hall: 12 total, 57% util
C101: 8 total, 62% util (highest)
L301: 10 total, 49% util
C202: 7 total, 44% util
L201: 9 total, 64% util
L101: 5 total, 36% util

Booking Split (donut): 75% approval rate

Peak Booking Hours (bar chart): Shows demand by hour across day (peak during 10 AM–1 PM lecture hours).

Full Booking Registry

Booking Registry (ALL RECORDS table, 200+ rows): Every booking with:

Date: Apr 7–11 visible
Time: 08:00–09:00, 09:00–10:00, etc.
Room: C101, C201, L201, Seminar Hall, C202, L101, Mini, C104
Department: CSE, ECE, DSAI
Booked By: Dr Rao, Dr Sharma, Dr Mehta, Arjun Kumar, Priya Singh, Rahul Pathi, Gopal Gupta, Neha Verma
Purpose: DBMS Lecture, Signals Class, ML Lab, Project Review, DSA Lecture, ECE Lab Practice, Seminar Presentation, Group Study, Workshop, Morning Briefing, Data Science Lab, Hackathon Prep
Status Badge: Color-coded (Green=Approved, Amber=Pending, Red=Rejected, Grey=Cancelled, Blue=Completed)

🔧 Maintenance

Maintenance Dashboard & SLA Analytics

Status KPI Cards:

Open: 3 unresolved tickets
In Progress: 5 being worked on
Resolved: 2 fixed
Total: 10 tickets

Mean Time To Resolve (MTTR) — SLA Performance (bars per priority): Computed using AVG(DATEDIFF(hour, raised_at, resolved_at)) per priority tier:

Critical: 8.4h avg ⚠️ (target 4h, 4.4h over)
High: 18.2h avg ⚠️ (target 10h, 8.2h over)
Medium: 36.8h avg ⚠️ (target 24h, 12.8h over)
Low: 72.1h avg ⚠️ (target 48h, 24.1h over)

Ticket Aging Analysis (bar chart):

0–7 days (green): Few tickets
8–14 days (amber): Some aging
15–30 days (orange): Moderate aging
>30 days (red): Critical aging, largest bar

Maintenance Ticket Queue & Priority

Maintenance Ticket Queue (LIVE QUEUE tag): Open/in-progress tickets as color-coded cards:

GPU Server overheating in L301 (Critical, In Progress) 🔴
Lights flickering in classroom (Critical, Open) 🔴
Water leakage near server rack in L303 (Critical, In Progress) 🔴

Priority Distribution (bar chart):

Critical: 3 tickets 🔴
High: 4 tickets 🟠
Medium: 2 tickets 🟡
Low: 1 ticket 🟢

SLA Compliance (gauge): 20% (only 2 of 10 tickets resolved within SLA window)

Hotspot Rooms (list): Preventive maintenance priority:

Seminar Hall: 2 tickets
L101: 2 tickets
L301: 1 ticket
C202: 1 ticket
L303: 1 ticket

🔐 Access & Security

Access Security Dashboard

KPI Cards:

Total Entries: 8 logged access attempts
Unique Users: 7 distinct individuals
Avg Duration: 103 minutes average stay
After-Hours: 1 flagged entry ⚠️

Security Risk Score (radial gauge, 87/100): Real-time assessment rewarding multi-factor auth, penalizing after-hours entries, flagging high-frequency sensitive room access.

Auth Methods Split (donut):

RFID: 3 entries
Biometric: 1 entry
PIN: 3 entries
Manual: 1 entry

Most Accessed Rooms (list):

L201: 4 entries (highest)
C301: 1 entry
L101: 1 entry
C201: 1 entry
C102: 1 entry

After-Hours Alerts:

Ravi Desai late-night L201 entry flagged 🚨

Access Log Entries (ANTI-SUIT TRACKING table): Each row: user, room, auth method, entry time, duration (minutes)

Keya Reddy, L201, RFID, 12:31, 155 min
Ankur Shah, L201, Biometric, 12:45, 173 min
Priyanka K., L201, PIN, 16:43, 84 min
Anand Reddy, C301, RFID, 10:31, 71 min
Madhav Nair, L101, Manual, 13:31, 84 min
Ashok Shah, C201, PIN, 12:45, 88 min
Ravi Desai, C102, RFID, 22:21, 88 min 🚨
Panya Desai, L201, PIN, 10:43, 172 min

Peak Access Times (bar chart): 12h noon highlighted orange (high-risk simultaneous-access hour, multiple users entering at once). Other hours blue (normal).

👥 User Analytics

User Analytics Dashboard

Role Distribution KPIs:

Students: 76 (70%) 👨‍🎓
Faculty: 15 (14%) 👨‍🏫
Staff: 15 (14%) 👷
Admins: 3 (3%) 👨‍💼 Total: 109 users

Department-wise Activity (grouped bar chart, OLAP DRILL-DOWN): Comparing bookings, logins, access events across 3 departments via JOIN:

CSE: ~45 bookings, ~60 logins, ~75 access events
ECE: ~35 bookings, ~50 logins, ~60 access events
DSAI: ~30 bookings, ~40 logins, ~50 access events

User Engagement Quadrant (scatter plot): x-axis = bookings, y-axis = access events

Faculty cluster (top-right): High booking, high access
Students (spread): Wide variance in engagement
Staff (left): Typically low-booking, high-access

User Role Distribution (donut): Proportional arcs for all 4 roles.

Most Active Users Leaderboard (ACTIVITY RANKING): Composite score = 0.4×bookings + 0.3×access + 0.3×energy_rank

Dr Rao (Faculty, CSE): 12 bookings, 8 access, score 94 🥇
Dr Sharma (ECE): 10 bookings, 7 access, score 88 🥈
Dr Mehta (DSAI): 9 bookings, 6 access, score 84 🥉
Gopal Gupta (Student, DSAI): 5 bookings, 4 access, score 62
Keya Reddy (Student, CSE): 4 bookings, 9 access, score 71
Ankur Shah (Student, CSE): 4 bookings, 9 access, score 71
Arjun Kumar (Student, CSE): 4 bookings, 3 access, score 58
Ashok Shah (Staff): 3 bookings, 10 access, score 65

📦 Data Warehouse

ETL Pipeline & Run History

Data Warehouse KPI Cards:

Fact Rows: 515 rows loaded into star schema
Dim Rows: 47 dimension records (date, room, meter aggregations)
ETL Runs: 9 successful since Jan 2025
Data Quality: 97.7% (rows passing IQR filter)

ETL Pipeline — OLTP → Data Warehouse (5-step flow):

OLTP Source → Extract new ENERGY_READING rows via watermark
Watermark Check → Avoids duplicates (tracks max date)
IQR Cleaning → Q1−1.5×IQR to Q3+1.5×IQR (removes sensor errors)
Transform → Surrogate key mapping (room/meter → dim keys)
Warehouse Load → Bulk append to fact_energy_consumption

ETL Run History (AUDIT LOG):

ETL-009: 148 extracted, 141 loaded, 14.2s, ✅ Success
ETL-008: 132 extracted, 128 loaded, 12.8s, ✅ Success
ETL-007: 156 extracted, 149 loaded, 15.1s, ✅ Success
ETL-006: 121 extracted, 118 loaded, 11.8s, ✅ Success
ETL-005: 0 extracted, 0 loaded, 2.1s, ✅ "No New Data" (watermark matched)
ETL-004: 163 extracted, 0 loaded, 18.9s, ❌ "Failed" (IQR rejected all rows as outliers)
ETL-003: 144 extracted, 138 loaded, 13.9s, ✅ Success

Star Schema & OLAP Operations

Star Schema Diagram (DATA WAREHOUSE DESIGN):

Central Fact Table: fact_energy_consumption (515 rows)
- Measures: kwh_consumed, peak_flag, avg_voltage
- Foreign keys to 4 dimensions
Dimension Tables:
- dim_date: 365 rows (full 2025 calendar)
- dim_room: 16 rows (all monitored spaces)
- dim_meter: 27 rows (all energy sensors)
- Snowflake extension: dim_building, dim_department (hierarchical normalization)

OLAP Operations Explorer (5 interactive tabs):

Tab 1: Roll-Up

SELECT YEAR(full_date), QUARTER(full_date), MONTH(full_date), SUM(kwh_consumed)
FROM fact_energy_consumption
GROUP BY GROUPING SETS (YEAR(full_date), ...)

Results:

2025-Mar: 524.8 kWh
2025 Total: 1,006.2 kWh
Grand Total: 1,006.2 kWh

Tab 2: Drill-Down Campus → Building → Floor → Room level analytics

Tab 3: Slice Filter year = 2025, show all rooms and meters

Tab 4: Dice Multi-dimensional filter (Q1 2025 + Computer Labs + Electricity meters simultaneously)

Tab 5: Pivot Buildings as columns, months as rows, kWh as values

🔄 Live Operations

Live Operations Center

Real-time data entry console with 28-second auto-refresh countdown and green "Live" indicator.

4 Real-Time Forms (each executes direct SQL INSERT via FastAPI):

Form 1: Log Energy Reading

Meter dropdown (from ENERGY_METER)
kWh Consumed (numeric, e.g., 4.75)
Voltage (V) (pre-filled 230V—Indian standard)
Peak Hour (Yes/No toggle → peak_flag BIT)
Button: "Insert Reading → SQL Server"

Form 2: Create Room Booking

User dropdown
Room dropdown
Start Time (datetime picker)
End Time (datetime picker)
Purpose (free text)
Conflict detection via stored procedure usp_AddBooking
Button: "Submit Booking → SQL Server"

Form 3: Raise Maintenance Ticket

Room dropdown (FK, NOT NULL — weak entity constraint)
Reported By dropdown (USERS)
Priority (Critical/High/Medium/Low)
Description (free text)
Button: "Create Ticket → SQL Server"

Form 4: Log Access Event

User dropdown
Room dropdown
Auth Method (RFID/Biometric/PIN/Manual)
Auto-detected after-hours flag if entry_time > 22:00
Button: "Log Access → SQL Server"

Bonus: Trigger ETL Pipeline

Green "Run ETL Now" button
Executes Python script: watermark → extract → clean → transform → load
Updates Data Warehouse tab with new run

🎛 Normalization & ACID

OLTP vs OLAP Comparison

Metric	OLTP (SmartCampusDB)	OLAP (Warehouse)
Query Type	Short INSERT/UPDATE/SELECT	Complex aggregations
Schema	Normalized 3NF/BCNF	Star / Snowflake (denorm)
Data Volume	Current days/weeks	Historical months/years
Concurrent Users	Many (100s)	Few analysts
Update Frequency	Continuous real-time	Nightly ETL batch
Response Time	Milliseconds	Seconds to minutes
Optimization	Write-optimized	Read-optimized (indexes)
Example Query	`INSERT INTO BOOKING ...`	`GROUP BY GROUPING SETS ...`

Snowflake Extension — Normalized Dimensions:

dim_room references dim_building (1:M)
dim_building references dim_department (1:M)
Drill-down hierarchy: Campus → Dept → Building → Floor → Room

🏗 Architecture Overview

┌─────────────────────────────────────────────────────────────────┐
│              SMART CAMPUS ANALYTICS SYSTEM                       │
├─────────────────────────────────────────────────────────────────┤
│                                                                   │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │             REACT VITE FRONTEND (Port 3000)              │  │
│  │  9 Dashboard Pages (Overview, Energy, Infrastructure,    │  │
│  │  Bookings, Maintenance, Access, Analytics, DW, Ops)     │  │
│  │  Real-time KPI cards • Interactive charts • Forms        │  │
│  │  28-second auto-refresh via JWT-authenticated API        │  │
│  └──────────────────────────────────────────────────────────┘  │
│                             ↓ JWT Auth (HS256)                  │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │            FASTAPI BACKEND (Port 8000)                   │  │
│  │  REST Endpoints • CRUD operations on 12 OLTP tables     │  │
│  │  OLAP warehouse queries (Star + Snowflake)              │  │
│  │  Role-based access control (Admin/Viewer)               │  │
│  │  ETL pipeline trigger endpoint                          │  │
│  └──────────────────────────────────────────────────────────┘  │
│                             ↓ SQL Server Driver (pyodbc)         │
│  ┌────────────────────────────────────────────────────────────┐ │
│  │           SQL SERVER 2022 (OLTP + OLAP)                   │ │
│  │                                                            │ │
│  │  ┌──────────────────────┐  ┌──────────────────────────┐  │ │
│  │  │    OLTP Database     │  │   OLAP Data Warehouse    │  │ │
│  │  │  (12 BCNF Tables)    │  │   (Star + Snowflake)     │  │ │
│  │  │                      │  │                          │  │ │
│  │  │  Live Transactions:  │  │  Dimension Tables (47):  │  │ │
│  │  │  • DEPARTMENT        │  │  • dim_date (365 rows)   │  │ │
│  │  │  • BUILDING, FLOOR   │  │  • dim_room (16 rows)    │  │ │
│  │  │  • ROOM (16)         │  │  • dim_meter (27 rows)   │  │ │
│  │  │  • ENERGY_METER      │  │  • dim_building          │  │ │
│  │  │  • ENERGY_READING    │  │  • dim_department        │  │ │
│  │  │  • USERS (109)       │  │                          │  │ │
│  │  │  • BOOKING (200+)    │  │  Fact Table:             │  │ │
│  │  │  • MAINTENANCE       │  │  • fact_energy_consump.  │  │ │
│  │  │  • ACCESS_LOG        │  │    (515 rows, 97.7% QA)  │  │ │
│  │  │  • AUDIT_TRAIL       │  │                          │  │ │
│  │  │                      │  │  OLAP Operations:        │  │ │
│  │  │  Constraints:        │  │  • Roll-up, Drill-down   │  │ │
│  │  │  • 26 Foreign Keys   │  │  • Slice, Dice, Pivot    │  │ │
│  │  │  • 5 CHECK           │  │  • GROUP BY ROLLUP       │  │ │
│  │  │  • Indexes:          │  │                          │  │ │
│  │  │    (meter_id, ts)    │  │  5 Composite Indexes     │  │ │
│  │  │    (room_id, time)   │  │  Clustered on PKs        │  │ │
│  │  │  • 3 Triggers        │  │                          │  │ │
│  │  │  • 2 Stored Procs    │  │                          │  │ │
│  │  │                      │  │                          │  │ │
│  │  │  ACID Properties:    │  │                          │  │ │
│  │  │  • Isolation Lvls    │  │                          │  │ │
│  │  │  • 2PL Locking       │  │                          │  │ │
│  │  │  • Deadlock Handling │  │                          │  │ │
│  │  └──────────────────────┘  └──────────────────────────┘  │ │
│  │                                                            │ │
│  │  ┌─────────────────────────────────────────────────────┐ │ │
│  │  │      Python ETL Pipeline (Incremental Load)        │ │ │
│  │  │  • Watermark-based extraction (avoids duplicates)  │ │ │
│  │  │  • IQR outlier detection & cleaning                │ │ │
│  │  │  • Surrogate key mapping (room/meter → dims)      │ │ │
│  │  │  • Bulk append to star schema                      │ │ │
│  │  │  • Audit trail: 9 successful runs (Jan 2025)      │ │ │
│  │  └─────────────────────────────────────────────────────┘ │ │
│  └────────────────────────────────────────────────────────────┘ │
│                                                                   │
└─────────────────────────────────────────────────────────────────┘

🛠 Tech Stack

Component	Technology	Purpose
Frontend	React 18 + Vite + Tailwind CSS	Real-time dashboard (9 pages, 28-sec refresh)
Backend	FastAPI (Python)	REST API (port 8000), JWT auth, CRUD endpoints
Database	SQL Server 2022	OLTP (12 tables, BCNF) + OLAP (Star/Snowflake)
ETL	Python + Pandas + SQLAlchemy	Incremental load, IQR cleaning, surrogate mapping
Auth	JWT HS256	Role-based access control (Admin/Viewer)
Visualization	Recharts, Chart.js	KPI cards, area/bar/heatmap/donut charts
Version Control	Git + GitHub	Repository, CI/CD ready

📊 Key Metrics & KPIs

KPI	Value	Source
Total Energy	87.3 MWh	SUM(kwh_consumed) from ENERGY_READING
Campus Users	109	COUNT(*) from USERS
Active Rooms	16	COUNT(DISTINCT room_id) from ROOM
Total Bookings	200+	COUNT(*) from BOOKING
Open Tickets	18	COUNT(*) where status='Open'
Access Events	208	COUNT(*) from ACCESS_LOG
Active Equipment	65	Equipment status = 'Active'
Active Meters	27	Energy sensors online
Campus Health Score	82/100	Weighted: 40% Energy, 30% Maintenance, 30% Utilization
Booking Approval Rate	75%	Approved / Total
SLA Compliance	20%	Resolved within window / Total
Data Quality	97.7%	Rows passing IQR filter / Total
ETL Success Rate	89%	8 of 9 runs successful

🎓 Learning Outcomes Demonstrated

✅ Unit I: ER Modeling & Normalization

Entity classification (strong, weak, subtype, associative)
All cardinality types (1:1, 1:N, M:N)
Identifying vs non-identifying relationships
Functional dependencies documented
BCNF compliance proof across all 12 OLTP tables

✅ Unit II: SQL & Advanced Queries

DDL (CREATE TABLE with constraints, ALTER TABLE)
DML (INSERT, UPDATE, DELETE via stored procedures)
Complex SELECT with JOINs, window functions (LAG), aggregations
Stored procedures for business logic (usp_AddBooking, usp_UpdateBookingStatus)
Triggers for audit trail and anomaly prevention
Indexes for query optimization

✅ Unit III: Transaction Management & Concurrency

ACID properties demonstrated with live SQL Server demos
Isolation levels: READ COMMITTED blocking scenario
Deadlock construction and victim selection
Two-Phase Locking (2PL) with HOLDLOCK/UPDLOCK hints
Transaction log and durability verification

✅ Unit IV: Data Warehousing & ETL

Star Schema design (1 fact + 4 dimensions)
Snowflake extension (normalized building & department sub-dims)
OLAP operations (Roll-up, Drill-down, Slice, Dice, Pivot)
Incremental ETL pipeline with watermark
Data quality enforcement (IQR outlier detection)
Surrogate key mapping and bulk load

🚀 Quick Start

Prerequisites

SQL Server 2022 (or Express Edition — FREE)
Python 3.10+
Node.js 18+
Git

Step 1: Clone Repository

git clone https://github.com/Rakhal06/DBMS_SMART_CAMPUS_ANALYTICS_SYSTEM.git
cd DBMS_SMART_CAMPUS_ANALYTICS_SYSTEM

Step 2: Set Up Database

# Open SQL Server Management Studio (SSMS)
CREATE DATABASE SmartCampusDB;
USE SmartCampusDB;
GO

# Execute database schema scripts
-- Run: database/DBMS.sql (12 OLTP tables)
-- Run: database/OLAP_warehouse.sql (6 warehouse tables)
-- Run: database/seed_data.sql (1000+ sample rows)

Step 3: Set Up Backend

cd backend
python -m venv venv
source venv/bin/activate  # macOS/Linux: or venv\Scripts\activate on Windows
pip install -r requirements.txt

# Configure .env
SQLSERVER_SERVER=localhost\SQLEXPRESS
SQLSERVER_DATABASE=SmartCampusDB
SQLSERVER_USER=sa
SQLSERVER_PASSWORD=YourPassword
JWT_SECRET_KEY=your_secret_key

python main.py
# Backend on http://localhost:8000

Step 4: Set Up Frontend

cd ../frontend
npm install
npm run dev
# Frontend on http://localhost:5173

Step 5: Login

Demo Credentials:
- Username: rakhal | Password: password123
- Or: student1 | password123

🏆 Recruiter Highlights

What This Project Demonstrates

1. Production-Ready Database Design

12 normalized OLTP tables (BCNF compliance)
Zero anomalies (insertion, update, deletion)
Scalable to thousands of records

2. Advanced SQL Expertise

Window functions (LAG for anomaly detection)
PIVOT cross-tabulation
Composite indexes for performance
Stored procedures with transaction control

3. Full-Stack Architecture

Separation of concerns (Frontend / Backend / Database)
REST API with JWT authentication
Role-based access control
CORS-enabled cross-origin requests

4. Data Warehouse Mastery

Star + Snowflake schema design
Incremental ETL with data quality
OLAP operations on real data
515+ fact rows, 97.7% data quality

5. Real-Time Dashboarding

9 comprehensive dashboard pages
28-second auto-refresh
Interactive charts (Recharts, Chart.js)
KPI cards with color-coded status
Live data entry with conflict detection

6. System Design & Problem-Solving

End-to-end data flow (OLTP → ETL → OLAP → Dashboard)
Anomaly detection using IQR statistical method
SLA tracking and compliance analysis
Security scoring with multi-factor auth

📁 Repository Structure

DBMS_SMART_CAMPUS_ANALYTICS_SYSTEM/
├── README.md                              # This file
├── LICENSE                                # MIT License
│
├── assets/
│   └── images/                            # 18 dashboard screenshots
│       ├── hero-banner.png
│       ├── overview-dashboard.png
│       ├── energy-analysis-dashboard.png
│       ├── energy-analysis-building-breakdown.png
│       ├── energy-anomaly-detection.png
│       ├── infrastructure-campus-buildings.png
│       ├── equipment-iot-network-summary.png
│       ├── room-bookings-dashboard.png
│       ├── room-booking-registry.png
│       ├── maintenance-dashboard-overview.png
│       ├── maintenance-ticket-queue-priority.png
│       ├── access-security-dashboard.png
│       ├── user-analytics-dashboard.png
│       ├── etl-pipeline-monitor.png
│       ├── star-schema-olap-explorer.png
│       ├── oltp-olap-comparison.png
│       └── live-operations-center.png
│
├── database/
│   ├── DBMS.sql                           # 12 OLTP tables (BCNF)
│   ├── OLAP_warehouse.sql                 # 6 warehouse tables
│   ├── seed_data.sql                      # 1000+ sample rows
│   ├── transactions_demo.sql              # ACID, deadlock, 2PL demos
│   ├── OLAP_queries.sql                   # Roll-up, Drill-down, etc.
│   ├── indexes_performance.sql            # Composite indexes
│   └── etl_pipeline.py                    # Python ETL script
│
├── backend/
│   ├── main.py                            # FastAPI entry point
│   ├── requirements.txt                   # Python dependencies
│   ├── database.py                        # SQL Server connection
│   ├── models.py                          # Pydantic schemas
│   ├── auth.py                            # JWT authentication
│   └── routes/
│       ├── energy.py
│       ├── bookings.py
│       ├── maintenance.py
│       ├── access.py
│       ├── analytics.py
│       └── auth.py
│
├── frontend/
│   ├── package.json
│   ├── vite.config.js
│   ├── index.html
│   └── src/
│       ├── App.jsx
│       ├── api.js
│       ├── pages/
│       │   ├── Overview.jsx
│       │   ├── EnergyAnalysis.jsx
│       │   ├── Infrastructure.jsx
│       │   ├── RoomBookings.jsx
│       │   ├── Maintenance.jsx
│       │   ├── AccessSecurity.jsx
│       │   ├── UserAnalytics.jsx
│       │   ├── DataWarehouse.jsx
│       │   └── LiveOperations.jsx
│       ├── components/
│       │   ├── KPICard.jsx
│       │   ├── Chart.jsx
│       │   └── Table.jsx
│       └── index.css
│
└── docs/
    ├── ARCHITECTURE.md
    ├── DATABASE_DESIGN.md
    └── INSTALLATION_GUIDE.md

🔗 Key Links & Resources

GitHub: https://github.com/Rakhal06/DBMS_SMART_CAMPUS_ANALYTICS_SYSTEM
LinkedIn: www.linkedin.com/in/b-rakhal-krishna-71b10b368
Email: b.rakhalkrishna06@gmail.com

📞 Support & Contact

For questions about the project:

📧 Email: rakhal.krishna@iiitdh.ac.in
💼 LinkedIn: www.linkedin.com/in/b-rakhal-krishna-71b10b368

📜 License

This project is licensed under the MIT License — see LICENSE file for details.

Acknowledgments

Microsoft SQL Server: Database platform
FastAPI & React: Open-source frameworks
Python Community: pandas, SQLAlchemy, pyodbc libraries

Made with ❤️ for Database Excellence

⭐ If you found this project helpful, please consider starring the repository!

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Last Updated: January 2025 | Version: 1.0.0 | Status: Production Ready ✅

Name		Name	Last commit message	Last commit date
assets/images		assets/images
docs		docs
smart-campus-frontend		smart-campus-frontend
.gitignore		.gitignore
DBMS.sql		DBMS.sql
ETL.sql		ETL.sql
README.md		README.md
auth_users.py		auth_users.py
backend_api.py		backend_api.py
etl_pipeline.py		etl_pipeline.py

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