NetPlag-Stream — Continuous Plagiarism Detection on Distributed Architecture

NetPlag-Stream is a real-time plagiarism detection system built using Big Data technologies. It combines Spark Structured Streaming, HDFS, and Elasticsearch to continuously analyze academic documents and detect similarities against a reference corpus.

Project Overview

• Real-time streaming detection using Spark Structured Streaming.
• Persistent storage and shared artifacts on HDFS.
• Fast, queryable results via Elasticsearch with a Flask dashboard.

Architecture

Documents → Spark Streaming → TF–IDF → Cosine Similarity ↓ HDFS (storage) ↓ Elasticsearch (indexing) ↓ Dashboard (visualization)

Key technologies:

• Apache Spark
• HDFS
• Elasticsearch
• Flask
• Docker

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Quick Start (summary)

Prerequisites

• Docker Desktop
• Python 3.11
• Java 17

1. Start the services:

docker-compose up -d
Start-Sleep -Seconds 30
docker-compose ps

2. Exit HDFS safe mode (if needed):

docker exec namenode hdfs dfsadmin -safemode leave

3. Install Python dependencies:

pip install -r requirements.txt

4. Create the HDFS structure:

python scripts/0_migrate_to_hdfs.py

5. Fast-batch migrate the local corpus into HDFS (optional, recommended):

.\migrate_fast.ps1

6. Build TF–IDF model and reference vectors:

python scripts/1_batch_init.py

7. Run the full streaming pipeline (automated):

python scripts/8_full_streamprocess.py

Or run pieces manually:

python scripts/2_streaming_app.py          # streaming process
python scripts/4_plagiarism_analysis.py    # batch analysis
python scripts/6_elasticsearch_indexer.py  # index results

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Implementation and Results (Summary)

This section summarizes the main pipeline stages, evidence of execution, and visual confirmations. Only the most relevant images are included below in the same order they appeared in the original documentation. Place the image files under docs/ using the filenames shown.

1. Infrastructure & HDFS initialization

The scripts/0_migrate_to_hdfs.py script creates a strict HDFS layout that separates raw data (/data) from processed artifacts (/storage). This step also validates Spark connectivity with the Dockerized NameNode.

2. Corpus migration and ingestion

The migrate_fast.ps1 script moves large numbers of small files into HDFS via efficient batch transfers (Docker volume mounts), reducing ingestion time.

3. Batch preprocessing and TF–IDF model training

The scripts/1_batch_init.py script reads the reference corpus, performs text cleaning and tokenization, computes IDF weights, and saves the model and the reference vectors to storage on HDFS.

4. Real-time streaming detection

scripts/2_streaming_app.py runs as a long-lived process that watches the stream_input folder. Incoming files are vectorized with the precomputed IDF and compared (cosine similarity) against the reference vectors. High-similarity results are immediately flagged (e.g., is_plagiarism: true).

5. Consolidated analysis and statistics

scripts/4_plagiarism_analysis.py aggregates streaming results, computes statistics (means, distributions), and writes Parquet summaries for traceability.

6. Indexing to Elasticsearch

scripts/6_elasticsearch_indexer.py pushes JSON results into Elasticsearch using the Bulk API so results become searchable via the dashboard.

7. Dashboard (global + detailed views)

The Flask dashboard queries Elasticsearch and provides both summary metrics and detailed traces for each suspicious document pairing.

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Detection method

• TF–IDF vectorization with cosine similarity.
• Suggested thresholds:

  • 0.7 : potential plagiarism

  • 0.8 : strong similarity

  • 0.9 : near-identical copy

Important files

• config/hdfs_config.py — HDFS / Spark settings
• config/elasticsearch_config.py — Elasticsearch settings
• scripts/0_migrate_to_hdfs.py — create HDFS tree
• scripts/1_batch_init.py — build TF–IDF & reference vectors
• scripts/2_streaming_app.py — streaming detection
• scripts/4_plagiarism_analysis.py — batch analysis
• scripts/6_elasticsearch_indexer.py — indexer
• scripts/8_full_streamprocess.py — end-to-end runner

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If you want, I can (1) add placeholder images into docs/, (2) adjust captions, or (3) produce a shorter summary README suitable for publication. Which would you prefer next?

HDFS

# List HDFS files
docker exec namenode hdfs dfs -ls /netplag/data/corpus_initial

# Show used space
docker exec namenode hdfs dfs -du -h /netplag

# Copy a file from HDFS
docker exec namenode hdfs dfs -get /netplag/storage/reports/plagiarism_cases.json

Elasticsearch

# Vérifier les indices
curl http://localhost:9200/_cat/indices?v

# Compter les documents indexés
curl http://localhost:9200/plagiarism_reports/_count

# Rechercher des documents
curl http://localhost:9200/plagiarism_reports/_search?q=similarity_score:>0.8

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🎯 Use Cases

1. Continuous Academic Monitoring

• Automatic surveillance of new publications
• Plagiarism detection across submitted articles
• Real-time alerts on suspicious similarities

2. Thesis and Dissertation Validation

• Batch analysis of student documents
• Comparison with bibliographic corpus
• Generation of detailed reports

3. Editorial Compliance

• Pre-publication checks
• Detection of uncredited reuse
• Source traceability

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⚙️ Advanced Configuration

Change Detection Threshold

Edit scripts/2_streaming_app.py or scripts/8_full_streamprocess.py:

# Approx line 80
PLAGIARISM_THRESHOLD = 0.7  # Change to 0.6 or 0.8

Adjust Streaming Frequency

# Approx line 50
TRIGGER_INTERVAL = "5 seconds"  # Change to "10 seconds"

Increase Number of TF-IDF Features

Edit scripts/1_batch_init.py:

# Approx line 60
hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=5000)
# Change to 10000 for higher precision

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📈 Performance

Tested Capacity

• Reference corpus: 500+ documents
• Streaming: 10 documents every 5 seconds
• Average latency: < 10 seconds per batch
• Throughput: ~120 documents/minute
• Accuracy: ~85% (threshold 0.7)

Optimizations

• Sparse vectors (SparseVector) to save memory
• Broadcast the reference corpus (avoids shuffle)
• Parquet storage (high compression)
• Bulk Elasticsearch indexing (1000 docs/batch)
• HDFS checkpointing for fault tolerance

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📚 Références

Articles Scientifiques

• "TF-IDF: A Statistical Interpretation" - Salton & McGill (1983)
• "Cosine Similarity in Information Retrieval" - Baeza-Yates (1999)
• "Plagiarism Detection: A Survey" - Alzahrani et al. (2012)

Documentation Technique

• Apache Spark Structured Streaming
• HDFS Architecture Guide
• Elasticsearch Reference

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🤝 Contribution & Publication

Potential Publication:

"NetPlag-Stream: A Real-Time Distributed Architecture for Academic Plagiarism Detection using Spark Streaming and Delta Lake"

Research Directions:

• Real-time Big Data architectures for scientific monitoring
• Optimization of large-scale similarity computation
• Semantic detection using transformers (BERT)
• Incremental management of TF-IDF models

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✨ Authors

Developed as part of a Big Data project on plagiarism detection in distributed architecture.

• Bellmir Yahya
• Ismaili Ayman
• Ait Abdou Ayman
• Chegdati Chouaib