R_Tree: Spatial Indexing and Query Processing in Python

This repository implements an R-Tree spatial index in Python, along with auxiliary modules for bulk loading, insertion, deletion, K-nearest neighbor (KNN) queries, range queries, and skyline queries.

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📂 Project Structure

File / Folder	Description
Entry.py	Implementation of the entry objects (leaf/non-leaf nodes)
Node.py	Node representation in the R-Tree (with children, bounding rectangle, etc.)
bulkloading.py	Bulk loading algorithm for building a tree from many objects
insert.py	Insertion logic for adding individual objects
delete.py	Deletion logic for removing objects
KNN.py	K-nearest neighbor search implementation
range_query.py	Range query support (rectangular region queries)
Skyline.py	Skyline query processing over spatial objects
make_datafile.py	Utility script to generate example spatial data files
make_indexfile.py	Utility script to generate example index files
datafile*.xml	Sample datasets in XML format
indexfile*.xml	Sample index files for experiments
map*.osm	Sample map / spatial data files for testing

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🔍 Features & Functionality

• Implementation of the R-Tree spatial index structure in Python
• Support for bulk loading, insert, delete
• Query operations:
  • K-nearest neighbors (KNN)
  • Range queries (axis-aligned rectangle)
  • Skyline queries on spatial data
• Utility scripts for generating test data & indexes
• Sample datasets and maps included for experimentation

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🛠 Requirements

To run and test the code, you will need:

• Python 3.7 or newer
• Standard Python libraries (no external dependencies assumed)
• Optionally: xml parsers / libraries if you customize input format
• A command-line or IDE environment to run .py scripts

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🚀 Usage Examples

Below are sample commands / usage examples.

Bulk load and index building

python bulkloading.py --input datafile4000.xml --output indexfile_bulk.xml

Insertion

python insert.py --index indexfile_bulk.xml --object new_object.xml

Deletion

python delete.py --index indexfile_bulk.xml --object object_to_remove.xml

KNN querry

python KNN.py --index indexfile_bulk.xml --query q_point.xml --k 5

Range querry

python range_query.py --index indexfile_bulk.xml --xmin x1 --ymin y1 --xmax x2 --ymax y2

Skyline querry

python Skyline.py --index indexfile_bulk.xml --input datafile4000.xml

📊 Performance Considerations & Design Choices

• Node capacity & split strategies: The choice of maximum entries per node and the splitting policy directly affect tree height and query efficiency.
• Bounding rectangle heuristics: Different heuristics for minimum bounding rectangles influence overlap and coverage, impacting query performance.
• Bulk loading: Typically produces higher-quality trees compared to inserting objects in random order.
• Deletion handling: Must manage node underflow properly, including reinsertion of orphaned entries.
• Query performance: Strongly depends on pruning effectiveness using bounding rectangles.
• Skyline queries: Often require advanced pruning techniques to remain efficient on large datasets.

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🧪 Testing & Validation

• Use the provided sample data files (datafile*.xml) and index files (indexfile*.xml) to validate correctness.
• Compare the results of KNN, range, and skyline queries against a brute-force baseline (if feasible).
• Experiment with different R-Tree parameters (e.g., node size, split policy) to observe trade-offs in performance and accuracy.