This project aims to implement multidimensional data structures using Python. These data structures are evaluated utilizing real datasets in various basic operations, including building, inserting, deleting, updating, and searching for different types of queries.
Also, it performs similarity queries on the indexed texts, specifically focusing on the third field, which describes the education of each scientist. These similarity queries are executed using the Locality-Sensitive Hashing (LSH) method.
The main goal is to compare the performance of four different methods: k-d trees with LSH, quad trees with LSH, range trees with LSH, and R-trees with LSH, in the context of multidimensional data indexing and similarity querying for text datasets authored by computer scientists.
- Multidimensional Data Structures Implementation.
- Experimental Evaluation.
- Text Data Processing.
- LSH-based Similarity Queries.
Back End: Python
To deploy this project run
python experiments.py