Bilal RAZA

Master's thesis

Understanding the Performance of Hierarchical Navigable Small World (HNSW) in the Facebook FAISS Library for Indexing Large Databases

Abstract:
This thesis explores the performance of the Hierarchical Navigable Small Worlds (HNSW) algorithm within the FAISS library for indexing large databases. As data volumes grow exponentially, Approximate Nearest Neighbor (ANN) algorithms, like HNSW, offer efficient solutions. HNSW constructs a hierarchical graph structure, demonstrating superiority over other ANN algorithms. However, its performance within …more
Abstract:
This thesis explores the performance of the Hierarchical Navigable Small Worlds (HNSW) algorithm within the FAISS library for indexing large databases. As data volumes grow exponentially, Approximate Nearest Neighbor (ANN) algorithms, like HNSW, offer efficient solutions. HNSW constructs a hierarchical graph structure, demonstrating superiority over other ANN algorithms. However, its performance within …more
 

Keywords

HNSW ANN NLP FAISS Wikipedia Skip List NSW Graphs BERT
 
Language used: English
Date on which the thesis was submitted / produced: 9. 2. 2024

Thesis defence

  • Supervisor: prof. Dr. Andreas Fischer

Citation record

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RAZA, Bilal. Understanding the Performance of Hierarchical Navigable Small World (HNSW) in the Facebook FAISS Library for Indexing Large Databases. České Budějovice, 2024. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta

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  • Soubory jsou nedostupné do 9. 2. 2027
  • Po tomto datu bude práce dostupná: světu
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UNIVERSITY OF SOUTH BOHEMIA IN ČESKÉ BUDĚJOVICE

Faculty of Science

Master programme / field:
Artificial Intelligence and Data Science / Artificial Intelligence and Data Science

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