Publication Date

Spring 2025

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Ching-Seh Wu

Second Advisor

Navrati Saxena

Third Advisor

Thomas Austin

Keywords

Hierarchical bloom filter, cuckoo filter, partitioned bloom filter, metadata indexing, geospatial data, striped bloom filter

Abstract

Big Data infrastructure growth has produced overwhelming metadata volumes which create extensive problems for spatial indexing and both system scalability and database queries. Traditional solutions consisting of R-trees and conventional Bloom filters manage to provide either range query support or approximate membership testing, yet they face performance issues when used at large-scale metadata management. This research proposes Hierarchical Bloom Filter Tree (HBFT) as an improved framework that integrates hierarchical spatial partitioning with partitioned, scalable, cuckoo, and striped Bloom filter variants based on existing studies in hierarchical and probabilistic indexing. The complete evaluation process shows that HBFT outperforms PostGIS (an industry-standard spatial database system). The experimental outcome indicates that HBFT delivers point, range, spatial, and aggregate queries with 5-7X performance improvement with a slight storage overhead for building the metadata index while scaling with the dataset size. The HBFT approach combines practical features of fast approximate queries with memory scalability capabilities alongside support for various query types. The combination of features in HBFT makes this system highly suitable for performing massive geospatial metadata services by supporting applications in urban planning and map-based metadata systems and high-throughput spatial analytics, which require scalable and memory-efficient querying of static data.

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Available for download on Monday, May 25, 2026

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