Author Type

Graduate Student

Date of Award

2021

Document Type

Dissertation

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Yang, KwangSoo

Abstract

Resource allocation for Spatial Network Big Database is challenging due to the large size of spatial networks, variety of types of spatial data, a fast update rate of spatial and temporal elements. It is challenging to learn, manage and process the collected data and produce meaningful information in a limited time. Produced information must be concise and easy to understand. At the same time, the information must be very descriptive and useful. My research aims to address these challenges through the development of fundamental data processing components for advanced spatial network queries that clearly and briefly deliver critical information. This thesis proposal studied two challenging Spatial Network Big Database problems: (1) Multiple Resource Network Voronoi Diagram and (2) Node-attributed Spatial Graph Partitioning. To address the challenge of query processing for multiple resource allocation in preparing for or after a disaster, we investigated the problem of the Multiple Resource Network Voronoi Diagram (MRNVD). Given a spatial network and a set of service centers from k different resource types, a Multiple Resource Network Voronoi Diagram (MRNVD) partitions the spatial network into a set of Service Areas that can minimize the total cycle-distances of graph-nodes to allotted k service centers with different resource types. The MRNVD problem is important for critical societal applications such as assigning essential survival supplies (e.g., food, water, gas, and medical assistance) to residents impacted by man-made or natural disasters. The MRNVD problem is NP-hard; it is computationally challenging due to the large size of the transportation network. Previous work proposed the Distance bounded Pruning (DP) approach to produce an optimal solution for MRNVD. However, we found that DP can be generalized to reduce the computational cost for the minimum cycle-distance. We extend our prior work and propose a novel approach that reduces the computational cost. Experiments using real-world datasets from five different regions demonstrate that the proposed approach creates MRNVD and significantly reduces the computational cost.

Extent

101 p.

Comments

Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2021.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00013854

Format

pdf

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