Efficient and scalable processing of visual property graph queries

Graph-based data structures are extremely important in the field of representing complex relationships across various domains, such as social networks. Among different types of graphs, property graphs stand out because of their ability to store detailed attributes for nodes and edges. However, th...

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Main Author: Wang, Tianyu
Other Authors: Sourav S Bhowmick
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/175024
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1750242024-04-19T15:46:36Z Efficient and scalable processing of visual property graph queries Wang, Tianyu Sourav S Bhowmick School of Computer Science and Engineering ASSourav@ntu.edu.sg Computer and Information Science Graph databases Property graph Visual query formulation Graph indexing Graph-based data structures are extremely important in the field of representing complex relationships across various domains, such as social networks. Among different types of graphs, property graphs stand out because of their ability to store detailed attributes for nodes and edges. However, the increased data volume presents a significant challenge on how to efficiently query within large-scale property graphs. This project aims to address these performance issues by proposing a system called Facraym, which is optimized for blending visual property graph query and processing. It can efficiently generate query results within large-scale property graphs by utilizing the delay between user actions. It introduces an adaptive index schema, the Adaptive Property Candidates (APC) Index, that stores candidates for each query node and edge, efficiently pruning unavailable candidates to rapidly generate query results. Additionally, a large-scale property graph dataset called Dummy Social Network Dataset is generated, which simulates a huge social network. Experiments were con ducted using Facraym to query within this dataset, and the results were compared with those from a well-known database, Neo4j. Results demonstrate that Facraym reduces waiting time compared to Neo4j when generating results. Bachelor's degree 2024-04-18T08:30:45Z 2024-04-18T08:30:45Z 2024 Final Year Project (FYP) Wang, T. (2024). Efficient and scalable processing of visual property graph queries. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175024 https://hdl.handle.net/10356/175024 en SCSE23-0261 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Graph databases
Property graph
Visual query formulation
Graph indexing
spellingShingle Computer and Information Science
Graph databases
Property graph
Visual query formulation
Graph indexing
Wang, Tianyu
Efficient and scalable processing of visual property graph queries
description Graph-based data structures are extremely important in the field of representing complex relationships across various domains, such as social networks. Among different types of graphs, property graphs stand out because of their ability to store detailed attributes for nodes and edges. However, the increased data volume presents a significant challenge on how to efficiently query within large-scale property graphs. This project aims to address these performance issues by proposing a system called Facraym, which is optimized for blending visual property graph query and processing. It can efficiently generate query results within large-scale property graphs by utilizing the delay between user actions. It introduces an adaptive index schema, the Adaptive Property Candidates (APC) Index, that stores candidates for each query node and edge, efficiently pruning unavailable candidates to rapidly generate query results. Additionally, a large-scale property graph dataset called Dummy Social Network Dataset is generated, which simulates a huge social network. Experiments were con ducted using Facraym to query within this dataset, and the results were compared with those from a well-known database, Neo4j. Results demonstrate that Facraym reduces waiting time compared to Neo4j when generating results.
author2 Sourav S Bhowmick
author_facet Sourav S Bhowmick
Wang, Tianyu
format Final Year Project
author Wang, Tianyu
author_sort Wang, Tianyu
title Efficient and scalable processing of visual property graph queries
title_short Efficient and scalable processing of visual property graph queries
title_full Efficient and scalable processing of visual property graph queries
title_fullStr Efficient and scalable processing of visual property graph queries
title_full_unstemmed Efficient and scalable processing of visual property graph queries
title_sort efficient and scalable processing of visual property graph queries
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175024
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