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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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/175024 |
| _version_ | 1824453631183683584 |
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| author | Wang, Tianyu |
| author2 | Sourav S Bhowmick |
| author_facet | Sourav S Bhowmick Wang, Tianyu |
| author_sort | Wang, Tianyu |
| collection | NTU |
| 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. |
| first_indexed | 2025-02-19T03:09:29Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175024 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:09:29Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | Efficient and scalable processing of visual property graph queries |
| title_sort | efficient and scalable processing of visual property graph queries |
| topic | Computer and Information Science Graph databases Property graph Visual query formulation Graph indexing |
| url | https://hdl.handle.net/10356/175024 |
| work_keys_str_mv | AT wangtianyu efficientandscalableprocessingofvisualpropertygraphqueries |