FERRARI: an efficient framework for visual exploratory subgraph search in graph databases
Exploratory search paradigm assists users who do not have a clear search intent and are unfamiliar with the underlying data space. Query formulation evolves iteratively in this paradigm as a user becomes more familiar with the content. Although exploratory search has received significant attention r...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/161029 |
| _version_ | 1826110563978051584 |
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| author | Wang, Chaohui Xie, Miao Bhowmick, Sourav S. Choi, Byron Xiao, Xiaokui Zhou, Shuigeng |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Wang, Chaohui Xie, Miao Bhowmick, Sourav S. Choi, Byron Xiao, Xiaokui Zhou, Shuigeng |
| author_sort | Wang, Chaohui |
| collection | NTU |
| description | Exploratory search paradigm assists users who do not have a clear search intent and are unfamiliar with the underlying data space. Query formulation evolves iteratively in this paradigm as a user becomes more familiar with the content. Although exploratory search has received significant attention recently in the context of structured data, scant attention has been paid for graph-structured data. An early effort for building exploratory subgraph search framework on graph databases suffers from efficiency and scalability problems. In this paper, we present a visual exploratory subgraph search framework called ferrari, which embodies two novel index structures called vaccine and advise, to address these limitations. vaccine is an offline, feature-based index that stores rich information related to frequent and infrequent subgraphs in the underlying graph database, and how they can be transformed from one subgraph to another during visual query formulation. advise, on the other hand, is an adaptive, compact, on-the-fly index instantiated during iterative visual formulation/reformulation of a subgraph query for exploratory search and records relevant information to efficiently support its repeated evaluation. Extensive experiments and user study on real-world datasets demonstrate superiority of ferrari to a state-of-the-art visual exploratory subgraph search technique. |
| first_indexed | 2024-10-01T02:36:11Z |
| format | Journal Article |
| id | ntu-10356/161029 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:36:11Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1610292022-08-12T03:41:11Z FERRARI: an efficient framework for visual exploratory subgraph search in graph databases Wang, Chaohui Xie, Miao Bhowmick, Sourav S. Choi, Byron Xiao, Xiaokui Zhou, Shuigeng School of Computer Science and Engineering Engineering::Computer science and engineering Exploratory Subgraph Search Visual Interface Exploratory search paradigm assists users who do not have a clear search intent and are unfamiliar with the underlying data space. Query formulation evolves iteratively in this paradigm as a user becomes more familiar with the content. Although exploratory search has received significant attention recently in the context of structured data, scant attention has been paid for graph-structured data. An early effort for building exploratory subgraph search framework on graph databases suffers from efficiency and scalability problems. In this paper, we present a visual exploratory subgraph search framework called ferrari, which embodies two novel index structures called vaccine and advise, to address these limitations. vaccine is an offline, feature-based index that stores rich information related to frequent and infrequent subgraphs in the underlying graph database, and how they can be transformed from one subgraph to another during visual query formulation. advise, on the other hand, is an adaptive, compact, on-the-fly index instantiated during iterative visual formulation/reformulation of a subgraph query for exploratory search and records relevant information to efficiently support its repeated evaluation. Extensive experiments and user study on real-world datasets demonstrate superiority of ferrari to a state-of-the-art visual exploratory subgraph search technique. Ministry of Education (MOE) The first three authors are supported by AcRF MOE2015-T2-1-040 and AcRF Tier-1 Grant RG24/12. Shuigeng Zhou is supported by National NSF of China (Grant No. U1636205). 2022-08-12T03:41:11Z 2022-08-12T03:41:11Z 2020 Journal Article Wang, C., Xie, M., Bhowmick, S. S., Choi, B., Xiao, X. & Zhou, S. (2020). FERRARI: an efficient framework for visual exploratory subgraph search in graph databases. VLDB Journal, 29(5), 973-998. https://dx.doi.org/10.1007/s00778-020-00601-0 1066-8888 https://hdl.handle.net/10356/161029 10.1007/s00778-020-00601-0 2-s2.0-85078832713 5 29 973 998 en MOE2015-T2-1-040 RG24/12 VLDB Journal © 2020 Springer-Verlag GmbH Germany, part of Springer Nature. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Exploratory Subgraph Search Visual Interface Wang, Chaohui Xie, Miao Bhowmick, Sourav S. Choi, Byron Xiao, Xiaokui Zhou, Shuigeng FERRARI: an efficient framework for visual exploratory subgraph search in graph databases |
| title | FERRARI: an efficient framework for visual exploratory subgraph search in graph databases |
| title_full | FERRARI: an efficient framework for visual exploratory subgraph search in graph databases |
| title_fullStr | FERRARI: an efficient framework for visual exploratory subgraph search in graph databases |
| title_full_unstemmed | FERRARI: an efficient framework for visual exploratory subgraph search in graph databases |
| title_short | FERRARI: an efficient framework for visual exploratory subgraph search in graph databases |
| title_sort | ferrari an efficient framework for visual exploratory subgraph search in graph databases |
| topic | Engineering::Computer science and engineering Exploratory Subgraph Search Visual Interface |
| url | https://hdl.handle.net/10356/161029 |
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