An indexing framework for efficient visual exploratory subgraph search in graph databases

Chaohui Wang; Miao Xie; Sourav S. Bhowmick; Koon Kau CHOI; Xiaokui Xiao; Shuigeng Zhou

doi:10.1109/ICDE.2019.00168

An indexing framework for efficient visual exploratory subgraph search in graph databases

Chaohui Wang
, Miao Xie
, Sourav S. Bhowmick
, Koon Kau CHOI
, Xiaokui Xiao
, Shuigeng Zhou

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

3 Citations (Scopus)

Abstract

Although exploratory search has received significant attention recently in the context of structured data, scant attention has been paid for graph-structured data. In this paper, we present two novel index structures called VACCINE and ADVISE to efficiently support exploratory subgraph search in a visual environment (VESS). 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. 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. These indexes engender more efficient and scalable visual exploratory subgraph search framework compared to a state-of-the-art technique.

Original language	English
Title of host publication	Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
Publisher	IEEE Computer Society
Pages	1666-1669
Number of pages	4
ISBN (Electronic)	9781538674741
DOIs	https://doi.org/10.1109/ICDE.2019.00168
Publication status	Published - Apr 2019
Event	35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China Duration: 8 Apr 2019 → 11 Apr 2019 https://doi.org/10.1109/ICDE44378.2019 (Conference proceedings)

Publication series

Name	Proceedings - International Conference on Data Engineering
Volume	2019-April
ISSN (Print)	1084-4627

Conference

Conference	35th IEEE International Conference on Data Engineering, ICDE 2019
Abbreviated title	ICDE 2019
Country/Territory	China
City	Macau
Period	8/04/19 → 11/04/19
Internet address	https://doi.org/10.1109/ICDE44378.2019 (Conference proceedings)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

User-Defined Keywords

Exploratory search
Graph database
Indexing
Visual interface

Access to Document

10.1109/ICDE.2019.00168

Cite this

Wang, C., Xie, M., Bhowmick, S. S., CHOI, K. K., Xiao, X., & Zhou, S. (2019). An indexing framework for efficient visual exploratory subgraph search in graph databases. In Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019 (pp. 1666-1669). Article 8731420 (Proceedings - International Conference on Data Engineering; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/ICDE.2019.00168

@inproceedings{5c69b766b62b4d518d48320352129b20,

title = "An indexing framework for efficient visual exploratory subgraph search in graph databases",

abstract = "Although exploratory search has received significant attention recently in the context of structured data, scant attention has been paid for graph-structured data. In this paper, we present two novel index structures called VACCINE and ADVISE to efficiently support exploratory subgraph search in a visual environment (VESS). 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. 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. These indexes engender more efficient and scalable visual exploratory subgraph search framework compared to a state-of-the-art technique.",

keywords = "Exploratory search, Graph database, Indexing, Visual interface",

author = "Chaohui Wang and Miao Xie and Bhowmick, \{Sourav S.\} and CHOI, \{Koon Kau\} and Xiaokui Xiao and Shuigeng Zhou",

note = "Funding Information: Acknowledgments. 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).; 35th IEEE International Conference on Data Engineering, ICDE 2019, ICDE 2019 ; Conference date: 08-04-2019 Through 11-04-2019",

year = "2019",

month = apr,

doi = "10.1109/ICDE.2019.00168",

language = "English",

series = "Proceedings - International Conference on Data Engineering",

publisher = "IEEE Computer Society",

pages = "1666--1669",

booktitle = "Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019",

address = "United States",

url = "https://doi.org/10.1109/ICDE44378.2019",

}

Wang, C, Xie, M, Bhowmick, SS, CHOI, KK, Xiao, X & Zhou, S 2019, An indexing framework for efficient visual exploratory subgraph search in graph databases. in Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019., 8731420, Proceedings - International Conference on Data Engineering, vol. 2019-April, IEEE Computer Society, pp. 1666-1669, 35th IEEE International Conference on Data Engineering, ICDE 2019, Macau, China, 8/04/19. https://doi.org/10.1109/ICDE.2019.00168

An indexing framework for efficient visual exploratory subgraph search in graph databases. / Wang, Chaohui; Xie, Miao; Bhowmick, Sourav S. et al.
Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society, 2019. p. 1666-1669 8731420 (Proceedings - International Conference on Data Engineering; Vol. 2019-April).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - Bhowmick, Sourav S.

AU - CHOI, Koon Kau

AU - Xiao, Xiaokui

AU - Zhou, Shuigeng

N1 - Funding Information: Acknowledgments. 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).

PY - 2019/4

Y1 - 2019/4

N2 - Although exploratory search has received significant attention recently in the context of structured data, scant attention has been paid for graph-structured data. In this paper, we present two novel index structures called VACCINE and ADVISE to efficiently support exploratory subgraph search in a visual environment (VESS). 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. 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. These indexes engender more efficient and scalable visual exploratory subgraph search framework compared to a state-of-the-art technique.

AB - Although exploratory search has received significant attention recently in the context of structured data, scant attention has been paid for graph-structured data. In this paper, we present two novel index structures called VACCINE and ADVISE to efficiently support exploratory subgraph search in a visual environment (VESS). 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. 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. These indexes engender more efficient and scalable visual exploratory subgraph search framework compared to a state-of-the-art technique.

KW - Exploratory search

KW - Graph database

KW - Indexing

KW - Visual interface

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ER -

Wang C, Xie M, Bhowmick SS, CHOI KK, Xiao X, Zhou S. An indexing framework for efficient visual exploratory subgraph search in graph databases. In Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society. 2019. p. 1666-1669. 8731420. (Proceedings - International Conference on Data Engineering). doi: 10.1109/ICDE.2019.00168

An indexing framework for efficient visual exploratory subgraph search in graph databases

Abstract

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UN SDGs

User-Defined Keywords

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