Paper Info
Title
CATAPULT: Data-driven Selection of Canned Patterns for Efficient Visual Graph Query Formulation
Abstract
Visual graph query interfaces (a.k.a gui ) widen the reach of graph querying frameworks across different users by enabling non-programmers to use them. Consequently, several commercial and academic frameworks for querying a large collection of small- or medium-sized data graphs (\textite.g., chemical compounds) provide such visual interfaces. Majority of these interfaces expose a fixed set ofcanned patterns (\textiti.e., small subgraph patterns) to expedite query formulation by enabling pattern-at-a-time in lieu of edge-at-a-time construction mode. Canned patterns to be displayed on a gui are typically selected manually based on domain knowledge. However, manual generation of canned patterns is labour intensive. Furthermore, these patterns may not sufficiently cover the underlying data graphs to expedite visual formulation of a wide range of subgraph queries. In this paper, we present a generic and extensible framework called Catapult to address these limitations. Catapult takes a data-driven approach toautomatically select canned patterns, thereby taking a concrete step towards the vision of data-driven construction of visual query interfaces. Specifically, it firstclusters the underlying data graphs based on their topological similarities and thensummarize each cluster to create acluster summary graph (csg ). The canned patterns within a user-specifiedpattern budget are then generated from these csg s by maximizingcoverage anddiversity, and minimizingcognitive load of the patterns. Experimental study with real-world datasets and visual graph interfaces demonstrates the superiority of Catapult compared to traditional techniques.
Year
DOI
Venue
2019
10.1145/3299869.3300072
Proceedings of the 2019 International Conference on Management of Data
Keywords
Field
DocType
data-driven construction, graph database, subgraph query, visual query formulation, visual query interface
Data mining,Graph,Data-driven,Information retrieval,Computer science,Catapult,Query formulation
Conference
ISSN
ISBN
Citations 
0730-8078
978-1-4503-5643-5
2
PageRank 
References 
Authors
0.36
0
5
Name
Order
Citations
PageRank
Kai Huang16312.50
Huey-Eng Chua2236.55
Sourav S. Bhowmick31519272.35
Byron Choi455445.50
Shuigeng Zhou52089207.00