@inproceedings{03a25c6aa1174e6eab494822177ce2ce,
title = "MIDAS: Towards Efficient and Effective Maintenance of Canned Patterns in Visual Graph Query Interfaces",
abstract = "Several visual graph query interfaces (a.k.a gui) expose a set of canned patterns (i.e., small subgraph patterns) to expedite subgraph query formulation by enabling pattern-at-a-time construction. Unfortunately, manual generation of canned patterns is not only labour intensive but also may lack diversity to support efficient visual formulation of a wide range of subgraph queries. Recent efforts have taken a data-driven approach to select high-quality canned patterns for a gui automatically from the underlying graph database. However, as the underlying database evolves, these selected patterns may become stale and adversely impact efficient query formulation. In this paper, we present a novel framework called Midas for efficient and effective maintenance of the canned patterns as the database evolves. Specifically, it adopts a selective maintenance strategy that guarantees progressive gain of coverage of the patterns without sacrificing their diversity and cognitive load. Experimental study with real-world datasets and visual graph interfaces demonstrates the effectiveness of Midas compared to static guis.",
keywords = "canned patterns, cognitive load, coverage, database updates, diversity, pattern maintenance, query formulation, visual graph query interfaces",
author = "Kai Huang and Chua, {Huey Eng} and Bhowmick, {Sourav S} and Byron Choi and Shuigeng Zhou",
note = "Funding Information: Kai Huang (partially), Sourav S Bhowmick, and Huey- Eng Chua are supported by the AcRF Tier-2 Grant MOE2015-T2-1-040. Byron Choi is supported by HKRGC GRF HKBU12201518 and IRCMS/19- 20/H01. Kai Huang (partially) and Shuigeng Zhou were supported by Na- tional Natural Science Foundation of China (U1636205). Publisher Copyright: {\textcopyright} 2021 Association for Computing Machinery.; ACM SIGMOD International Conference on Management of Data, SIGMOD 2021 ; Conference date: 20-06-2021 Through 25-06-2021",
year = "2021",
month = jun,
doi = "10.1145/3448016.3457251",
language = "English",
isbn = "9781450383431",
series = "Proceedings of the ACM SIGMOD International Conference on Management of Data",
publisher = "Association for Computing Machinery (ACM)",
pages = "764–776",
editor = "Guoliang Li and Zhanhuai Li",
booktitle = "SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data",
address = "United States",
edition = "1st",
}