{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T01:32:22Z","timestamp":1767835942104,"version":"3.49.0"},"reference-count":9,"publisher":"Association for Computing Machinery (ACM)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2016,9]]},"abstract":"<jats:p>\n            Composing queries is evidently a tedious task. This is particularly true of graph queries as they are typically complex and prone to errors, compounded by the fact that graph schemas can be missing or too loose to be helpful for query formulation. Despite the great success of query formulation aids, in particular,\n            <jats:italic>automatic query completion<\/jats:italic>\n            , graph query autocompletion has received much less research attention. In this demonstration, we present a novel interactive visual subgraph query autocompletion framework called A\n            <jats:sc>uto<\/jats:sc>\n            G which alleviates the potentially painstaking task of graph query formulation. Specifically, given a large collection of small or medium-sized graphs and a visual query fragment\n            <jats:italic>q<\/jats:italic>\n            formulated by a user, A\n            <jats:sc>uto<\/jats:sc>\n            G returns top-\n            <jats:italic>k<\/jats:italic>\n            query suggestions\n            <jats:italic>Q<\/jats:italic>\n            \u2032 as output at interactive time. Users may choose a query from\n            <jats:italic>Q<\/jats:italic>\n            \u2032 and iteratively apply A\n            <jats:sc>uto<\/jats:sc>\n            G to compose their queries. We demonstrate various features of A\n            <jats:sc>uto<\/jats:sc>\n            G and its superior ability to generate high quality suggestions to aid visual subgraph query formulation.\n          <\/jats:p>","DOI":"10.14778\/3007263.3007295","type":"journal-article","created":{"date-parts":[[2016,11,1]],"date-time":"2016-11-01T13:47:47Z","timestamp":1478008067000},"page":"1505-1508","source":"Crossref","is-referenced-by-count":11,"title":["AutoG"],"prefix":"10.14778","volume":"9","author":[{"given":"Peipei","family":"Yi","sequence":"first","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong"}]},{"given":"Byron","family":"Choi","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong"}]},{"given":"Sourav S","family":"Bhowmick","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"given":"Jianliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2016,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920901"},{"key":"e_1_2_1_2_1","volume-title":"Exploratory search: New name for an old hat? http:\/\/wp.sigmod.org\/?p=1183","author":"Herschel M.","year":"2014","unstructured":"M. Herschel , Y. Tzitzikas , K. S. Candan , and A. Marian . Exploratory search: New name for an old hat? http:\/\/wp.sigmod.org\/?p=1183 , 2014 . M. Herschel, Y. Tzitzikas, K. S. Candan, and A. Marian. Exploratory search: New name for an old hat? http:\/\/wp.sigmod.org\/?p=1183, 2014."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2463681"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824106"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1121949.1121979"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783343"},{"key":"e_1_2_1_7_1","first-page":"219","volume-title":"VLDB","author":"Nandi A.","year":"2007","unstructured":"A. Nandi and H. V. Jagadish . Effective phrase prediction . In VLDB , pages 219 -- 230 , 2007 . A. Nandi and H. V. Jagadish. Effective phrase prediction. In VLDB, pages 219--230, 2007."},{"key":"e_1_2_1_8_1","first-page":"721","volume-title":"ICDM","author":"Yan X.","year":"2002","unstructured":"X. Yan and J. Han . gSpan: Graph-based substructure pattern mining . In ICDM , pages 721 -- 724 , 2002 . X. Yan and J. Han. gSpan: Graph-based substructure pattern mining. In ICDM, pages 721--724, 2002."},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","unstructured":"P. Yi B. Choi J. Xu and S. S. Bhowmick. AutoG: A visual query autocompletion framework for graph databases. http:\/\/goo.gl\/4KnJeq 2016.  P. Yi B. Choi J. Xu and S. S. Bhowmick. AutoG: A visual query autocompletion framework for graph databases. http:\/\/goo.gl\/4KnJeq 2016.","DOI":"10.14778\/3007263.3007295"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3007263.3007295","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T09:39:33Z","timestamp":1672220373000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3007263.3007295"}},"subtitle":["a visual query autocompletion framework for graph databases"],"short-title":[],"issued":{"date-parts":[[2016,9]]},"references-count":9,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2016,9]]}},"alternative-id":["10.14778\/3007263.3007295"],"URL":"https:\/\/doi.org\/10.14778\/3007263.3007295","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2016,9]]}}}