# AutoG: a visual query autocompletion framework for graph databases

- 304 Downloads
- 1 Citations

## Abstract

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, *automatic query completion*, graph query autocompletion has received much less research attention. In this paper, we propose a novel framework for subgraph query autocompletion (called AutoG). Given an initial query *q* and a user’s preference as input, AutoG returns ranked query suggestions \(Q'\) as output. Users may choose a query from \(Q'\) and iteratively apply AutoG to compose their queries. The novelties of AutoG are as follows: First, we formalize query composition. Second, we propose to increment a query with the logical units called *c*-*prime features* that are (i) frequent subgraphs and (ii) constructed from smaller *c*-prime features in no more than *c* ways. Third, we propose algorithms to rank candidate suggestions. Fourth, we propose a novel index called *feature * Dag (FDag) to optimize the ranking. We study the query suggestion quality with simulations and real users and conduct an extensive performance evaluation. The results show that the query suggestions are useful (saved roughly 40% of users’ mouse clicks), and AutoG returns suggestions shortly under a large variety of parameter settings.

### Keywords

Subgraph query Query autocompletion Graphs Database usability## Notes

### Acknowledgements

Peipei Yi and Byron Choi are partially supported by the HK-RGC GRF 12201315 and 12232716. Sourav S Bhowmick is supported by the Singapore MOE AcRF Tier-1 Grant RG24/12 and MOE AcRF Tier-2 Grant 2015-T2-1-040. Jianliang Xu is partially supported by the HK-RGC GRF 12244916 and 12200114.

### References

- 1.Abiteboul, S., Amsterdamer, Y., Milo, T., Senellart, P.: Auto-completion learning for xml. In: SIGMOD (2012)Google Scholar
- 2.Bast, H., Weber, I.: Type less, find more: fast autocompletion search with a succinct index. In: SIGIR (2006)Google Scholar
- 3.Bhowmick, S.S., Choi, B., Zhou, S.: VOGUE: towards a visual interaction-aware graph query processing framework. In: CIDR (2013)Google Scholar
- 4.Bhowmick, S.S., Chua, H.-E., Thian, B., Choi, B.: ViSual: An hci-inspired simulator for blending visual subgraph query construction and processing. In: ICDE (2015)Google Scholar
- 5.Bhowmick, S.S., Dyreson, C.E., Choi, B., Ang, M.-H.: Interruption-sensitive empty result feedback: Rethinking the visual query feedback paradigm for semistructured data. In: CIKM (2015)Google Scholar
- 6.Borodin, A., Lee, H.C., Ye, Y.: Max-sum diversification, monotone submodular functions and dynamic updates. In: PODS (2012)Google Scholar
- 7.Braga, D., Campi, A., Ceri, S.: XQBE (XQuery By Example), A visual interface to the standard xml query language. In: TODS (2005)Google Scholar
- 8.Broder, A.Z.: On the resemblance and containment of documents. In: Compression and complexity of sequences (1997)Google Scholar
- 9.Bunke, H., Shearer, K.: A graph distance metric based on the maximal common subgraph. Pattern Recognit. Lett.
**19**(3), 255–259 (1998)CrossRefMATHGoogle Scholar - 10.Cheng, J., Ke, Y., Ng, W., Lu, A.: Fg-index: towards verification-free query processing on graph databases. In: SIGMOD (2007)Google Scholar
- 11.Comai, S., Damiani, E., Fraternali, P.: Computing graphical queries over xml data. In: TOIS (2001)Google Scholar
- 12.Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: A (sub)graph isomorphism algorithm for matching large graphs. In: PAMI (2004)Google Scholar
- 13.Fan, Z., Peng, Y., Choi, B., Xu, J., Bhowmick, S.S.: Towards efficient authenticated subgraph query service in outsourced graph databases. In: TSC (2014)Google Scholar
- 14.Feng, J., Li, G.: Efficient fuzzy type-ahead search in xml data. In: TKDE, pp. 882–895 (2012)Google Scholar
- 15.Gollapudi, S., Sharma, A.: An axiomatic approach for result diversification. In: WWW (2009)Google Scholar
- 16.Han, W.-S., Lee, J., Pham, M.-D., Yu, J.X.: iGraph: a framework for comparisons of disk-based graph indexing techniques. In: PVLDB, pp. 449–459 (2010)Google Scholar
- 17.Herschel, M., Tzitzikas, Y., Candan, K.S., Marian, A.: Exploratory search: New name for an old hat? http://wp.sigmod.org/?p=1183 (2014)
- 18.Hung, H.H., Bhowmick, S.S., Truong, B.Q., Choi, B., Zhou, S.: QUBLE: blending visual subgraph query formulation with query processing on large networks. In: SIGMOD, pp. 1097–1100 (2013)Google Scholar
- 19.Jayaram, N., Goyal, S., Li, C.: VIIQ: auto-suggestion enabled visual interface for interactive graph query formulation. In: PVLDB, pp. 1940–1951 (2015)Google Scholar
- 20.Jayaram, N., Gupta, M., Khan, A., Li, C., Yan, X., Elmasri, R.: GQBE: querying knowledge graphs by example entity tuples. In: ICDE (2014)Google Scholar
- 21.Jin, C., Bhowmick, S.S., Xiao, X., Cheng, J., Choi, B.: GBLENDER: towards blending visual query formulation and query processing in graph databases. In: SIGMOD (2010)Google Scholar
- 22.Kriege, N., Mutzel, P., Schäfer, T.: Practical sahn clustering for very large data sets and expensive distance metrics. J. Graph Algorithms Appl.
**18**, 577–602 (2014)Google Scholar - 23.Li, Y., Yu, C., Jagadish, H.V.: Enabling schema-free xquery with meaningful query focus. VLDB J.
**17**, 355–377 (2008)Google Scholar - 24.Lin, C., Lu, J., Ling, T.W., Cautis, B.: LotusX: a position-aware xml graphical search system with auto-completion. In: ICDE (2012)Google Scholar
- 25.Luks, E.M.: Isomorphism of graphs of bounded valence can be tested in polynomial time. J. Comput. Syst. Sci.
**25**, 42–65 (1982)Google Scholar - 26.Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM
**49**, 41–46 (2006)Google Scholar - 27.McGregor, J.J.: Backtrack search algorithms and the maximal common subgraph problem. Softw. Pract. Exp.
**12**, 23–34 (1982)Google Scholar - 28.Mottin, D., Bonchi, F., Gullo, F.: Graph query reformulation with diversity. In: KDD, pp. 825–834 (2015)Google Scholar
- 29.Nandi, A., Jagadish, H.V.: Effective phrase prediction. In: VLDB, pp. 219–230 (2007)Google Scholar
- 30.
- 31.NLM. PubChem. ftp://ftp.ncbi.nlm.nih.gov/pubchem/
- 32.Pandey, S., Punera, K.: Unsupervised extraction of template structure in web search queries. In: WWW, pp. 409–418 (2012)Google Scholar
- 33.Papakonstantinou, Y., Petropoulos, M., Vassalos, V.: QURSED: querying and reporting semistructured data. In: SIGMOD (2002)Google Scholar
- 34.Qin, L., Yu, J.X., Chang, L.: Diversifying top-k results. CoRR, arXiv:1208.0076 (2012)
- 35.Shasha, D., Wang, J.T.-L., Giugno, R.: Algorithmics and applications of tree and graph searching. In: PODS (2002)Google Scholar
- 36.Venero, M.L.F., Valiente, G.: A graph distance metric combining maximum common subgraph and minimum common supergraph. Pattern Recognit. Lett.
**22**, 753–758 (2001)CrossRefMATHGoogle Scholar - 37.Vieira, M.R., Razente, H.L., Barioni, M.C.N., Hadjieleftheriou, M., Srivastava, D., Traina, C., Tsotras, V.J.: On query result diversification. In: ICDE (2011)Google Scholar
- 38.Wallis, W.D., Shoubridge, P., Kraetzl, M., Ray, D.: Graph distances using graph union. Pattern Recognit. Lett.
**22**, 701–704 (2001)CrossRefMATHGoogle Scholar - 39.Xiao, C., Qin, J., Wang, W., Ishikawa, Y., Tsuda, K., Sadakane, K.: Efficient error-tolerant query autocompletion. In: PVLDB (2013)Google Scholar
- 40.Xie, X., Fan, Z., Choi, B., Yi, P., Bhowmick, S.S., Zhou, S.: PIGEON: Progress indicator for subgraph queries. In: ICDE (2015)Google Scholar
- 41.Yan, X., Han, J.: gSpan: Graph-based substructure pattern mining. In: ICDM, pp. 721–724, (2002)Google Scholar
- 42.Yan, X., Yu, P.S., Han, J.: Graph indexing: a frequent structure-based approach. In: SIGMOD (2004)Google Scholar
- 43.Yi, P., Choi, B., Bhowmick, S.S., Xu, J.: AutoG: A visual query autocompletion framework for graph databases. https://goo.gl/Xr9MRY (2016)
- 44.Yi, P., Choi, B., Bhowmick, S.S., Xu, J.: AutoG: a visual query autocompletion framework for graph databases [demo]. PVLDB
**9**, 1505–1508 (2016)Google Scholar - 45.Yuan, D., Mitra, P.: Lindex: a lattice-based index for graph databases. VLDB J.
**22**, 229–252 (2013)CrossRefGoogle Scholar