ABSTRACT
Writing complex queries in SQL is a challenge for users. Prior work has developed several techniques to ease query specification but none of these techniques are applicable to a particularly difficult class of queries: quantified queries. Our hypothesis is that users prefer to specify quantified queries interactively by trial-and-error. We identify two impediments to this form of interactive trial-and-error query specification in SQL: (i) changing quantifiers often requires global syntactical query restructuring, and (ii) the absence of non-answers from SQL's results makes verifying query correctness difficult. We remedy these issues with DataPlay, a query tool with an underlying graphical query language, a unique data model and a graphical interface. DataPlay provides two interaction features that support trial-and-error query specification. First, DataPlay allows users to directly manipulate a graphical query by changing quantifiers and modifying dependencies between constraints. Users receive real-time feedback in the form of updated answers and non-answers. Second, DataPlay can auto-correct a user's query, based on user feedback about which tuples to keep or drop from the answers and non-answers. We evaluated the effectiveness of each interaction feature with a user study and we found that direct query manipulation is more effective than auto-correction for simple queries but auto-correction is more effective than direct query manipulation for more complex queries.
Supplemental Material
- Microsoft access. office.microsoft.com/en-us/access/.Google Scholar
- pgadmin: Post-gresql administration and management tools. www.pgadmin.org.Google Scholar
- Tableau software. www.tableausoftware.com.Google Scholar
- Bragdon, A., et al. Code bubbles: a working set-based interface for code understanding and maintenance. In CHI (2010). Google ScholarDigital Library
- Chapman, A., and Jagadish, H. V. Why not? In SIGMOD (2009). Google ScholarDigital Library
- Danaparamita, J., and Gatterbauer, W. Queryviz: helping users understand sql queries and their patterns. In EDBT/ICDT (2011). Google ScholarDigital Library
- Gulwani, S., Harris, W. R., and Sing, R. Spreadsheet data manipulation using examples. In CACM (2012). Google ScholarDigital Library
- Heer, J., Agrawala, M., and Willett, W. Generalized selection via interactive query relaxation. CHI (2008). Google ScholarDigital Library
- Heer, J., and Perer, A. Orion: A system for modeling, transformation and visualization of multidimensional heterogeneous networks. In VAST (2011).Google Scholar
- Kandel, S., Paepcke, A., Hellerstein, J., and Heer, J. Wrangler: Interactive visual specification of data transformation scripts. In CHI (2011). Google ScholarDigital Library
- Karrer, T., et al. Stacksplorer: call graph navigation helps increasing code maintenance efficiency. In UIST '11 (2011). Google ScholarDigital Library
- Khoussainova, N., Kwon, Y., Balazinska, M., and Suciu, D. Snipsuggest: context-aware autocompletion for sql. Proc. VLDB Endow. 4, 1 (2010). Google ScholarDigital Library
- Levene, M. The nested universal relation database model. Lecture notes in computer science. Springer-Verlag, 1990.Google Scholar
- Maier, D., Ullman, J. D., and Vardi, M. Y. On the foundations of the universal relation model. ACM Trans. Database Syst. 9, 2 (June 1984). Google ScholarDigital Library
- McHugh, J., Abiteboul, S., Goldman, R., Quass, D., and Widom, J. Lore: a database management system for semistructured data. SIGMOD Rec. 26, 3 (Sept. 1997). Google ScholarDigital Library
- Meliou, A., Gatterbauer, W., Moore, K. F., and Suciu, D. The complexity of causality and responsibility for query answers and non-answers. Proc. VLDB Endow. 4, 1 (Oct. 2010). Google ScholarDigital Library
- Norman, D. A. The Design of Everyday Things, reprint paperback ed. Basic Books, New York, 2002. Google ScholarDigital Library
- Olston, C., Stonebraker, M., Aiken, A., Aiken, E., and Hellerstein, J. M. Viqing: Visual interactive querying, 1998. Google ScholarDigital Library
- Reisner, P. Human factors studies of database query languages: A survey and assessment. ACM Comput. Surv. 13, 1 (1981), 13--31. Google ScholarDigital Library
- Reisner, P., Boyce, R. F., and Chamberlin, D. D. Human factors evaluation of two data base query languages: square and sequel. In AFIPS '75, ACM (New York, NY, USA, 1975), 447--452. Google ScholarDigital Library
- Stolte, C., and Hanrahan, P. Polaris: A system for query, analysis and visualization of multi-dimensional relational databases. In INFOVIS (2000). Google ScholarDigital Library
- Zloof, M. M. Query by example. In AFIPS National Computer Conference (1975), 431--438. Google ScholarDigital Library
Index Terms
- DataPlay: interactive tweaking and example-driven correction of graphical database queries
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