Abstract
With the increase of information technology, multiple terabytes of structured and unstructured data are generated on daily basis through various sources, such as sensors, lab simulations, social media, web blogs, etc. Due to big data occurrences, acquisition of relevant information is getting complex processing task. These data are often stored and kept in the vast schema, and thus formulating data retrieval requires a fundamental understanding of the schema and content. A discovery-oriented search mechanism delivers good results here, as the user can stepwise explore the database and stop when the result content and quality meet. In this, a naïve user often transforms data request in order to discover relevant items; morphing is a historical approach for the generation of various transformations of input. We proposed “Query Morphing”, an approach for query reformulation based on data exploration. Various design issues and implementation constraints of the proposed approach are also listed.
Keywords
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Ryen, R.W., Roth, R.A.: Exploratory search: beyond the query-response paradigm. Synthesis lectures on information concepts, retrieval, and services, vol. 1, no. 1, pp. 1–98 (2009)
Cetintemel, U., et al.: Query Steering for Interactive Data Exploration. In: CIDR (2013)
Dimitriadou, K., Olga, P., Yanlei, D.: Explore-by-example: an automatic query steering framework for interactive data exploration. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 517–528. ACM (2014)
Drosou, M., Evaggelia, P.: YmalDB: exploring relational databases via result-driven recommendations. The VLDB 22(6), 849–874 (2013)
Idreos, S., Papaemmanouil, O., Chaudhuri, S.: Overview of data exploration techniques. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 277–281. ACM (2015)
White, R.: Interactions with search systems. Cambridge University Press (2016)
White, R., Muresan, G., Marchionini, G.: Report on ACM SIGIR 2006 workshop on evaluating exploratory search systems. In: Acm Sigir Forum, vol. 40, no. 2, pp. 52–60. ACM (2006)
Kersten, M.L., Idreos, S., Manegold, S., Liarou, E.: The researcher’s guide to the data deluge: querying a scientific database in just a few seconds. In: PVLDB Challenges and Visions, vol. 3 (2011)
Rocchio, J.: Relevance feedback in information retrieval. The Smart retrieval system-experiments in automatic document processing, pp. XXIII-1–XXIII-11 (1971)
Beier, T., Neely, S.: Feature-based image metamorphosis. In: ACM SIGGRAPH Computer Graphics, vol. 26, no. 2, pp. 35–42. ACM (1992)
Hankins, R.A., Patel, J.M.: Data morphing: an adaptive, cache-conscious storage technique. In: Proceedings of the 29th International Conference on Very Large Data Bases, vol. 29, pp. 417–428. VLDB Endowment (2003)
Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. In: Readings in Information Retrieval, vol. 24, no. 5, pp. 355–363 (1997)
Li, H., Chan, C.Y., Maier, D.: Query from examples: an iterative, data-driven approach to query construction. In: Proceedings of the VLDB Endowment, vol. 8, no. 13, pp. 2158–2169 (2015)
Yu, J.X., Qin, L., Chang, L., Ozsu, M.T.: Keyword Search in Databases (Synthesis Lectures on Data Management) (2010)
Abouzied, A., et al.: Learning and verifying quantified boolean queries by example. In: Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pp. 49–60. ACM (2013)
Abouzied, A., Hellerstein, J.M., Silberschatz, A.: Playful query specification with DataPlay. In: Proceedings of the VLDB Endowment, vol. 5, no. 12, pp. 1938–1941 (2012)
Acharya, S., Gibbons, P.B., Poosala, V., Ramaswamy, S.: The aqua approximate query answering system. In: ACM Sigmod Record, vol. 28, no. 2, pp. 574–576. ACM (1999)
Agarwal, S., et al.: Knowing when you’re wrong: building fast and reliable approximate query processing systems. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 481–492. ACM (2014)
Agarwal, S., Mozafari, B., Panda, A., Milner, H., Madden, S., Stoica, I.: BlinkDB: queries with bounded errors and bounded response times on very large data. In: Proceedings of the 8th ACM European Conference on Computer Systems, pp. 29–42. ACM (2013)
Fan, J., Li, G., Zhou, L.: Interactive SQL query suggestion: Making databases user-friendly. In: 2011 IEEE 27th International Conference on Data Engineering (ICDE), pp. 351–362. IEEE (2011)
Bonifati, A., Ciucanu, R., Staworko, S.: Interactive inference of join queries. In: Gestion de Données-Principes, Technologies et Applications (BDA) (2014)
Cormode, G., Garofalakis, M., Haas, P.J., Jermaine, C.: Synopses for massive data: Samples, histograms, wavelets, sketches. Foundations and Trends in Databases 4(1–3), 1–294 (2012)
Shen, Y., Chakrabarti, K., Chaudhuri, S., Ding, B., Novik, L.: Discovering queries based on example tuples. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of data, pp. 493–504. ACM (2014)
Psallidas, F., Ding, B., Chakrabarti, K., Chaudhuri, S.: S4: top-k spreadsheet-style search for query discovery. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 2001–2016. ACM (2015)
Hellerstein, J.M., et al.: Interactive data analysis: the control project. Computer 32(8), 51–59 (1999)
Hellerstein, J.M., Haas, P.J., Wang, H.J.: Online aggregation. In: Proceedings of the ACM SIGMOD Conference on Management of Data (1997)
Qarabaqi, B., Riedewald, M.: User-driven refinement of imprecise queries. In: Proceedings of the International Conference on Data Engineering (ICDE) (2014)
Sellam, T., Kersten, M.L.: Meet Charles, big data query advisor. In: Proceedings of the Biennial Conference on Innovative Data Systems Research (CIDR), vol. 13, pp. 1–1 (2013)
Ruotsalo, T., Jacucci, G., Myllymäki, P., Kaski, S.: Interactive intent modeling: information discovery beyond search. Commun. ACM 58(1), 86–92 (2015)
Klouche, K., et al.: Designing for exploratory search on touch devices. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 4189–4198. ACM (2015)
Ruotsalo, T., et al.: Directing exploratory search with interactive intent modeling. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, pp. 1759–1764. ACM (2013)
Andolina, S., et al.: Intentstreams: smart parallel search streams for branching exploratory search. In: Proceedings of the 20th International Conference on Intelligent User Interfaces, pp. 300–305. ACM (2015)
Glowacka, D., Ruotsalo, T., Konuyshkova, K., Kaski, S., Jacucci, G.: Directing exploratory search: reinforcement learning from user interactions with keywords. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces, pp. 117–128. ACM (2013)
Singh, V., Jain, S.K.: A progressive query materialization for interactive data exploration. In: Proceeding of 1st International Workshop Social Data Analytics and Management (SoDAM’2016) Co-located at 44th VLDB’2016, pp. 1–10. VLDB (2016)
Ahn, J.W., Brusilovsky, P.: Adaptive visualization for exploratory information retrieval. Inf. Process. Manage. 49(5), 1139–1164 (2013)
Dhankar, A., Singh, V.: A scalable query materialization algorithm for interactive data exploration. In: 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 128–133. IEEE (2016)
Stolte, C., Tang, D., Hanrahan, P.: Polaris: a system for query, analysis, and visualization of multidimensional relational databases. IEEE Trans. Visual Comput. Graph. 8(1), 52–65 (2002)
Chau, D.H., Kittur, A., Hong, J.I., Faloutsos, C.: Apolo: making sense of large network data by combining rich user interaction and machine learning. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 167–176. ACM (2011)
Andolina, S., Klouche, K., Cabral D., Ruotsalo T., Jacucci, G.: InspirationWall: supporting idea generation through automatic information exploration. In: Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition, pp. 103–106. ACM (2015)
Zhang, Y., Gao, K., Zhang, B., Li, P.: TimeTree: A novel way to visualize and manage exploratory search process. In: International Conference on Human-Computer Interaction, pp. 313–319. Springer International Publishing, Chicago (2016)
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Patel, J., Singh, V. (2019). Query Morphing: A Proximity-Based Data Exploration for Query Reformulation. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_20
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DOI: https://doi.org/10.1007/978-981-13-1132-1_20
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