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Query Morphing: A Proximity-Based Approach for Data Exploration and Query Reformulation

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Mining Intelligence and Knowledge Exploration (MIKE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10682))

Abstract

Evolution of Extremely large databases is a vital challenge for data processing via traditional database systems, such as scientific DB, Genome DB, Social Media DB etc. As these DBs are often stored in a complex schema, and inherent vastness raises challenges to a naïve user on initial data request formulation and comprehending the resulting content. A discovery-oriented search mechanism delivers good results in these information seeking scenario, as the user can stepwise explore the database and stop when the result content and quality reaches his satisfaction point. In this, understanding user’s actual search intentions and how the search motives change with session progress will help greatly in achieving a search goal. A proximity-based data exploration approach, which explores the neighborhood and subsequently guides a user to overcome these limitations, named as ‘Query morphing’ is proposed in this paper. Various design issues and implementation constraints of the proposed approach are also listed.

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References

  1. White, R.W., Roth, R.A.: Exploratory search: beyond the query-response paradigm. Synth. Lect. Inf. Concepts Retrieval Serv. 1(1), 1–98 (2009)

    Google Scholar 

  2. Cetintemel, U., et al.: Query steering for interactive data exploration. In: 6th Biennial Conference on Innovative Data Systems Research, pp. 12–23. Asilomar, CA, USA (2013)

    Google Scholar 

  3. Dimitriadou, K., Papaemmanouil, O., Diao, Y.: Explore-by-example: an automatic query steering framework for interactive data exploration. In: ACM SIGMOD Conference on Management of Data, pp. 126–128. Snowbird, UT, USA (2014)

    Google Scholar 

  4. Drosou, M., Pitoura, E.: Ymaldb: exploring relational databases via result-driven recommendations. VLDB J. 22(6), 849–874 (2013)

    Article  Google Scholar 

  5. Idreos, S., Papaemmanouil, O., Chaudhuri, S.: Overview of data exploration techniques. In: ACM SIGMOD International Conference on Management of Data, pp. 277–281 (2015)

    Google Scholar 

  6. White, R.W., Muresan, G., Marchionini, G.: Report on ACM SIGIR 2006 workshop on evaluating exploratory search systems. SIGIR Forum 40(2), 52–60 (2006). ACM

    Article  Google Scholar 

  7. Kersten, M.L., et al.: The researcher’s guide to the data deluge: querying a scientific database in just a few seconds. In: PVLDB Challenges and Visions, vol. 3, no. 3. VLDB (2011)

    Google Scholar 

  8. White, R.W.: Interactions with Search Systems, 1st edn. Cambridge University Press, Cambridge (2016)

    Book  Google Scholar 

  9. Rocchio, J.J.: Relevance feedback in information retrieval. In: Scientific Report ISR-9 (Information Retrieval) to National Science Foundation. pp. 129–140 (1971)

    Google Scholar 

  10. Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. Readings Inf. Retrieval 24(5), 355–363 (1997)

    Google Scholar 

  11. Li, H., Chan, C.Y., Maier, D.: Query from examples: an iterative, data-driven approach to query construction. In: VLDB Endowment, vol. 8, no. 13, pp. 2158–2169 (2015)

    Google Scholar 

  12. Yu, J., Qin, X.: Keyword search in databases. Synth. Lect. Data Manag. 1(1), 1–155 (2009)

    Article  MATH  Google Scholar 

  13. Abouzied, D., et al.: Learning and verifying quantified Boolean queries by example. In: 32nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database (PODS), pp. 49–60, ACM, New York, New York, USA (2013)

    Google Scholar 

  14. Abouzied, J., Hellerstein, M., Silberschatz, A.: Playful query specification with dataplay. Very Large Data Bases Endowment (PVLDB) 5(12), 1938–1941 (2012)

    Google Scholar 

  15. Acharya, S., et al.: The aqua approximate query answering system. In: ACM SIGMOD Record, vol. 28, no. 2, pp. 574–576. ACM (1999)

    Google Scholar 

  16. Agarwal, S., et al.: Knowing when you’re wrong: building fast and reliable approximate query processing systems. In: ACM SIGMOD Conference on Management of Data, pp. 481–492. ACM, Snowbird, Utah, USA (2014)

    Google Scholar 

  17. Agarwal, S., et al.: BlinkDB: queries with bounded errors and bounded response times on very large data. In: 8th European Conference on Computer Systems (EuroSys), pp. 29–42. ACM, NY, USA (2013)

    Google Scholar 

  18. Bonifati, R., Staworko, S.: Interactive inference of join queries. In: 17th International Conference on Extending Database Technology (EDBT), pp. 451–462. Athènes, Greece (2014)

    Google Scholar 

  19. Cormode, G., et al.: Synopsis for massive data: Samples, histograms, wavelets, sketches. Found. Trends Databases 4(3), 1294–1319 (2012)

    Google Scholar 

  20. Fan, J., Li, G.: Interactive SQL query suggestion: making databases user-friendly. In: International Conference on Data Engineering (ICDE), pp. 126–136, IEEE, Hannover, Germany (2011)

    Google Scholar 

  21. Hellerstein, J.M., et al.: Interactive data analysis: the control project. Computer 32(8), 51–59 (1999)

    Article  Google Scholar 

  22. Hellerstein, J.M., et al.: Online aggregation. In: ACM SIGMOD Record, vol. 26, no. 2, pp. 171–182. ACM, New York, NY, USA (1997)

    Google Scholar 

  23. Qarabaqi, B., Riedewald, M.: User-driven refinement of imprecise queries. In: 30th International Conference on Data Engineering (ICDE), pp. 916–926. IEEE, USA (2014)

    Google Scholar 

  24. Sellam, T., Kersten, M.: Meet Charles, big data query advisor. In: Biennial Conference on Innovative Data Systems Research, pp. 94–102. Asilomar, California (2013)

    Google Scholar 

  25. Shen, Y., et al.: Discovering queries based on example tuples. In: SIGMOD Conference on Management of Data, pp. 493–504. ACM, Snowbird, Utah, USA (2014)

    Google Scholar 

  26. Psallidas, F., et al.: Top-k spreadsheet-style search for query discovery. In: SIGMOD Conference on Management of Data, pp. 2001–2016, ACM, Melbourne, Australia (2015)

    Google Scholar 

  27. Peng, Y.: A system for query, analysis and visualization of a multi-dimensional relational database (Doctoral dissertation) (2002)

    Google Scholar 

  28. Chau, D.H., et al.: Apolo: making sense of large network data by combining rich user interaction and machine learning. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 167–176. ACM, Vancouver, BC, Canada (2011)

    Google Scholar 

  29. Ahn, J.W., Brusilovsky, P.: Adaptive visualization for exploratory information retrieval. Inf. Process. Manag. 49(5), 1139–1164 (2013)

    Article  Google Scholar 

  30. Ruotsalo, T., et al.: Directing exploratory search with interactive intent modeling. In: 22nd ACM International Conference on Information & Knowledge Management, pp. 1759–1764. ACM, CA, USA (2013)

    Google Scholar 

  31. Glowacka, D., et al.: Directing exploratory search: reinforcement learning from user interactions with keywords. In: International Conference on Intelligent User Interfaces, pp. 117–128. ACM, Tokyo, Japan (2013)

    Google Scholar 

  32. Ruotsalo, T., et al.: Interactive intent modeling: information discovery beyond search. Commun. ACM 58(1), 86–92 (2015)

    Article  Google Scholar 

  33. Klouche, K., et al.: Designing for exploratory search on touch devices. In: 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 4189–4198. ACM, Seoul (2015)

    Google Scholar 

  34. Andolina, S., et al.: Intentstreams: smart parallel search streams for branching exploratory search. In: 20th International Conference on Intelligent User Interfaces, pp. 300–305. ACM, Georgia, USA (2015)

    Google Scholar 

  35. Beier, T., Neely, S.: Feature-based image metamorphosis. In: SIGGRAPH 92, In: Computer Graphics, pp. 35–42 (1992)

    Google Scholar 

  36. Richard A., Jignesh, M.: Data morphing: an adaptive, cache-conscious storage technique. In: 29th International conference on very large database, pp. 417–428. Berlin (2003)

    Google Scholar 

  37. Dhankar, A., Singh, V.: A scalable query materialization algorithm for interactive data exploration. In: 4th IEEE International Conference on Parallel, Grid and Distributed Computing, pp. 128–133. IEEE, India (2016)

    Google Scholar 

  38. Singh, V., Jain, S.K.: A progressive query materialization for interactive data exploration. In: 1st International Workshop, Social Data Analytics and Management (SoDAM’2016) Co-located at 44th VLDB’2016, pp. 1–10. VLDB, India (2016)

    Google Scholar 

  39. Andolina, S., Klouche, K., Cabral, D., Ruotsalo, T., Jacucci, G.: InspirationWall: supporting idea generation through automatic information exploration. In: ACM SIGCHI Conference on Creativity and Cognition, pp. 103–106. ACM (2015)

    Google Scholar 

  40. Zhang, Y., Gao, K., Zhang, B., Li, P.: TimeTree: a novel way to visualize and manage exploratory search process. In: Stephanidis, C. (ed.) HCI 2016. CCIS, vol. 617, pp. 313–319. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40548-3_53

    Chapter  Google Scholar 

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Correspondence to Vikram Singh .

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Patel, J., Singh, V. (2017). Query Morphing: A Proximity-Based Approach for Data Exploration and Query Reformulation. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_26

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  • DOI: https://doi.org/10.1007/978-3-319-71928-3_26

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