Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Computational Intelligence: Theories, Applications and Future Directions - Volume I

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 798))

  • 698 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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)

    Google Scholar 

  2. Cetintemel, U., et al.: Query Steering for Interactive Data Exploration. In: CIDR (2013)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. Drosou, M., Evaggelia, P.: YmalDB: exploring relational databases via result-driven recommendations. The VLDB 22(6), 849–874 (2013)

    Article  Google Scholar 

  5. 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)

    Google Scholar 

  6. White, R.: Interactions with search systems. Cambridge University Press (2016)

    Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. 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)

    Google Scholar 

  9. Rocchio, J.: Relevance feedback in information retrieval. The Smart retrieval system-experiments in automatic document processing, pp. XXIII-1–XXIII-11 (1971)

    Google Scholar 

  10. Beier, T., Neely, S.: Feature-based image metamorphosis. In: ACM SIGGRAPH Computer Graphics, vol. 26, no. 2, pp. 35–42. ACM (1992)

    Article  Google Scholar 

  11. 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)

    Chapter  Google Scholar 

  12. Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. In: Readings in Information Retrieval, vol. 24, no. 5, pp. 355–363 (1997)

    Google Scholar 

  13. 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)

    Article  Google Scholar 

  14. Yu, J.X., Qin, L., Chang, L., Ozsu, M.T.: Keyword Search in Databases (Synthesis Lectures on Data Management) (2010)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. 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)

    Google Scholar 

  19. 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)

    Google Scholar 

  20. 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)

    Google Scholar 

  21. Bonifati, A., Ciucanu, R., Staworko, S.: Interactive inference of join queries. In: Gestion de Données-Principes, Technologies et Applications (BDA) (2014)

    Google Scholar 

  22. 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)

    MATH  Google Scholar 

  23. 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)

    Google Scholar 

  24. 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)

    Google Scholar 

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

    Article  Google Scholar 

  26. Hellerstein, J.M., Haas, P.J., Wang, H.J.: Online aggregation. In: Proceedings of the ACM SIGMOD Conference on Management of Data (1997)

    Google Scholar 

  27. Qarabaqi, B., Riedewald, M.: User-driven refinement of imprecise queries. In: Proceedings of the International Conference on Data Engineering (ICDE) (2014)

    Google Scholar 

  28. 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)

    Google Scholar 

  29. Ruotsalo, T., Jacucci, G., Myllymäki, P., Kaski, S.: Interactive intent modeling: information discovery beyond search. Commun. ACM 58(1), 86–92 (2015)

    Article  Google Scholar 

  30. 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)

    Google Scholar 

  31. 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)

    Google Scholar 

  32. 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)

    Google Scholar 

  33. 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)

    Google Scholar 

  34. 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)

    Google Scholar 

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

    Article  Google Scholar 

  36. 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)

    Google Scholar 

  37. 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)

    Article  Google Scholar 

  38. 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)

    Google Scholar 

  39. 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)

    Google Scholar 

  40. 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)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, National Institute of Technology, Kurukshetra, 136119, Haryana, India

    Jay Patel & Vikram Singh

Authors
  1. Jay Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vikram Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vikram Singh .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Nishchal K. Verma

  2. Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    A. K. Ghosh

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

Publish with us

Policies and ethics

Search

Navigation