Skip to main content

User Query Optimisation: A Creative Computing Approach

  • Conference paper
  • First Online:
Software Engineering and Methodology for Emerging Domains (NASAC 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 675))

Included in the following conference series:

  • 499 Accesses

Abstract

User query acts as an important role in software systems. In order to optimise user query and provide a better user experience, this paper introduces a creative computing approach to user query optimisation. We develop three creativity rules, i.e., combinational, transformational and exploratory rules, and corresponding algorithms to provide a robust and scalable solution for user query optimisation. A poetry analysis system is chosen as a case study to show that it better delineates more possibility, diversity and deepness of user query. We believe the proposed approach has potential applications to academic research of texts and digital art areas.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Bagnall, A.J., Rayward-Smith, V.J., Whittley, I.M.: The next release problem. Inf. Softw. Technol. 43(14), 883–890 (2001)

    Article  Google Scholar 

  2. Harman, M., Jones, B.F.: Search-based software engineering. Inf. Softw. Technol. 43(14), 833–839 (2001)

    Article  Google Scholar 

  3. Harman, M.: The current state and future of search based software engineering. In: 29th International Conference on Software Engineering, Future of Software Engineering, pp. 342–357. IEEE Press, Washington (2007)

    Google Scholar 

  4. Yuanyuan, Z., Finkelstein, A., Harman, M.: Search based requirements optimisation: existing work and challenges. In: Paech, B., Rolland, C. (eds.) REFSQ 2008. LNCS, vol. 5025, pp. 88–94. Springer, Heidelberg (2004)

    Google Scholar 

  5. Harman, M., Clark, J.: Metrics are fitness functions too. In: 10th International Conference on Software Metrics Symposium, pp. 58–69. IEEE Press, California (2004)

    Google Scholar 

  6. Yann, C., Siarry, P.: Multiobjective Optimization: Principles and Case Studies. Springer, Heidelberg (2013)

    MATH  Google Scholar 

  7. Szidarovszky, F., Gershon, M.E., Duckstein, L.: Techniques for Multi-Objective Decision Making in Systems Management. Elsevier, New York (1986)

    MATH  Google Scholar 

  8. Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)

    MathSciNet  Google Scholar 

  9. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  10. Trummer, I., Koch, C.: Multi-objective parametric query optimization. Sigmod Rec. 45(1), 24–31 (2015)

    Article  Google Scholar 

  11. Rungsawang, A., Tangpong, A., Laohawee, P., Khampachu, T.: Novel query expansion technique using apriori algorithm. In: The Eighth Text Retrieval Conference (TREC 8), pp. 453–456. NIST, United Kingdom (1999)

    Google Scholar 

  12. Qin, Z., Liu, L., Zhang, S.: Mining term association rules for heuristic query construction. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 145–154. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24775-3_18

    Chapter  Google Scholar 

  13. Jie, W., Stephane, B., Beng, C.O.: Mining term association rules for automatic global query expansion: methodology and preliminary results. In: First International Conference on Web Information Systems Engineering (WISE 2000), p. 366. IEEE Press, Washington (2000)

    Google Scholar 

  14. Saliu, M.O., Ruhe, G.: Bi-objective release planning for evolving software systems. In: 6th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 105–114. ACM, New York (2007)

    Google Scholar 

  15. Yuanyuan, Z., Harman, M., Mansouri, S.A.: The multi-objective next release problem. In: 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1129–1137. ACM, New York (2007)

    Google Scholar 

  16. Boden, M.A.: The Creative Mind: Myths and Mechanisms, 2nd edn. Routledge, London (2004)

    Google Scholar 

  17. Boden, M.A.: Computer models of creativity. AI Mag. 30(3), 23–34 (2009)

    Google Scholar 

  18. Python Language. https://www.python.org/

  19. Python Standard Library. https://docs.python.org/2/library/

  20. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xuan Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Wang, X., Yang, H. (2016). User Query Optimisation: A Creative Computing Approach. In: Zhang, L., Xu, C. (eds) Software Engineering and Methodology for Emerging Domains. NASAC 2016. Communications in Computer and Information Science, vol 675. Springer, Singapore. https://doi.org/10.1007/978-981-10-3482-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3482-4_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3481-7

  • Online ISBN: 978-981-10-3482-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics