Abstract
Query autocompletion (QAC) is an important interactive feature that assists users in formulating queries and saving keystrokes. Due to the convenience it brings to users, it has been adopted in many applications, such as Web search engines, integrated development environments (IDEs), and mobile devices. In my previous works, I studied several fundamental problems of QAC and developed novel QAC techniques that deliver high-quality suggestions in an efficient way. The remarkable contribution is the proposal of a novel QAC paradigm through which users may abbreviate keywords by prefixes and do not have to explicitly separate them. Another contribution is to efficiently solve geographical location constraints such as considering Euclidean distances to different locations when completing text queries. Based on the above studies, an overview of novel QAC methods across different application domains is provided in this chapter. By creating a data circulation on various QAC applications, I believe that the proposed methods are practical and easy to use in many real-world scenarios. I illustrate the realized data circulation in Sect. 2. Contributions to the society are presented in Sect. 3.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Asaduzzaman M, Roy CK, Schneider KA, Hou D (2014) CSCC: simple, efficient, context sensitive code completion. In: Proceedings 2014 IEEE international conference on software maintenance and evolution, pp 71–80. https://doi.org/10.1109/ICSME.2014.29
Cai F, de Rijke M (2016) A survey of query auto completion in information retrieval. Found Trends Inform Retr 10(4):273–363. https://doi.org/10.1561/1500000055
Chaudhuri S, Kaushik R (2009) Extending autocompletion to tolerate errors. In: Proceedings 2009 ACM SIGMOD international conference on management of data, pp 707–718, https://doi.org/10.1145/1559845.1559919
Han S, Wallace DR, Miller RC (2011) Code completion of multiple keywords from abbreviated input. Autom Softw Eng 18(3–4):363–398. https://doi.org/10.1007/s10515-011-0083-2
Hu S (2019) Efficient text autocompletion for online services. Doctoral Thesis at Graduate School of Information Science, Nagoya University. https://www.irdbniiacjp/01152/0004293757
Hu S, Xiao C, Ishikawa Y (2018) An efficient algorithm for location-aware query autocompletion. IEICE Trans Inf Sys 101-D(1):181–192. http://search.ieice.org/bin/summary.php?id=e101-d_1_181
Hu S, Xiao C, Ishikawa Y (2019a) Scope-aware code completion with discriminative modeling. J Inf Proc 27:469–478. https://doi.org/10.2197/ipsjjip.27.469
Hu S, Xiao C, Qin J, Ishikawa Y, Ma Q (2019b) Autocompletion for prefix-abbreviated input. In: Proceedings 2019 ACM SIGMOD international conference on management of data, pp 211–228. https://doi.org/10.1145/3299869.3319858
Ji S, Li C (2011) Location-based instant search. In: Proceedings 2011 international conference on scientific and statistical database management, pp 17–36. https://doi.org/10.1007/978-3-642-22351-8_2
Nguyen AT, Nguyen TN (2015) Graph-based statistical language model for code. In: Proc. 37th international conference on software engineering (ICSE), pp 858–868. https://doi.org/10.1109/ICSE.2015.336
Nguyen AT, Nguyen TT, Nguyen HA, Tamrawi A, Nguyen HV, Al-Kofahi JM, Nguyen TN (2012) Graph-based pattern-oriented, context-sensitive source code completion. In: Proceedings 34th international conference on software engineering (ICSE), pp 69–79. https://doi.org/10.1109/ICSE.2012.6227205
Nguyen TT, Nguyen AT, Nguyen HA, Nguyen TN (2013) A statistical semantic language model for source code. In: Proceedings 9th joint meeting on foundations of software engineering, pp 532–542. https://doi.org/10.1145/2491411.2491458
Reynolds DA (2009, Springer) Gaussian mixture models. Encyclopedia of Biometrics 741
Roy SB, Chakrabarti K (2011) Location-aware type ahead search on spatial databases: Semantics and efficiency. In: Proceedings 2011 ACM SIGMOD international conference on management of data, pp 361–372. https://doi.org/10.1145/1989323.1989362
Schaback J, Li F (2007) Multi-level feature extraction for spelling correction. In: Proceedings IJCAI-2007 workshop on analytics for noisy unstructured text data, pp 79–86
Zheng Y, Bao Z, Shou L, Tung AKH (2015) INSPIRE: a framework for incremental spatial prefix query relaxation. IEEE Trans Knowl Data Eng 27(7):1949–1963. https://doi.org/10.1109/TKDE.2015.2391107
Zhong R, Fan J, Li G, Tan K, Zhou L (2012) Location-aware instant search. In: Proceedings 21st ACM international conference on information and knowledge management, pp 385–394. https://doi.org/10.1145/2396761.2396812
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Hu, S. (2021). Efficient Text Autocompletion for Online Services. In: Takeda, K., Ide, I., Muhandiki, V. (eds) Frontiers of Digital Transformation. Springer, Singapore. https://doi.org/10.1007/978-981-15-1358-9_11
Download citation
DOI: https://doi.org/10.1007/978-981-15-1358-9_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1357-2
Online ISBN: 978-981-15-1358-9
eBook Packages: Computer ScienceComputer Science (R0)