Abstract
Auto completion is one of the useful features available as a web service. This technique can be implemented in many applications, ranging from a small scale of service like auto complete for items sold in a shop to a large scale of service like Google suggestions which involve suggesting a huge dataset. One of the challenges in implementing auto complete service with a large dataset and a limited computer power is on how to achieve a fast lookup without consuming a lot of memory. This paper presents a data structure that can implement auto complete service that contains up to millions of concepts in a standard computer. The proposed data structure increases the search complexity in return to saving a large amount of memory. A service similar to DBpedia lookup service which contains 9 million words as completion candidates is developed to test the performance of this data structure. The testing shows that such data structure requires less memory than ternary search tree and more importantly a lookup can be performed within milliseconds.
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
Preview
Unable to display preview. Download preview PDF.
References
Bast, H., Weber, I.: Type less, find more: fast autocompletion search with a succinct index. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, Washington, USA, August 06-11 (2006)
Bentley, Sedgewick, R.: Fast algorithms for sorting and searching strings. In: Eighth Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM Pres (1997)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: A Nucleus for a Web of Open Data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)
Mirizzi, R., Ragone, A., Di Noia, T., Di Sciascio, E.: Ranking the Linked Data: The Case of DBpedia. In: Benatallah, B., Casati, F., Kappel, G., Rossi, G. (eds.) ICWE 2010. LNCS, vol. 6189, pp. 337–354. Springer, Heidelberg (2010)
Softic, S., Taraghi, B., Halb, W.: Weaving Social E-Learning Platforms Into the Web of Linked Data. In: Proceedings of I-SEMANTICS 2009 - International Conference on Semantic Systems, pp. 559–567 (2009)
Bast, H., Weber, I.: When you ’re lost for words: Faceted search with autocompletion. In: SIGIR 2006 Workshop on Faceted Search, Seattle, Washington, USA (August 2006)
Acharya, A., Zhu, H., Shen, K.: Adaptive Algorithms for Cache-Efficient Trie Search. Selected Papers from the International Workshop on Algorithm Engineering and Experimentation, January 15-16, pp. 296–311 (1999)
Aoe, J.-I., Morimoto, K., Shishibori, M., Park, K.-H.: A Trie Compaction Algorithm for a Large Set of Keys. IEEE Transactions on Knowledge and Data Engineering 8(3), 476–491 (1996)
Kastrinakis, D., Tzitzikas, Y.: Advancing Search Query Autocompletion Services with More and Better Suggestions. In: Benatallah, B., Casati, F., Kappel, G., Rossi, G. (eds.) ICWE 2010. LNCS, vol. 6189, pp. 35–49. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Boo, V.K., Anthony, P. (2012). A Data Structure between Trie and List for Auto Completion. In: Lukose, D., Ahmad, A.R., Suliman, A. (eds) Knowledge Technology. KTW 2011. Communications in Computer and Information Science, vol 295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32826-8_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-32826-8_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32825-1
Online ISBN: 978-3-642-32826-8
eBook Packages: Computer ScienceComputer Science (R0)