Abstract
As the Web enters Big Data age, users and search engines may find it more and more difficult to effectively use and manage such big data. On one hand, people expect to get more accurate information with less search steps. On the other hand, search engines are expected to incur fewer resources of computing, storage and network, while serving the users more effectively. After more and more personal data becomes available, the basic issue is how to generate Cyber-I’s initial models and make the models growable. The ultimate goal is for the growing models to successively approach to or become more similar as individual’s actual characteristics along with increasing personal data from various sources covering different aspects. In this paper, we propose the concept of search pattern, summarize search engines into three search patterns and compare them in order to seek the more efficient one. We propose a new search pattern termed as ExNa, which can be incorporated into search engines to support more efficient search with better results.
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
References
ICSTI Insight: Next generation search. http://www.icsti.org/IMG/pdf/insight_2010_july.pdf
Tunkelang D (2009) Faceted search. In: Synthesis lectures on information concepts, retrieval, and services, vol. 1(1), pp 1–80
Alisi T, Bertini M, D’Amico G, Del Bimbo A, Ferracani A, Pernici F, Serra, G (2009) Sirio: an ontology-based web search engine for videos. In: Proceedings of the 17th ACM international conference on multimedia, pp 967–968
Shehata S, Karray F, Kamel M (2007) Enhancing search engine quality using concept-based text retrieval. In: Proceedings of IEEE/WIC/ACM international conference on web intelligence, pp 26–32
Pilz T, Luther W, Ammon U, Fuhr N (2006) Rule-based search in text databases with nonstandard orthographym. Literary Linguist Comput 21(2):179–186
Wang L, Ke L, Liu P (2015) Compressed sensing of a remote sensing image based on the priors of the reference image. IEEE Geosci Remote Sens Lett 12(4):736–740
Liu P, Yuan T, Ma Y, Wang L, Liu D, Yue S, Kolodziej J (2014) Parallel processing of massive remote sensing images in a GPU archi-tecture. Comput Inform 33(1):197–217
Wang L, Ke L, Liu P, Ranjan R, Chen L (2014) IK-SVD: dictionary learning for spatial big data via incremental atom update. Comput Sci Eng 16(4):41–52
Wang L, Geng H, Liu P, Ke L, Kolodziej J, Ranjan R, Zomaya AY (2015) Particle swarm optimization based dictionary learning for remote sensing big data. Knowl-Based Syst 79:43–50
Yen NY, Ma J, Huang R, Jin Q, Shih TK (2010) Shift to Cyber-I: reexamining personalized pervasive learning. In: Proceedings of the 3rd IEEE/ACM International conference on cyber. Physical and social computing, pp 685–690 December 2010
Wei J, Huang B, Ma J (2009) Cyber-I: vision of the individual’s counterpart on cyberspace. In: Proceedings of the IEEE international conference on dependable. Autonomic and secure computing, pp 295–302
Levene M (2011) An introduction to search engines and web navigation. Wiley
Hu WC, Chen Y, Schmalz MS, Ritter GX (2001). An overview of the world wide web search technologies. In: Proceedings of the 5th world multi-conference on system, cybernetics and information, pp 22–25
Cutting D, Pedersen J (1989) Optimization for dynamic inverted index maintenance. In: Proceedings of the 13th annual international ACM SIGIR conference on research and development in information retrieval, pp 405–411
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323
Acknowledgments
This work was supported in part by the National Science and Technology Major Project under Grant 2013ZX01033002-003, in part by the National High Technology Research and Development Program of China (863 Program) under Grant 2013AA014601, 2013AA014603, in part by National Key Technology Support Program under Grant 2012BAH07B01, in part by the National Science Foundation of China under Grant 61300202, 61300028, in part by the Project of the Ministry of Public Security under Grant 2014JSYJB009, in part by the China Postdoctoral Science Foundation under Grant 2014M560085, the project of Shanghai Municipal Commission of Economy and Information under Grant 12GA-19, and in part by the Science Foundation of Shanghai under Grant 13ZR1452900, 12ZR1411000.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Xu, Z., Wei, X., Chen, D., Chen, H., Liu, F. (2016). Building the Search Pattern of Social Media User Based on Cyber Individual Model. In: Hung, J., Yen, N., Li, KC. (eds) Frontier Computing. Lecture Notes in Electrical Engineering, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-10-0539-8_3
Download citation
DOI: https://doi.org/10.1007/978-981-10-0539-8_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0538-1
Online ISBN: 978-981-10-0539-8
eBook Packages: Computer ScienceComputer Science (R0)