Abstract
Digital data increase on servers with time which resulted in different researchers focusing on this field. Various issues are arising on the server such as data handling, security, maintenance, etc. In this paper, an approach for the document retrieval is proposed which efficiently fetches the document according to the query which is given by user. Here hash-based indexing of the dataset document was done by utilizing term features. In order to provide privacy for the terms, each of this is identified by a unique number and each document has its hash index key for identification. Experiment was done on real and artificial dataset. Results show that NDCG, precision, and recall parameter of the work are better as compared to previous work on different size of datasets.
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
Aparna Humad and Vikas Solanki, “A New Context Based Indexing in Search Engines Using Binary Search Tree”, International Journal of Latest Trends in Engineering and Technology (IJLTET), Vol. 4, Issue 1 May 2014, pp. 26–30.
Souneil Park et al., “Disputant Relation-Based Classification for Contrasting Opposing Views of Contentious News Issues”, IEEE Transactions on Knowledge and Data Engineering, Vol. 25, No. 12, December 2013, pp. 2740–2751.
K. Fragos, P. Belsis, and C. Skourlas, “Combining Probabilistic Classifiers for Text Classification”, 3rd International Conference on Integrated Information Vol. 147 pp. 307–312, 2014.
Jian Ma, et al. “An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection”. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, Vol. 42, No. 3, May 2012.
Jian Ma et al. “An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection”. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, Vol. 42, No. 3, May 2012.
N. Cao et al., “Privacy-Preserving Multikeyword Ranked Search Over Encrypted Cloud Data,” Proc. IEEE InfoCom, pp. 829–837, Apr 2014.
N. Cao, S. Yu, Z. Yang, W. Lou, and Y. Hou, “Lt Codes-Based Secure and Reliable Cloud Storage Service,” Proc. IEEE InfoCom, pp. 693–701, 2012.
Dinesh Nepolean et al. “Privacy Preserving Ranked Keyword Search Over Encrypted Cloud Data”, Vol. 4, No. 11, Nov 2013.
Fabrizio Silvestri, Raffaele Perego and Salvatore Orlando. Assigning Document Identifiers to Enhance Compressibility of Web Search Engines Indexes. In The Proceedings of SAC, 2004.
S. Ramasundaram, “Ngramssa Algorithm for Text Categorization”, International Journal of Information Technology & Computer Science, Vol. 13, Issue No: 1, pp. 36–44, 2014.
Ning Cao et al., “Privacy-Preserving Multi-Keyword Ranked Search Over Encrypted Cloud Data”, IEEE Transaction Parallel and Distributed Systems, Vol. 25, No. 1, Jan 2014.
Yuefeng Li et al., “Relevance Feature Discovery for Text Mining”. IEEE Transactions on Knowledge.
Wenhai Sun et al., “Verifiable Privacy-Preserving Multi-Keyword Text Search in the Cloud Supporting Similarity-Based Ranking”. IEEE Transactions on Parallel and Distributed Systems, Vol. 25, No. 11, November 2014.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, R.K., Patel, D., Bramhe, A. (2019). A Hash-Based Approach for Document Retrieval by Utilizing Term Features. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_55
Download citation
DOI: https://doi.org/10.1007/978-981-10-8055-5_55
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8054-8
Online ISBN: 978-981-10-8055-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)