Document Retrieval using Efficient Indexing Techniques: A Review

Shweta Gupta, Sunita Yadav and Rajesh Prasad
Additional contact information
Shweta Gupta: Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India & Dr. A.P.J. Abdul Kalam Technical University, Uttar Pradesh, India
Sunita Yadav: Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India & Dr. A.P.J. Abdul Kalam Technical University, Uttar Pradesh, India
Rajesh Prasad: Department of Computer Science, Yobe State University, Damaturu, Nigeria

International Journal of Business Analytics (IJBAN), 2016, vol. 3, issue 4, 64-82

Abstract: Document retrieval plays a crucial role in retrieving relevant documents. Relevancy depends upon the occurrences of query keywords in a document. Several documents include a similar key terms and hence they need to be indexed. Most of the indexing techniques are either based on inverted index or full-text index. Inverted index create lists and support word-based pattern queries. While full-text index handle queries comprise of any sequence of characters rather than just words. Problems arise when text cannot be separated as words in some western languages. Also, there are difficulties in space used by compressed versions of full-text indexes. Recently, one of the unique data structure called wavelet tree has been popular in the text compression and indexing. It indexes words or characters of the text documents and help in retrieving top ranked documents more efficiently. This paper presents a review on most recent efficient indexing techniques used in document retrieval.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJBAN.2016100104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jban00:v:3:y:2016:i:4:p:64-82

Access Statistics for this article

International Journal of Business Analytics (IJBAN) is currently edited by John Wang

More articles in International Journal of Business Analytics (IJBAN) from IGI Global
Bibliographic data for series maintained by Journal Editor ().