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IJIET 2020 Vol.10(7): 516-522 ISSN: 2010-3689
doi: 10.18178/ijiet.2020.10.7.1417

Traits of Interval Tree in Solving Blind Search Problems of Finding a Term in an Ordered Data Set

Xingbo Wang and Jicong Wu

Abstract—This paper investigates the traits of the interval tree in solving the blind-searched problems of finding uninformed terms in an ordered data set. It first proves several new properties of the interval tree and then shows that applying an interval tree to express data set results in half of the objective terms lying on the bottom level while another half lying on the levels over the bottom, and a bigger probability as well as half or less than half a amount of searching steps to find an objective term in comparison to conventional search strategies. Mathematical reasoning on the new properties of the interval tree plus conclusions related with the distribution of the objective terms on the interval tree is shown in detail and searching strategy is proposed in the end. The results in this paper are helpful for designing a searching algorithm.

Index Terms—Artificial intelligence, blind search, binary tree, probability, algorithm.

Xingbo Wang is with Department of Mechatronic Engineering, Foshan University, Foshan, China (e-mail: 153668@qq.com). Jicong Wu is with State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China.


Cite:Xingbo Wang and Jicong Wu, "Traits of Interval Tree in Solving Blind Search Problems of Finding a Term in an Ordered Data Set," International Journal of Information and Education Technology vol. 10, no. 7, pp. 516-522, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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