UTHM Institutional Repository

Comparative study on data searching in linked list & B-tree and B+tree techniques

S Giuma, Ahmed Eshtewi (2015) Comparative study on data searching in linked list & B-tree and B+tree techniques. Masters thesis, Universiti Tun Hussein Onn Malaysia.

[img]
Preview
PDF
AHMED_ESHTEWI_S_GIUMA.pdf

Download (1MB)

Abstract

There are many methods of searching large amount of data to find one particular piece of information. Such as finding the name of a person in a mobile phone record. Certain methods of organizing data make the search process more efficient. The objective of these methods is to find the element with the least time. In this study, the focus is on time of search in large databases, which is considered an important factor in the success of the search. The goal is choosing the appropriate search techniques to test the time of access to data in the database and what is the ratio difference between them. Three search techniques are used in this work namely; linked list, B-tree, and B+ tree. A comparison analysis is conducted using five case databases studies. Experimental results reveal that after the average times for each search algorithms on the databases have been recorded, the linked list requires lots of time during search process, with B+ tree producing significantly low times. Based on these results, it is clear that searching in B- tree is faster than linked list at a ratio of (1: 5). The searching time in a B+ tree is faster than B- tree at the ratio of (1: 2). The searching time in a B+ tree is faster than linked list at the ratio of (1: 8). With that, it can be concluded that B+ tree is the fastest technique for data access.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > QA75 Calculating machines
Depositing User: Normajihan Abd. Rahman
Date Deposited: 27 May 2015 07:24
Last Modified: 27 May 2015 07:24
URI: http://eprints.uthm.edu.my/id/eprint/6933
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year