Gaughran: Binary Search Trees
Binary Search Trees as a Sorting
Mechanism
Peter Gaughran
Second Computer Science and Software Engineering
SE214 Communication Skills
Technical Report
April 2005
Department of Computer Science
National University of Ireland, Maynooth
Co. Kildare
Ireland
Gaughran: Binary Search Trees
Abstract
Searching large files of data is common in information systems. For efficiency, the
data should be sorted into a particular order on some key field so that fast search
techniques can be applied. One way of sorting records is using a binary search tree.
The records are read from the file and are inserted into the binary tree so they are inorder. The tree is then traversed and at each node visited the record is written to a
new, sorted file. Although effective, using a binary search tree to sort data has
disadvantages of memory use and speed over other methods such as quick sort.
Gaughran: Binary Search Trees
Contents
Chapter 1: Introduction
1.1
Data and Information Processing ............................................................... 1
1.2
Searching Data ........................................................................................... 1
1.3
Sorting Data ............................................................................................... 2
1.4
Report Structure ......................................................................................... 3
Chapter 2: Sorting using a Binary Search Tree
2.1
Binary Trees ............................................................................................... 4
2.2
Binary Search Trees ................................................................................... 5
2.3
Insertion into Binary Search Trees............................................................. 6
2.4
Traversal of Binary Search Trees............................................................... 8
2.5
Sorting strings using a Binary Search Tree ................................................ 9
2.6
Case study: Sorting the counties of Ireland ............................................. 10
Chapter 3: Conclusions
3.1
Binary Search Trees as a sorting mechanism........................................... 11
3.2
Drawbacks of Binary Search Trees. ......................................................... 11
3.3
Other sorting methods compared. ............................................................ 12
3.4
Conclusion ............................................................................................... 12
References ............................................................................................................. 13
Gaughran: Binary Search Trees
Chapter One: Introduction
1.1 Data and Information Processing
Please note that the text included here has nothing to do with the supposed title of this
report. It is here just to give you a style template for the preparation of your own
reports. The styles available in this document have been set up to give your report the
prescribed style, namely:

Heading 1 (for chapter titles),

Heading 2 (for section titles),

Heading 3 (for sub-section titles),

Normal (for main text),

Caption (to label illustrations),

Header,

Footer, and

Page Number.
Your report should include illustrations, equations, formulae, tables and so on where
appropriate. It is usually easiest to have each illustration, equation etc. as a paragraph
of its own, making sure the "float over text" option is not selected. The illustration
should be centered between the margins, as here, and can be resized if necessary by
dragging the corner.
5
x 10
3.811
3.8105
3.81
3.8095
3.809
3.43
3.4305
3.431
3.4315
3.432
3.4325
5
x 10
Figure 1: All illustrations should be centered and should have captions.
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Gaughran: Binary Search Trees
Diagrams can be drawn within word (using Insert - Object - Microsoft Word Picture
menu option) using the drawing tools provided. Similarly, an equation editor is
available (using Insert - Object - Microsoft Equation menu option). All equations and
formulas should be numbered so they can be referred to in the text.
F u  

 f u e
 j 2ux
dx
(1)

Photos and drawings already created can be imported from a file (using Insert - Picture
- From File…).
References should be given in the text at the appropriate place like this [2].. Look at
the end to see how they are to be listed.
1.2 Marking scheme
Here is the marking scheme I am using for this course.
Introduction
15
Technical clarity
10
Critical Evaluation
15
Use of English
10
Use of illustrations etc.
10
General presentation of report
10
Poster
10
Oral Presentation
15
Job applications/CVs
5
Table 1: Marking scheme for SE214.
1.3 Poster Presentation
You are required to create a poster presentation summarising the material in your
technical report. Your poster presentation should be A2 size (i.e. four sheets of A4
paper). Your poster should be complete and understandable on its own. It should be
less text-based than your report - summarise using bulleted points and/or short
paragraphs of text. Your poster should be legible from about a metre distance so use a
text point size of about 18 to 24. Diagrams and illustrations with appropriate captions
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Gaughran: Binary Search Trees
are often better than text. For examples of what is required, you should (critically)
look at the framed posters in the department.
The materials and equipment to mount your poster will be available on Tuesday from
1.30pm and Wednesday morning in the foyer of the Callan Building, namely:

Blue boards (A2 size)

Sticky velcro pads

Pritt stick glue

Guillotine

Display stands.
I will give a demonstration of mounting a poster at 1.30pm on Tuesday in the Callan
foyer. Your poster should be on display by 12noon on Wednesday 3rd May. The
external examiner wants to see the posters so I will remove them after they have been
marked. If you want your poster back, it will be available from me after the exam
results are published.
Mounting instructions for posters
2 Take an A2 sized blue board
3 Trim about 3mm of each edge of your four A4 sheets using the guillotine.
4 Glue your four sheets onto the blue board so the blue shows around the edge of
each sheet. Be careful that the sheets are all parallel and equally spaced.
5 Take four sticky velcro squares of hooks and stick to each corner of the back of
the blue board.
6 Attach your completed poster to a vacant display stand - the hooks will cling to the
material.
You are required to give a four-minute presentation on the topic in your research
report. Here are a few pointers that may help you give a professional and impressive
presentation.

Be professional: have well-prepared slides that use colour, diagrams and text in
appropriate ways to bring your message across. I suggest you prepare your slides
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Gaughran: Binary Search Trees
using Microsoft Powerpoint. A computer and projector will be available for you to
show these directly to the audience.

Remember, you only have four minutes so don't have too much material to
present. Practice with a clock or watch, preferably in front of other people
(classmates), to check your talk is about the right length.

In the talk, you will be informed after three minutes that there is one minute left.
You will be cut off if you go more than a few seconds over the four-minute timelimit.

Prepare slides that are not overloaded with material. The slides should outline
what you are saying using phrases and diagrams rather tan being a word-by-word
script of what you say.

Don’t depend too much on your slides: your speech is the presentation and the
slides support you (not the other way around).

Although we only have four minutes, don't rush, take your time: pause frequently.
Sometimes, the best thing to say is nothing.
Short one-second rests create
dramatic impact and also give your audience time to assimilate what you’ve said.
Of course, you also have to maintain continuity and flow; otherwise people forget
what you are talking about. It’s a question of balance.

Arrive early and make sure you know where all the equipment is. Know how to
use it.

Look mostly at the AUDIENCE, not at your slides or the screen behind you.

Project your voice (but don’t shout).

Smile: enjoy giving your presentation.

Be confident: you've written a report on your topic so you know what you are
talking about – here is your opportunity to show it.

The people in the audience are on your side (though sometimes they disguise it
well!)
They want you to succeed.
If they ask you a question you don’t
understand, say so and ask their help. Ask them to explain, and ask nicely. If you
still don’t understand, don’t bluff. Admit your ignorance and suggest ways of how
you will overcome that lack of knowledge. You are not being assessed on your
technical knowledge - just on your presentation and communication skills.
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Gaughran: Binary Search Trees

Nobody knows everything; but that’s no excuse for not trying to know everything.
A knowledgeable person knows enough to do his job well, a wise person knows
that he doesn’t know everything, and an intelligent person knows how to find out
what he doesn’t know. Be knowledgeable, wise, and intelligent in your
presentation and answers to questions.

Has the student covered all of the relevant issues?

Was the presentation clear and concise?

Was the student audible?

Did the student make effective use of audiovisual
Hard to hear, no supporting material
1
Barely covered the material; uninspiring delivery
Covered the material; audible and clear

Was he confident of his subject matter?

Did he answer questions well (i.e. confidently, not
Did the student make the subject matter interesting?
If you knew nothing about the project, would you have
much
from
the
Ditto; also made the subject interesting
5
8
9
Yes to all these questions.
examples.
6
7
presentation?
The rest of this text is here just to give more
4
5
Complete coverage; audible, clear, confident
necessarily completely)?
learned
2
3
aids?

0
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Gaughran: Binary Search Trees
Chapter Two: Binary Trees
Automatic structuring (feature coding and object recognition) of topographic data,
such as that derived from air survey or raster scanning large-scale paper maps,
requires the classification of objects such as buildings, roads, rivers, fields and
railways. Shape and context are the main attributes used by humans. Our project
combines shape recognition techniques developed for computer vision and contextual
models derived from statistical language theory to recognise objects. This paper
describes the measurement of shape to characterise features that will then be used as
input into a graphical language model.
Much work has been done in computer vision on the identification and classification
of objects within images. However, less progress has been made on automating
feature extraction and semantic capture in vector graphics. This is partly because the
low-level graphical content of maps has often been captured manually (on digitising
tables etc.) and the encoding of the semantic content has been seen as an extension of
this. However, the successful automation of raster-vector conversion plus the large
quantity of new and archived graphical data available on paper makes the automation
of feature extraction desirable.
Feature extraction and object recognition are large research areas in the field of image
processing and computer vision. Recognition is largely based on the matching of
descriptions of shapes. Numerous shape description techniques have been developed
in computer vision, such as, boundary chain coding, analysis of scalar features
(dimension, area, number of corners etc), Fourier descriptors and moment invariants.
These techniques are well understood when applied to images and have been
developed to describe shapes irrespective of position, orientation and scale. They can
also be easily applied to vector graphical shapes.
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Gaughran: Binary Search Trees
References
[1] H.M. Deital and P.J. Deital [1998]: C++ How To Program, 2nd edition, Prentice
Hall, 1998.
[2] L. Keyes and A.C. Winstanley [1999a]: Using Fourier Descriptors for Classifying
Shapes on Large Scale Maps, Proceedings of the GIS Research UK 7th Annual
Conference, 87-90, Southampton, April 1999.
[3] L. Keyes and A.C. Winstanley [1999b]: Fourier Descriptors as a General
Classification Tool for Topographic Shapes, IMVIP '99 Proceedings of the
Irish Machine Vision and Image Processing Conference, 193-203, Dublin City
University, 1999.
[4] M. Monaghan, M.Frain and A.C. Winstanley [1999]: Map Feature Recognition
Using Neural Networks, Proceedings of the GIS Research UK 7th Annual
Conference, 87-90, Southampton, April 1999.
[5] A.C. Winstanley [1998]: Structuring Vector Maps Using Computer Vision
Techniques, Proceedings of the Association for Geographic Information
Conference, 8.11.1-1.11.2, Birmingham, 1998.
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