Efficiency Improvement of E-Learning Document Search Engine for Mobile Browser

International Journal of Research and Reviews in Computer Science (IJRRCS)

Vol. 2, No. 6, December 2011, ISSN: 2079-2557

© Science Academy Publisher, United Kingdom

www.sciacademypublisher.com

1287

Efficiency Improvement of E-Learning Document Search Engine

for Mobile Browser

Kohei Arai1

and Herman Tolle2

1

Information Science Department, Saga University, Saga, Japan

2

Software Engineering Department, Brawijaya University, Malang, Indonesia

Abstract – E-Learning document is one of the contents usually searched by people in the education area especially in the

university. Searching specific content is not easy for common user of search engine. Sometimes user frustrates on searching

something if they cannot find it in the fast way. We develop a new search engine with improvement from existing search

engine, for helping people searching E-Learning document files efficiently and in the effective way. We use a combination of

searching filtering and relational keyword insertion method build using Yahoo BOSS API and Google Doc API. Experimental

results of the implementation of the E-Learning document search engine system prove that the new system more efficient and

effective to find E-Learning document in the area of computer science as a case, compared to existing search engine. This new

system is also accessible through mobile browser on mobile phone.

Keywords – Search engine, E-learning document, Filtering, Mobile application, Relational keyword, Semantic Search

1. Introduction

It has been estimated that there may be over 4 billion web

documents with over 1 million web sites added daily. How is

it possible to find what we are looked for? A Search Engine

allows you to find information on a multitude of topics.

Search Engines is created by computer programs known as

spiders (also known as crawlers, robots, or simply bots).

These spiders locate web documents and create an index of

words. When a word or words are typed in the search engine,

a list of web sites that contain that word(s) displays. The

words you enter are known as a query [1].

The biggest problem facing users of web search engines

today is the quality of the results they get back. While the

results are often amusing and expand users’ horizons, they

are often frustrating and consume precious time. Google is

one of the search engines that was designed to provide higher

quality search so as the Web continues to grow rapidly,

information can be found easily. While evaluation of a search

engine is difficult, we have subjectively found that Google

returns higher quality search results than current search

engines.

Searching an E-Learning document file using a search

engine is not as simple as common searching process using

current search engine. When using Google or Yahoo as a

search engine for searching E-Learning document file, we

should put an additional keyword for search document in the

specific area also using filtering for search only document

files. For most of web user on search engine, using filtering

and additional keyword is confusing and troublesome. They

need training and practice to be able to use these techniques.

We propose a new method and approach for developing a

new search engine for helping people search E-Learning

document on the Internet. We develop a new system based on

the open search engine API (Application Protocol Interface)

like Google and Yahoo. We create an e-learning specific

search engine with improvement in the efficiency and

effectiveness in searching document file format comparing

with just using original Google or Yahoo. The new system is

also accessible through mobile browser on mobile devices for

support recent and future technology in the mobile area. The

rest of this paper is divided into the following sections:

Section 2 (Background and Related Work): In this

section, we give an overview of, information retrieval,

specific content searching, in addition to the semantic web

for E-learning.

Section 3 (Methodology): In this section, we present the

core contribution of this paper starting with filtering method,

then the tag relational method. It ends with efficiency

improvement with simplification method for document

checking and grabbing.

Section 4 (Experimental Analysis): In this section, we

describe our evaluation methods and results.

Section 5 (Conclusion and Future Work): In this section,

we present the novelty of our research and future work.

2. Background & Related Work

2.1. Information Retrieval

McGill and Salton define Information Retrieval (IR) as “a

field concerned with the structure, analysis, organization,

storage, searching, and retrieval of information [7].” Baeza￾Yates and Ribeiro-Neto linked Information Retrieval to the

user information needs which can be expressed as a query

submitted to a search engine [2]. To accommodate this need,

information has to be first analyzed and structured, then

stored and organized in order to be retrieved. Korfhage [6]

looked at an information retrieval system from three

perspectives: design, evaluation, and usage. Several

evaluation methods have been introduced in the literature,

such as Recall, Precision, F-measure, Harmonic Mean, E

Measure, User- Oriented Measure, expected search length,