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American Journal of Engineering Research (AJER)
2013
w w w . a j e r . o r g
Page 98
American Journal of Engineering Research (AJER)
e-ISSN : 2320-0847 p-ISSN : 2320-0936
Volume-02, Issue-12, pp-98-109
www.ajer.org
Research Paper Open Access
Design, Analysis and Implementation of a Search Reporter
System (SRS) Connected to a Search Engine Such as Google
Using Phonetic Algorithm
Md. Palash Uddin1, Mst. Deloara Khushi2, Fahmida Akter3, Md. Fazle Rabbi4
1&4Computer Science and Information Technology, Hajee Mohammad Danesh Science and Technology
University (HSTU), Dinajpur, Bangladesh.
2Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
3Computer Science and Engineering, East Delta University, Chittagong, Bangladesh.
Abstract: - A web search engine is an information retrieval system on the World Wide Web showing a list of
necessary and less necessary URLs against the searching keywords elapsing a more time and requiring some
analysis to get the required URL. The web creates new challenges for required information retrieval because the
amount of information on the web is growing rapidly. Thus the target of SRS connected to a search engine such
as Google is to get the required information though the searching process more easily, effectively and
efficiently. For this, admin should insert the searching keywords in the SRS and then the SRS connected to any
search engine will get all the titles, URLs and descriptions and then check and count the keywords in the web
pages of the URLs. Then these retrieved information and an id associated with each keyword are stored in the
database. Now, if a user searches for the keyword, then the SRS loads the search results from database and then
ranks the pages based on its highest number of matching keywords. More significantly, a phonetic algorithm
namely Metaphone algorithm is used in the SRS to eliminate the problem of spelling errors in the keywords
given by the users. Admin can update existing keyword with the rapid updating of information based on the
keyword and also add new keywords that are not found by the users. The SRS with necessary analysis has been
implemented using appropriate and latest demanding tools and technologies such as Metaphone algorithm,
HTML, PHP, JavaScript, CSS, MYSQ, Apache server etc.
Keywords: - Information Retrieval, Metaphone Algorithm, Phonetic Algorithm, Search Engine, Search
Reporter, Spelling Errors
I. INTRODUCTION
Search Reporter System (SRS) along with a search engine offers that when we search for any keyword,
then it provides the search result exactly related to the keywords. The SRS along with a search engine is very
user friendly and simple also. A user can easily search and get the informative result from this system. The SRS
is designed in such a way that a user will never feel boring with the system. If a user searches for a keyword,
then the search reporter loads the search results from any search engine such as Google and then ranking the
pages based on its information that means the most informative page will have rank 1, then the second
informative page rank 2 and so on. In this way a user get the main information which he/she is actually looking
for. There are three steps in this system: search any keyword in the system, get the results as they want from
Google based on ranking, and open the links and get the best information. It provides an outstanding web based
search interface. The salient features of the SRS are given below:
It saves large amount of time.
It offers the informative result without analysis all the results get from Google.
It helps the users who actually don’t know about searching.
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2013
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1.1 History of Search Engine and Development of SRS
In the summer of 1993, no search engine existed for the web, though numerous specialized catalogues
were maintained by hand. Oscar Nierstrasz at the University of Geneva wrote a series of Perl scripts that
periodically mirrored these pages and rewrote them into a standard format. This formed the basis for
W3Catalog, the web's first primitive search engine, released on September 2, 1993 [1]. The web's second search
engine Aliweb appeared in November 1993. One of the first "all text" crawler-based search engines was
WebCrawler, which came out in 1994. Google adopted the idea of selling search terms in 1998, from a small
search engine company named goto.com. Around 2000, Google's search engine rose to prominence [1]. The
company achieved better results for many searches with an innovation called PageRank. By 2000, Yahoo! was
providing search services based on Inktomi's search engine. Yahoo! acquired Inktomi in 2002, and Overture
(which owned Allthe Web and AltaVista) in 2003. Yahoo! switched to Google's search engine until 2004, when
it launched its own search engine based on the combined technologies of its acquisitions. Microsoft's rebranded
search engine, Bing, was launched on June 1, 2009. On July 29, 2009, Yahoo! and Microsoft finalized a deal in
which Yahoo! Search would be powered by Microsoft Bing technology. By the passing of time the use of search
engine is increasing. As increased use of search engine for searching information, a system has been developed
that helps users to search information. When a person wants to search anything he simply places his words in
search engine. Then search engine returns him relevant information according to his/her words based on many
more criteria. But user has to extract their necessary information after doing much analysis as search engines
can’t give the exact information manually. This makes searching for any information very time consuming.
Then we thought that we may develop search reporter system so that users may search and get any information
manually and which is not time consuming. Search engines use many criteria such as SEO (Search Engine
Optimization), searching and returning information but we choose primarily only the words that are given for
searching. On the stage of developing the SRS at first, admin places a keyword in the field that is defined for
him. Then the system which is connected to any search engine such as Google will get all the titles, URLs and
descriptions and then check and count the keyword in the web pages of the URLs. Then the titles, URLs,
descriptions, number of matches of the keyword and an associated id against the keyword are stored in database.
Now, if a user searches for a keyword, then the search reporter loads the search results from database and then
ranking the pages based on its highest number of matching keywords. As search engines updated their
information day by day, so the admin needs to update the database of SRS day by day so that user gets the
updated information from the SRS.
1.2 Present Search Engine
Generally, a search engine is software code that is designed to search for information on the World Wide Web
[1]. The search results are generally presented in a line of results often referred to as search engine results pages
(SERP's). The structure diagram of present system is shown below:
Figure 1: Structure Diagram of Search Engine
The structure diagram of present system shows that it contains resources such as server. Then
information from resources come to crawler, harvester and import. A Web Crawler is a computer program that
browses the World Wide Web in a methodical, automated manner or in an orderly fashion. Web crawlers are
mainly used to create a copy of all the visited pages for later processing by a search engine that will index the
downloaded pages to provide fast searches [2]. A Web Harvester extracts every single word every time it
accesses a webpage. Additionally, a web harvester stores every single page harvested as a separate version in
our database. It has two main advantages. These are Analytic capabilities and Versioning of Web Pages [3]. A
American Journal of Engineering Research (AJER)
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Web Import imports information from server and provide it to search engine. Users request information from
search engine by query and search engine them information by response.
An information flow diagram (IFD) shows the relationship between external and internal information
flows between organizations. It also shows the relationship between the internal departments and sub-systems
[4]. The IFD for present system is shown below:
Figure 2: IFD of Search Engine
1.3 Limitation of the Present System
Time consuming: In present system, information retrieval is very time consuming.
Complex queries: The queries are very complex in present system.
Less user interactivity: In present system, user interactivity is very poor.
Scam: As more visitors , there is a chance to more scam and sometimes it might become cost efficient as more and
more people to advertise a website online.
Learning curve: Using engines does involve a learning curve. Many beginning Internet users, because of these
disadvantages, become discouraged and frustrated.
Sophistication: Regardless of the growing sophistication, many well thought-out search phrases produce list after list
of irrelevant web pages. The typical search still requires sifting through dirt to find the gems.
Overload: Search engine creates information overload.
1.4 Proposed SRS
To minimize the problems as specified above we are proposing the following structure and the IFD of the SRS.
Figure 3: Structure Diagram of SRS
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Figure 4: IFD of SRS
1.5 Objectives of SRS
The SRS is an open system. Here the user who wants to search anything, he/she just needs to visit the website
and searches the keyword which he/she actually wants. The main objectives of this system are:
to save time
to help those users who don’t know about searching
to give the users meaningful information which they need
to place the less important information in the last position
Also the SRS possesses the following advantages:
It offers instant services, no need of time consuming
The new users can easily understand the system and maintain it easily
The system enables the users to precisely describe the information that they seek
The SRS reduces the problem of sophistication and scam of present search engines.
II. DESIGN METHODOLOGY
The purpose of system design is to create a technical solution that serves both the user and the admin.
The system should be designed in such a way that is very flexible to use for both the administrator and the user.
The preparation of the environment needed to build the system, the testing of the system and the migration and
the preparation of the data that will ultimately be used by the system are equally important. In addition to
designing the technical solution, system design is the time to initiate focused planning efforts for both the testing
and data preparation activates. The SRS is a real life problem solving application. Both the admin section and
the user section are designed in such a way that both parties enjoy the facilities of the application.
2.1 Modular Design
The whole system is divided into two parts i.e. the user and the admin section. That is why, the modular design
of the system is also divided into two modular diagrams.
Figure 5(a): Modular diagram for user Figure 5(b): Modular diagram for admin
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2.2 Use-case Diagram
It covers the whole SRS and how it works. It makes easier to communicate between the user and the system
developers. The two main components of a use case diagram are actors and use cases as specified in the
following diagram.
Actor Use-case
Figure 6: Symbol of Actor and Use-case
Figure 7: Use-case Diagram for Admin and User
2.3 Working Stucture
The SRS works in the following way:
Figure 8: Working Structure of SRS
The working steps are in details:
Create an index.
Receive a query-a set of search terms
Look in the index file for matches
Gather the matching page entries and rank them by number of keyword matches
Format the result
Return the result page in HTML to the searcher’s web browser.
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2.4 Data Flow Diagram (DFD)
The level-0, level-1, and level-2 DFD of the SRS are shown below:
Figure 9: Level-0 Data Flow Diagram
Figure 10: Level-1 Data Flow Diagram
Figure 11: Level-2 Data Flow Diagram
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2.5 Relational Diagram
The relational or schema diagram among the table used in the SRS is shown below:
Figure 12: Relational Diagram of SRS
2.6 Activity Diagram
The activity at different level of the system can be shown in the following diagram:
Figure 13: Activity Diagram of SRS
2.7 Architecture Diagram
Architecture Diagram of SRS designed for searching shows the different components of the system.
We have populated the central database of the system by MySql database and populated it in the MySql
Essential-5.0.67. The web server used is Xampp-1.7.3. This system designed by HTML, PHP, JavaScript, and
CSS provides an interface with the user using both the servers. This interaction requires a network among the
computer from which the personnel will provide their requirements.
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Figure 14: Architecture Diagram of SRS
III. TOOLS AND TECHNOLOGY
The latest demanding tools and technologies such as HTML, PHP, CSS, JavaScript, MySQL database,
and Apache web server have been used to develop the SRS. More significantly, a phonetic algorithm namely
Metaphone algorithm has been used for searching in SRS to ignore the spelling error in the searching keywords.
3.1 Importance and Phonetic Algorithm
A phonetic algorithm is an algorithm for indexing of words by their pronunciation. The main advantage
of phonetic algorithm is to eliminate misspelling of words. When user search for a keyword than it may happen
that he/she will place misspell of his desired keyword. To solve this problem we use Metaphone algorithm in
this system which produce phonetic similarity of different alphabets or group of alphabets to avoid the loss of
information. Here, users will give search keywords and actual data are matched by its phonetic similarity.
3.2 Metaphone Algorithm
Metaphone is a phonetic algorithm, published by Lawrence Philips in 1990, for indexing words by their
English pronunciation. It fundamentally improves on the Soundex algorithm by using information about
variations and inconsistencies in English spelling and pronunciation to produce a more accurate encoding, which
does a better job of matching words and names which sound similar [5]. As with Soundex, similar sounding
words should share the same keys. Metaphone is available as a built-in operator in a number of systems,
including later versions of PHP.
Metaphone codes use the 16 consonant symbols 0BFHJKLMNPRSTWXY. The '0' represents "th" (as
an ASCII approximation of Θ), 'X' represents "sh" or "ch", and the others represent their usual English
pronunciations. The vowels AEIOU are also used, but only at the beginning of the code. This table summarizes
most of the rules in the original implementation:
1. Drop duplicate adjacent letters, except for C.
2. If the word begins with 'KN', 'GN', 'PN', 'AE', 'WR', drop the first letter.
3. Drop 'B' if after 'M' at the end of the word.
4. 'C' transforms to 'X' if followed by 'IA' or 'H' (unless in latter case, it is part of '-SCH-', in which case it
transforms to 'K'). 'C' transforms to 'S' if followed by 'I', 'E', or 'Y'. Otherwise, 'C' transforms to 'K'.
5. 'D' transforms to 'J' if followed by 'GE', 'GY', or 'GI'. Otherwise, 'D' transforms to 'T'.
6. Drop 'G' if followed by 'H' and 'H' is not at the end or before a vowel. Drop 'G' if followed by 'N' or 'NED'
and is at the end.
7. 'G' transforms to 'J' if before 'I', 'E', or 'Y', and it is not in 'GG'. Otherwise, 'G' transforms to 'K'.
8. Drop 'H' if after vowel and not before a vowel.
9. 'CK' transforms to 'K'.
10. 'PH' transforms to 'F'.
11. 'Q' transforms to 'K'.
12. 'S' transforms to 'X' if followed by 'H', 'IO', or 'IA'.
13. 'T' transforms to 'X' if followed by 'IA' or 'IO'. 'TH' transforms to '0'. Drop 'T' if followed by 'CH'.
14. 'V' transforms to 'F'.
15. 'WH' transforms to 'W' if at the beginning. Drop 'W' if not followed by a vowel.
16. 'X' transforms to 'S' if at the beginning. Otherwise, 'X' transforms to 'KS'.
17. Drop 'Y' if not followed by a vowel.
18. 'Z' transforms to 'S'.
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19. Drop all vowels unless it is the beginning [5].
3.3 Works on Metaphone Algorithm
Metaphone Calculator [6].
Doing a fuzzy match in MySQL. Soundex and Metaphone algorithm [7].
Naushad UzZaman and Mumit Khan, “A Bangla Phonetic Encoding for Better Spelling Suggestions” [8].
Chakkrit Snae and Michael Brückner, “Novel Phonetic Name Matching Algorithm with a Statistical
Ontology for Analyzing Names Given in Accordance with Thai Astrology” [9].
Chakkrit Snae, “A Comparison and Analysis of Name Matching Algorithms” [10].
3.4 Working Steps of Metaphone Algorithm
The main two steps of working of the Metaphone Algorithm are illustrated below:
Step-1:
Take the input for inserting in the database
Select the words which may be searched
Make Metaphone code for the searchable words
Store Metaphone code in database
Step-2:
Create Metaphone code for each of the words in the search key
Retrieve data from database based on the code of the words
Calculate percentage of matching with the keywords and the actual data in the database
Display retrieved data in the ascending order of matched with the actual data
Divide the whole data into several pages if it is required
IV. SNAPSHOTS OF SRS
The home page in the SRS looks like the following:
Figure 15: Hope Page of SRS
The following figure shows the form to search for keywords by the users:
Figure 16: Searching Form
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Suppose that the user has searched for “Cricket” in this SRS which is connected to a search engine such as
Google. In searching, he/she can mistype the input keywords but the Metaphone algorithm used in SRS
performs the related results as shown below:
Figure 17: Result Page
After successful login to the SRS, admin can insert keyword by using this page:
Figure 18: Keyword Inserting Form
Then the admin can update keywords by the following page:
Figure 19: Keyword Updating Form
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And the admin can see the missing keywords to add them in the SRS by this page:
Figure 18: Monitoring Missing Keyword and Then Inserting Them
V. CONCLUSION
Analyzing the above descriptions of the Search Reporter System along with a search engine like
Google it can be concluded that the SRS is highly effective, efficient and user-friendly for the fulfillment of the
user’s requirement. Users can be highly benefited using the SRS. With the rapid enhancement of modern
technology people want beneficial information within a short moment of time. Web search engine can provide
that required information but it shows many more necessary and less necessary URLs about the searching
keyword elapsing a more time. Hence the SRS aims to reduce the less necessary and totally unnecessary URLs
from a list of URLs resulted from a search engine after analyzing some factors for the same reducing the
searching time. To get full benefits of modern web technology using the SRS, it can be integrated with other
search engines.
Future Plan:
In the world nothing is free from error. So it is very common that the SRS may contain error. The SRS fully
dependent on any search engine like Google. In future the following features will be integrated with the SRS:
Searching based on search analyzer
Mailing facility
Chatting facility REFERENCES
[1] http://en.wikipedia.org/wiki/Web_search_engine
[2] http://en.wikipedia.org/wiki/Web_crawler.
[3] http://www.brightplanet.com/2012/11/deep-web-search-engines-vs-web-harvest-engines-finding-intel-in-
a-growing-internet/
[4] http://en.wikipedia.org/wiki/Information_flow_diagram
[5] http://en.wikipedia.org/wiki/Metaphone
[6] http://www.vbforums.com/showthread.php?655230-Metaphone-Calculator
[7] http://theunderweb.com/doing-a-fuzzy-match-of-names-in-mysql-soundex-and-metaphone-
algorithms.html.
[8] Naushad UzZaman and Mumit Khan, “A Bangla Phonetic Encoding for Better Spelling Suggestions”,
Proc. of 7th International Conference on Computer and Information Technology (ICCIT 2004), pp. 76-
80.
[9] Chakkrit Snae and Michael Brückner, “Novel Phonetic Name Matching Algorithm with a Statistical
Ontology for Analyzing Names Given in Accordance with Thai Astrology”, Issues in Informing Science
and Information Technology, 2009, V.6, pp. 497-515.
[10] Chakkrit Snae, “A Comparison and Analysis of Name Matching Algorithms”, World Academy of
Science, Engineering and Technology, 2007, Issue 1.
Md. Palash Uddin (palash_cse@hstu.ac.bd) received his B.Sc. degree in Computer Science
and Engineering from Hajee Mohammad Danesh Science and Technology University,
Dinajpur, Bangladesh in 2013. His main working interest is based on artificial intelligence,
bioinformatics, algorithm analysis, database structure analysis, software engineering, theory
of computation etc. Currently he is working as a lecturer in Dept. of Computer Science and
Information Technology in Hajee Mohammad Danesh Science and Technology University,
Dinajpur, Bangladesh. Previously, he was a lecturer in department of Computer Science and
Engineering at Central Women’s University, Dhaka, Bangladesh. He has research
publications in various fields of Computer Science and Engineering.
American Journal of Engineering Research (AJER)
2013
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Mst. Deloara Khushi received his B.Sc. degree in Computer Science and Engineering from
Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh in
2013. Her main working interest is based on communication theory and computer
algorithms. Currently she is working as a lecturer in Dept. of Computer Science and
Engineering in Bangladesh University of Business and Technology, Dhaka, Bangladesh
Fahmida Akter received his B.Sc. degree in Computer Science and Engineering from Hajee
Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh in 2013.
Her main working interest is based on bioinformatics and data mining. Currently she is
working as a lecturer in Dept. of Computer Science and Engineering in East Delta
University, Chittagong, Bangladesh
Md. Fazle Rabbi received his B.Sc. degree in Computer Science and Engineering from Hajee
Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh in 2008. His
main working interest is based on bioinformatics, data structures and algorithm etc.
Currently he is working as an assistant professor in Dept. of Computer Science &
Information Technology in Hajee Mohammad Danesh Science and Technology University,
Dinajpur, Bangladesh
