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Department of Computer Science and Engineering

(Affiliated to JNTUA, Anantapur and approved by AICTE, New Delhi)Sree Sainath nagar, A.Rangampet, Tirupathi-517102.

A project on

ORGANIZING USER SEARCH HISTORIES

M.VANI 09121A0565

C.MOHAN BABU 09121A0519

K.MANOJ KUMAR 09121A0543

B.PAVAN KUMAR 09121A0516

Submitted by - CS#15

GuideProf. D. Jatin Das, B.E., M.Sc.[Tech-CS],DEAN (FRESHMEN),Department of Computer Science,SVEC.

Head of the DepartmentDr. A. Senguttuvan M.E, Ph.D.,Head(CSE),Department of Computer Science,SVEC.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING VOL.24, NO.5, MAY 2012

ORGANIZING USER SEARCH HISTORIES

Heasoo Hwang, Hady W. Lauw, Lise Getoor, and Alexandros Ntoulas

Project Based On

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Introduction

• Users are increasingly using web search engines.

• People search task by dividing into co-dependent steps and issue multiple queries.

• Search engine needs keep track of all the multiple queries.

• We organize each user’s historical queries into groups in a dynamic and automated fashion.

.

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Problem

• Query grouping in an iterative fashion leads to undesirable effects.

• Time-based query grouping and text-based may alone work well in some cases.

• High computational cost.

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Objectives

• Query Grouping in a dynamic fashion.

• Query Grouping can be done on the queries

based on closely related and relevant queries

and query clicks.

• Relevance among query groups can be

measured using search logs such as query

reformulation and clicks 5

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Scope

• Our work can also extend and store in cloud computing.

• Calculating number of visitors clicked particular site.

• We can extend our work that might be useful for knowing user’s taste. Contd..

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Literature Survey

• Ref No. [1] – How Query flow Graph represented.

[ P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, and S. Vigna, “The Query Flow Graph: Model and Applications,” Proc. 17th ACM Conf. Information and Knowledge Management (CIKM), 2008.]

Query flow graph consist of G(V,E) where V is Queries and E represents edges.

Contd...

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• Ref No. [2] – How Session Time Outs In Query Logs happens.

[R. Jones and K.L. Klinkner, “Beyond the Session Timeout: Automatic Hierarchical Segmentation of Search Topics in Query Logs,” Proc. 17th ACM Conf. Information and Knowledge Management (CIKM), 2008.]

In session time out there exist threshold limit.

Literature Survey…

System Requirements

H/W System ConfigurationCONTEMPORARY PC

S/W System Configuration Operating System Windows XP / UNIX

Front End HTML, Java, JSP

Scripts JavaScript Database RDBMS Tools RSA(IBM)

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ALGORITHM & EXPLANATION

SelectBestQueryGroup:Input:1) The current singleton query group sc containing the current query qc and set of clicks clkc

2) A set of existing query groups S = {s1, . . . , sm}

3) A similarity threshold τsim, 0 ≤ τsim ≤ 1

Output: The query group s that best matches sc, or a new one if necessary.

( 0) s = Ф;( 1) τmax = τsim

( 2) for i = 1 to m( 3) if sim(sc, si) > τmax

( 4) s = si

( 5) τmax = sim(sc, si)

( 27) if s =Ф( 7) S = S Ư sc

( 8) s = sc

( 9) return s Contd...10

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UML DIAGRAMS

CLASS DIAGRAM

Contd...11

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Contd...

UML DIAGRAMS…

USECASE DIAGRAM

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Contd...

UML DIAGRAMS…

USECASE DIAGRAM

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UML DIAGRAMS…

SEQUENCE DIAGRAM

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SIMULATION MODEL

MODULE INTEGRATION

Query Grouping

• Time based query grouping.

- Time difference between 2 queries.

• Keyword based query grouping.

- keywords matching between 2 queries.

• Relevance based query grouping.

- Reformulations and similar clicks.Contd...

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SIMULATION MODEL

Contd...

Home page

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SIMULATION MODEL…

Contd...

Registration page

Registration Page

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SIMULATION MODEL…

Contd...

User login

User Login

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SIMULATION MODEL…

Database login

Contd...

Database Login

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SIMULATION MODEL…Database Home Page

Contd...20

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Travelling Database Structure

SIMULATION MODEL…

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SIMULATION MODEL…Registration Database Structure

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SIMULATION MODEL…

Public History Database Structure

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SIMULATION MODEL…User History Database Structure

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Result & PerformanceHome page-Input Query

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Result & Performance…Home page-Query Results

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Result & Performance…User Home page

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Result & Performance…User Update Details

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Result & Performance…User History

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Result & Performance…

Database Page – User Details

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Result & Performance…

Database Page – Add Travelling Details

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Result & Performance…

Database Page – View History Graph

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Result & Performance…Travelling Database

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Result & Performance…

Public History Database

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Result & Performance…User History Database

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Result & Performance…User Signup Database

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Conclusion

• Query grouping in dynamic fashion overcomes the query grouping in an iterative fashion.

• Low computational cost.

• Query fusion graph overcomes time-based and keyword similarity based approach.

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THANK YOU

Queries?

THANK YOU

Queries?

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