1. Traditional Approach to Predict Hard Queries
using Keyword Analyzer over Data bases
Presented By
E. Samuel Raju
M.Tech (II Year)
Roll No:139P1D5812
Under The Esteemed Guidance of
Mr. P. Srinivas., M. Tech
Assistant Professor.

2. ABSTRACT
 Querying is a common task to search any information with respect to
huge database. Finding correct results from the given query is a
challenging task.
 To predict such hard queries we propose a novel framework which
uses association analysis to find the top k results from the search
keyword.
 In this project we propose an algorithm to find the top k searched
keyword items to the user data with combination of keywords. This
probabilistic method will predict the results quickly.

3. EXISTING SYSTEM
 There have been collaborative efforts to provide standard
benchmarks and evaluation platforms for keyword search methods
over databases.
• In this approach they use Approximation algorithms for ranking the
results which is very cumbersome process
 The results indicate that even with structured data, finding the
desired answers to keyword queries is still a hard task.

4. DISADVANTAGES OF EXISTING
SYSTEM
 Suffer from low ranking quality.
 performing very poorly on a subset of queries.

5. PROPOSED SYSTEM
 In this project we propose an algorithm to find the top k searched
keyword items to the user data with combination of keywords. This
probabilistic method will predict the results quickly.
 Here we use a traditional component called Keyword Analyzer
 It uses fp tree to generate the frequent patterns and find set of rules
for re- ranking
 The SR algorithm is also applicable along with this for ranking the
top k results

6. ADVANTAGES OF PROPOSED SYSTEM
 Easily mapped to both XML and relational data.
 Higher prediction accuracy and minimize the incurred time
overhead.

7. SYSTEM ARCHITECTURE

8. UML DIAGARAM
Use Case Diagram

9. HARDWARE REQUIREMENTS(Min)
 System : Pentium IV 2.4 GHz.
 Hard Disk : 40 GB.
 RAM : 512 MB.

10. SOFTWARE REQUIREMENTS
 Operating system : Windows XP/7.
 Coding Language : JAVA/J2EE
 IDE : Net beans 7.4
 Database : IMDB OVER INTERNET

11. MODULES:
 Data and Query Modeling
 Keyword Analyzer
 Corruption Module
 Ranking Module

12. Data and Query Modeling
 An entity could be stored in an XML file or a set of normalized
relational tables. In this model , we works on entity search and data-
centric XML retrieval, and has the advantage that it can be easily
mapped to both XML and relational data.

13. Keyword Analyzer
 Keyword analyzer is a software component which is used to find all
frequent keyword count and generate the corresponding tree and
rank the results .In this model it simply rank the keywords based on
the previous user hits. We can use structured robustness algorithm to
rank top k results from the generated list.

14. Corruption Module
 A corrupted version of DB can be seen as a random sample of
keyword results . These results are updated on corrupted database

15. Ranking Module
 Each ranking algorithm uses some statistics about query terms or
attributes values over the whole content of DB.
 Some examples of such statistics are the number of occurrences of a
query term in all attributes values of the DB or total number of
attribute values in each attribute and entity set.

16. CONCLUSION
In this project, we propose an efficient SR Algorithm without
finding approximations. We built a component called keyword
analyzer. Keyword analyzer works on combination of keywords to
build a model and generate the corrupted results of top K Ranked
queries efficiently. Using of keyword analyzer will generate the
frequent occurring top K Ranked queries over corrupted data bases.
This model is applicable to both structured data and normal text
documents.