One Size Does Not Fit All: Towards User- and Query-Dependent Ranking For Web Databases
See the Output of the project here
See the Output of the project here
Abstract
With the emergence of the deep Web, searching Web databases in domains such as vehicles, real estate, etc. has become a routine task. One of the problems in this context is ranking the results of a user query. Earlier approaches for addressing this problem have used frequencies of database values, query logs, and user profiles. A common thread in most of these approaches is that ranking is done in a user- and/or query-independent manner. This paper proposes a novel query- and user-dependent approach for ranking query results in Web databases. We present a ranking model, based on two complementary notions of user and query similarity, to derive a ranking function for a given user query. This function is acquired from a sparse workload comprising of several such ranking functions derived for various user-query pairs. The model is based on the intuition that similar users display comparable ranking preferences over the results of similar queries. We define these similarities formally in alternative ways and discuss their effectiveness analytically and experimentally over two distinct Web databases.
EXISTING SYSTEM:
Where a large set of queries given by varied classes of users is involved, the corresponding results should be ranked in a user- and query-dependent manner. The current sorting-based mechanisms used by web databases do not perform such ranking.
While some extensions to SQL allow manual specification of attribute weights, this approach is cumbersome for most web users. Automated ranking of database results has been studied in the context of relational databases, and although a number of techniques perform query-dependent ranking, they do not differentiate between users and hence, provide a single ranking order for a given query across all users. In contrast, techniques for building extensive user profiles as well as requiring users to order data tuples.
DISADVANTAGES OF EXISTING SYSTEM:
- The current sorting-based mechanisms used by web databases do not perform ranking.
- Existing system databases are typically searched by formulating query conditions on their schema attributes. When the number of results returned is large, it is time-consuming to browse
Proposed system:
1) We propose a user- and query-dependent approach for ranking query results of web databases.
2) We develop a ranking model, based on two complementary measures of query similarity and user similarity, to derive functions from a workload containing ranking functions for several user-query pairs.
3) We present experimental results over two web databases supported by google base to validate our approach in terms of efficiency as well as quality for real-world use.
4) We present a discussion on the approaches for acquiring/ generating a workload, and propose a learning method for the same with experimental results.
Advantages of Proposed system:
In order to make our approach practically useful, a minimal workload is important. One way to acquire such a workload is to adapt relevance feedback techniques used in document retrieval systems.
The proposed system technique can be incorporated in the real world applications.
ARCHITECTURE:
Main modules:-
ü Admin login
ü Query-similarity
ü User-similarity
ü Ranking process
Admin login:
In this module admin maintained various products of bike details with several databases. the databases have bike cost, color, details, and performance of bike details like gear, engine, etc., and also has enhancement details like alloys, electric start, etc.,
Query-similarity:
When customer login and search the bike details with specific price. Then bike details to be appeared with the customer to desire/wish. Details are displayed from different types of databases, using join query. Then, he gives feedback to that product.
User - Similarity:
If he, expected more details for various product he go to search via user-similarity. It shows more details. Then he takes decision and once again search he wish. Then give another feedback to that product.
Ranking Process:
If Customer, gives the feedback to all products. Then, admin counts about the passion by customer. Then, he ranks the overall products.
Hardware Required:
System : Pentium IV 2.4 GHz
Hard Disk : 40 GB
Floppy Drive : 1.44 MB
Monitor : 15 VGA color
Mouse : Logitech.
Keyboard : 110 keys enhanced
RAM : 256 MB
Software Required:
O/S : Windows XP.
Language : Asp.Net, c#.
Data Base : Sql Server 2005.
REFERENCE:
Aditya Telang, Chengkai Li, Sharma Chakravarthy, “One Size Does Not Fit All: Towards User- and Query-Dependent Ranking For Web Databases”, IEEE Transactions on Knowledge and Data Engineering, 2011.
