Presentation

1. Efficient All Top-k Computation

2. Abstract
 Given
a set of objects P and a set of ranking functions F
over P, an interesting problem is to compute the top
ranked objects for all functions.
 Evaluation
of multiple top-k queries finds application in
systems, where there is a heavy workload of ranking
queries (e.g., online search engines and product
recommendation systems).

3.  We
propose methods that compute all top-k queries in
batch. Our first solution applies the block indexed
nested loops paradigm, while our second technique is a
view-based algorithm.
 We
propose appropriate optimization techniques for the
two approaches and demonstrate experimentally that the
second approach is consistently the best.

4. Architecture

5. Existing System
 The
result can be computed by issuing an individual topk query for each user, TOPk f (i). This iterative approach
becomes too expensive when a large number of queries
have to be evaluated over a large number of products.

6. Drawback
 An
individual top-k query for each user
 More
expensive when a large number of queries have to
be evaluated over a large number of products

7. Proposed System
In this paper, we study two batch processing
techniques for this problem. The first is a batch indexed
nested loops approach and the second is a view-based
threshold algorithm. We also propose several novel
optimization techniques for these methods. Besides
products recommendation, other tasks, such as product
promotion analysis and identifying the most influential
products, can benefit from an efficient approach for
computing multiple top-k queries simultaneously.

8. Advantages
 Batch
processing techniques.
 Computing
multiple top-k queries simultaneously.

9. Modules
 User
Registration
 Product
 Search
Registration
User preferences
 Product
Ranking and recommendation
 Multiple
top-k query processing

10. DATA FLOW DIAGRAM
Admin
User Query
Product Registration
Top-K
Database
Product Ranking
Top-K Query
Processing
Best Solution