Efficient All Top-k Computation
Upcoming SlideShare
Loading in...5
×
 

Efficient All Top-k Computation

on

  • 123 views

Presentation

Presentation

Statistics

Views

Total Views
123
Views on SlideShare
123
Embed Views
0

Actions

Likes
0
Downloads
1
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Efficient All Top-k Computation Efficient All Top-k Computation Presentation Transcript

    • Efficient All Top-k Computation
    • 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).
    •  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.
    • Architecture
    • 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.
    • 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
    • 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.
    • Advantages  Batch processing techniques.  Computing multiple top-k queries simultaneously.
    • Modules  User Registration  Product  Search Registration User preferences  Product Ranking and recommendation  Multiple top-k query processing
    • DATA FLOW DIAGRAM Admin User Query Product Registration Top-K Database Product Ranking Top-K Query Processing Best Solution