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...
 We

propose methods that compute all top-k queries in
batch. Our first solution applies the block indexed
nested loops p...
Architecture
Existing System
 The

result can be computed by issuing an individual topk query for each user, TOPk f (i). This iterativ...
Drawback
 An

individual top-k query for each user

 More

expensive when a large number of queries have to
be evaluated...
Proposed System
In this paper, we study two batch processing
techniques for this problem. The first is a batch indexed
nes...
Advantages
 Batch

processing techniques.

 Computing

multiple top-k queries simultaneously.
Modules
 User

Registration

 Product
 Search

Registration

User preferences

 Product

Ranking and recommendation

...
DATA FLOW DIAGRAM
Admin

User Query

Product Registration

Top-K
Database

Product Ranking

Top-K Query
Processing

Best S...
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Efficient All Top-k Computation

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Efficient All Top-k Computation

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

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