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.
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
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
propose appropriate optimization techniques for the
two approaches and demonstrate experimentally that the
second approach is consistently the best.
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.
individual top-k query for each user
expensive when a large number of queries have to
be evaluated over a large number of products
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.