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Best Effort Top-K Query Processing Under Budgetary Constraints

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45382

Shmueli-Scheuer,  Michal
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45380

Schenkel,  Ralf
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Shmueli-Scheuer, M., Li, C., Mass, Y., Roitman, H., Schenkel, R., & Weikum, G. (2009). Best Effort Top-K Query Processing Under Budgetary Constraints. In Y. Ioannidis, D. Lee, & R. Ng (Eds.), Proceedings of the 25th IEEE International Conference on Data Engineering (pp. 928-939). Los Alamitos, CA: IEEE.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-18F8-0
Abstract
We consider a novel problem of top-k query processing under budget constraints. We provide both a framework and a set of algorithms to address this problem. Existing algorithms for top-k processing are budget-oblivious, i.e., they do not take budget constraints into account when making scheduling decisions, but focus on the performance to compute the final top-k results. Under budget constraints, these algorithms therefore often return results that are a lot worse than the results that can be achieved with a clever, budget-aware scheduling algorithm. This paper introduces novel algorithms for budget-aware top-k processing that produce results that are significantly better than those of state-of-the-art budget-obvlivious solutions.