ACM Computing Surveys (CSUR)
A unified approach to approximating resource allocation and scheduling
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Online query processing: a tutorial
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Average-Case Competitive Analyses for Ski-Rental Problems
ISAAC '02 Proceedings of the 13th International Symposium on Algorithms and Computation
Optimal Reward-Based Scheduling of Periodic Real-Time Tasks
RTSS '99 Proceedings of the 20th IEEE Real-Time Systems Symposium
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Databases with uncertainty and lineage
The VLDB Journal — The International Journal on Very Large Data Bases
MCDB: a monte carlo approach to managing uncertain data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Monte-Carlo algorithms for enumeration and reliability problems
SFCS '83 Proceedings of the 24th Annual Symposium on Foundations of Computer Science
Beyond competitive analysis [on-line algorithms]
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Handling Uncertain Data in Array Database Systems
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
PODS: a new model and processing algorithms for uncertain data streams
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Scheduling Periodic Real-Time Tasks with Heterogeneous Reward Requirements
RTSS '11 Proceedings of the 2011 IEEE 32nd Real-Time Systems Symposium
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Answering real-time queries, especially over probabilistic data, is becoming increasingly important for service providers. We study anytime query processing algorithms, and extend the traditional query execution plan with a timing component. Our focus is how to determine this timing component, given the queries' deadline constraints. We consider the common multicore processors. Specifically, we propose two query optimization modes: offline periodic optimization and online optimization. We devise efficient algorithms for both offline and online cases followed by a competitive analysis to show the power of our online optimization. Finally, we perform a systematic experimental evaluation using real-world datasets to verify our approaches.