Least expected cost query optimization: an exercise in utility
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Query strategies for priced information (extended abstract)
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Strengthening integrality gaps for capacitated network design and covering problems
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
On computing functions with uncertainty
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Least expected cost query optimization: what can we expect?
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Allocating Bandwidth for Bursty Connections
SIAM Journal on Computing
Computing the Median with Uncertainty
SIAM Journal on Computing
Stochastic Load Balancing and Related Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
A measurement-based analysis of multihoming
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Tight Approximation Results for General Covering Integer Programs
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Approximate replication
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Boosted sampling: approximation algorithms for stochastic optimization
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
An Edge in Time Saves Nine: LP Rounding Approximation Algorithms for Stochastic Network Design
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Stochastic Optimization is (Almost) as easy as Deterministic Optimization
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Towards a robust query optimizer: a principled and practical approach
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Approximation algorithms for budgeted learning problems
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Model-driven optimization using adaptive probes
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
The ratio index for budgeted learning, with applications
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Approximation algorithms for restless bandit problems
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Large-scale uncertainty management systems: learning and exploiting your data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Multi-armed Bandits with Metric Switching Costs
ICALP '09 Proceedings of the 36th Internatilonal Collogquium on Automata, Languages and Programming: Part II
How to probe for an extreme value
ACM Transactions on Algorithms (TALG)
Approximation algorithms for restless bandit problems
Journal of the ACM (JACM)
New algorithms for budgeted learning
Machine Learning
Fast greedy algorithms in mapreduce and streaming
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
Hi-index | 0.00 |
In several database applications, parameters like selectivities and load are known only with some associated uncertainty, which is specified, or modeled, as a distribution over values. The performance of query optimizers and monitoring schemes can be improved by spending resources like time or bandwidth in observing or resolving these parameters, so that better query plans can be generated. In a resource-constrained situation, deciding which parameters to observe in order to best optimize the expected quality of the plan generated (or in general, optimize the expected value of a certain objective function) itself becomes an interesting optimization problem.We present a framework for studying such problems, and present several scenarios arising in anomaly detection in complex systems, monitoring extreme values in sensor networks, load shedding in data stream systems, and estimating rates in wireless channels and minimum latency routes in networks, which can be modeled in this framework with the appropriate objective functions.Even for several simple objective functions, we show the problems are Np-Hard. We present greedy algorithms with good performance bounds. The proof of the performance bounds are via novel sub-modularity arguments.