Data caching tradeoffs in client-server DBMS architectures
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Combining fuzzy information from multiple systems (extended abstract)
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Fuzzy queries in multimedia database systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Online computation and competitive analysis
Online computation and competitive analysis
Computing the median with uncertainty
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Adaptive precision setting for cached approximate values
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Safeguarding and Charging for Information on the Internet
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Evaluating Top-k Selection Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Offering a Precision-Performance Tradeoff for Aggregation Queries over Replicated Data
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Computing Shortest Paths with Uncertainty
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Managing uncertainty in sensor database
ACM SIGMOD Record
Approximate Selection Queries over Imprecise Data
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Compressing historical information in sensor networks
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Snapshot Queries: Towards Data-Centric Sensor Networks
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
A new strategy for querying priced information
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Cost-efficient processing of MIN/MAX queries over distributed sensors with uncertainty
Proceedings of the 2005 ACM symposium on Applied computing
Adaptive stream filters for entity-based queries with non-value tolerance
VLDB '05 Proceedings of the 31st international conference on Very large data bases
On the competitive ratio of evaluating priced functions
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Asking the right questions: model-driven optimization using probes
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Computing shortest paths with uncertainty
Journal of Algorithms
Range search on multidimensional uncertain data
ACM Transactions on Database Systems (TODS)
Model-driven optimization using adaptive probes
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Dissemination of compressed historical information in sensor networks
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient update strategies for geometric computing with uncertainty
CIAC'03 Proceedings of the 5th Italian conference on Algorithms and complexity
How to probe for an extreme value
ACM Transactions on Algorithms (TALG)
On the competitive ratio of evaluating priced functions
Journal of the ACM (JACM)
Adaptive Uncertainty Resolution in Bayesian Combinatorial Optimization Problems
ACM Transactions on Algorithms (TALG)
An optimal algorithm for querying priced information: monotone boolean functions and game trees
ESA'05 Proceedings of the 13th annual European conference on Algorithms
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
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We study the problem of computing a function f(x1,…, xn) given that the actual values of the variables xi's are known only with some uncertainty. For each variable xi, an interval Ii is known such that the value of xi is guaranteed to fall within this interval. Any such interval can be probed to obtain the actual value of the underlying variable; however, there is a cost associated with each such probe. The goal is to adaptively identify a minimum cost sequence of probes such that regardless of the actual values taken by the unprobed xi's, the value of the function f can be computed to within a specified precision.We design online algorithms for this problem when f is either the selection function or an aggregation function such as sum or average. We consider three natural models of precision and give algorithms for each model. We analyze our algorithms in the framework of competitive analysis and show that our algorithms are asymptotically optimal. Finally, we also study online algorithms for functions that are obtained by composing together selection and aggregation functions.