Communications of the ACM
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Practical selectivity estimation through adaptive sampling
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
On estimating the size of projections
ICDT '90 Proceedings of the third international conference on database theory on Database theory
Semantic complexity of classes of relational queries and query independent data partitioning
PODS '91 Proceedings of the tenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Statistical estimators for aggregate relational algebra queries
ACM Transactions on Database Systems (TODS)
Asymptomatic conditional probabilities for first-order logic
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Sequential sampling procedures for query size estimation
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Horn clauses and database dependencies
Journal of the ACM (JACM)
Approximate Dependency Inference from Relations
ICDT '92 Proceedings of the 4th International Conference on Database Theory
Perspectives on database theory
ACM SIGACT News
An efficient and effective algorithm for density biased sampling
Proceedings of the eleventh international conference on Information and knowledge management
Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules
Data Mining and Knowledge Discovery
On Issues of Instance Selection
Data Mining and Knowledge Discovery
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
Data Mining and Knowledge Discovery
Discovering interesting inclusion dependencies: application to logical database tuning
Information Systems - Databases: Creation, management and utilization
Sampling Strategies for Mining in Data-Scarce Domains
Computing in Science and Engineering
Efficiently Determining the Starting Sample Size for Progressive Sampling
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Sequential Sampling Algorithms: Unified Analysis and Lower Bounds
SAGA '01 Proceedings of the International Symposium on Stochastic Algorithms: Foundations and Applications
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An Index for the Data Size to Extract Decomposable Structures in LAD
ISAAC '01 Proceedings of the 12th International Symposium on Algorithms and Computation
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
DS '99 Proceedings of the Second International Conference on Discovery Science
Consistent database sampling as a database prototyping approach
Journal of Software Maintenance: Research and Practice
A new two-phase sampling based algorithm for discovering association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Progressive rademacher sampling
Eighteenth national conference on Artificial intelligence
A selective sampling approach to active feature selection
Artificial Intelligence
Elastic Translation Invariant Matching of Trajectories
Machine Learning
ACM Computing Surveys (CSUR)
Indexed-based density biased sampling for clustering applications
Data & Knowledge Engineering
Optimization-based feature selection with adaptive instance sampling
Computers and Operations Research
A dip in the reservoir: maintaining sample synopses of evolving datasets
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
An approach to online optimization of heuristic coordination algorithms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Knowledge discovery query language (KDQL)
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
A divide-and-conquer recursive approach for scaling up instance selection algorithms
Data Mining and Knowledge Discovery
Estimating the confidence of conditional functional dependencies
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Journal of Artificial Intelligence Research
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A formal framework for database sampling
Information and Software Technology
Focusing solutions for data mining: analytical studies and experimental results in real-world domains
Frequent subgraph mining on a single large graph using sampling techniques
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
An efficient preprocessing stage for the relationship-based clustering framework
Intelligent Data Analysis
Discovering process models with genetic algorithms using sampling
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
A clustering-based data reduction for very large spatio-temporal datasets
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Distributed genetic process mining using sampling
PaCT'11 Proceedings of the 11th international conference on Parallel computing technologies
A new hybrid clustering method for reducing very large spatio-temporal dataset
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Incremental linear model trees on massive datasets: keep it simple, keep it fast
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Dengue surveillance based on a computational model of spatio-temporal locality of Twitter
Proceedings of the 3rd International Web Science Conference
Adaptive stratified reservoir sampling over heterogeneous data streams
Information Systems
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We consider the problem of approximately verifying the truth of sentences of tuple relational calculus in a given relation M by considering only a random sample of M. We define two different measures for the error of a universal sentence in a relation. For a set of n universal sentences each with at most k universal quantifiers, we give upper and lower bounds for the sample sizes required for having a high probability that all the sentences with error at least &egr; can be detected as false by considering the sample. The sample sizes are O((log n)/&egr;) or O((|M|1–1/k)log n/&egr;), depending on the error measure used. We also consider universal-existential sentences.