Clique partitions, graph compression and speeding-up algorithms
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Efficient sampling strategies for relational database operations
ICDT Selected papers of the 4th international conference on Database theory
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Bifocal sampling for skew-resistant join size estimation
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
The space complexity of approximating the frequency moments
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Wavelet-based histograms for selectivity estimation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Self-tuning histograms: building histograms without looking at data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Join synopses for approximate query answering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Independence is good: dependency-based histogram synopses for high-dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Selectivity estimation using probabilistic models
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Processing complex aggregate queries over data streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Histogram-Based Approximation of Set-Valued Query-Answers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Approximate Query Processing Using Wavelets
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
An improved data stream summary: the count-min sketch and its applications
Journal of Algorithms
XSEED: Accurate and Fast Cardinality Estimation for XPath Queries
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
XCluster Synopses for Structured XML Content
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Graph-based synopses for relational selectivity estimation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Parallel computing for data reduction
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
A parallel algorithm to compute data synopsis
WSEAS Transactions on Information Science and Applications
Probabilistic model for accuracy estimation in approximate monodimensional analyses
WSEAS Transactions on Computers
Accuracy estimation in approximate query processing
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Foundations and Trends in Databases
Metadata for approximate query answering systems
Advances in Software Engineering
CS2: a new database synopsis for query estimation
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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This article introduces the Tuple Graph (TuG) synopses, a new class of data summaries that enable accurate approximate answers for complex relational queries. The proposed summarization framework adopts a “semi-structured” view of the relational database, modeling a relational data set as a graph of tuples and join queries as graph traversals, respectively. The key idea is to approximate the structure of the induced data graph in a concise synopsis, and to approximate the answer to a query by performing the corresponding traversal over the summarized graph. We detail the (TuG) synopsis model that is based on this novel approach, and we describe an efficient and scalable construction algorithm for building accurate (TuG) within a specific storage budget. We validate the performance of (TuG) with an extensive experimental study on real-life and synthetic datasets. Our results verify the effectiveness of (TuG) in generating accurate approximate answers for complex join queries, and demonstrate their benefits over existing summarization techniques.