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Tracking join and self-join sizes in limited storage
, 2002
"... This paper presents algorithms for tracking (approximate) join and self-join sizes in limited storage, in the presence of insertions and deletions to the data set(s). Such algorithms detect changes in join and self-join sizes without an expensive recomputation from the base data, and without the lar ..."
Abstract
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Cited by 123 (0 self)
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This paper presents algorithms for tracking (approximate) join and self-join sizes in limited storage, in the presence of insertions and deletions to the data set(s). Such algorithms detect changes in join and self-join sizes without an expensive recomputation from the base data, and without
Bifocal Sampling for Skew-Resistant Join Size Estimation
- In Proceedings of the 1996 ACM SIGMOD Intl. Conf. on Management of Data
, 1996
"... This paper introduces bifocal sampling, a new technique for estimating the size of an equi-join of two relations. Bifocal sampling classifies tuples in each relation into two groups, sparse and dense, based on the number of tuples with the same join value, Distinct estimation procedures are employed ..."
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Cited by 40 (6 self)
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This paper introduces bifocal sampling, a new technique for estimating the size of an equi-join of two relations. Bifocal sampling classifies tuples in each relation into two groups, sparse and dense, based on the number of tuples with the same join value, Distinct estimation procedures
Estimating xml structural join size quickly and economically
- Proc. 22nd Intl. Conf. on Data Engineering
, 2006
"... XML structural joins, which evaluate the contain-ment (ancestor-descendant) relationships between XML el-ements, are important operations of XML query process-ing. Estimating structural join size accurately and quickly is thus crucial to the success of XML query plan selection and the query optimiza ..."
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Cited by 1 (1 self)
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XML structural joins, which evaluate the contain-ment (ancestor-descendant) relationships between XML el-ements, are important operations of XML query process-ing. Estimating structural join size accurately and quickly is thus crucial to the success of XML query plan selection and the query
Iterated DFT Based Techniques for Join Size Estimation
- In Proc. 1988 ACM Intl. Conf. on Information and Knowledge Management
"... Novel techniques based on the Discrete Fourier Transform are proposed to estimate the size of relations resulting from join operations. For the special case of self join the proposed algorithm gives the exact join size using logarithmic space. A generalization to compute the join of arbitrary relati ..."
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Cited by 1 (0 self)
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Novel techniques based on the Discrete Fourier Transform are proposed to estimate the size of relations resulting from join operations. For the special case of self join the proposed algorithm gives the exact join size using logarithmic space. A generalization to compute the join of arbitrary
Containment join size estimation: Models and methods
- In Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data
, 2003
"... Recent years witnessed an increasing interest in researches in XML, partly due to the fact that XML has now become the de facto standard for data interchange over the inter-net. A large amount of work has been reported on XML storage models and query processing techniques. However, few works have ad ..."
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Cited by 18 (1 self)
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addressed issues of XML query optimiza-tion. In this paper, we report our study on one of the chal-lenges in XML query optimization: containment join size estimation. Containment join is well accepted as an impor-tant operation in XML query processing. Estimating the size of its results is no doubt
Similarity Join Size Estimation using Locality Sensitive Hashing ∗
"... Similarity joins are important operations with a broad range of applications. In this paper, we study the problem of vector similarity join size estimation (VSJ). It is a generalization of the previously studied set similarity join size estimation (SSJ) problem and can handle more interesting cases ..."
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Cited by 5 (0 self)
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Similarity joins are important operations with a broad range of applications. In this paper, we study the problem of vector similarity join size estimation (VSJ). It is a generalization of the previously studied set similarity join size estimation (SSJ) problem and can handle more interesting cases
Join Size Estimation on Boolean Tensors of RDF Data
"... The Resource Description Framework (rdf) represents in-formation as subject–predicate–object triples. These triples are commonly interpreted as a directed labelled graph. We instead interpret the data as a 3-way Boolean tensor. Stan-dard sparql queries then can be expressed using elementary Boolean ..."
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algebra operations. We show how this representa-tion helps to estimate the size of joins. Such estimates are valuable for query handling and our approach might yield more efficient implementations of sparql query processors.
Iterated DFT Based Techniques for Join Size Estimation
"... The aim of query optimizers in relational database systems is to select the most ecient way among all the possible ways of executing a query. In practice this process requires approximating the cost of possible execution sequences and selecting the cheapest one in terms of a cost metric, which is us ..."
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is usually the resulting size of the query operation. The accuracy of this approximation is crucial to the performance of the database
Scalable Application Layer Multicast
, 2002
"... We describe a new scalable application-layer multicast protocol, specifically designed for low-bandwidth, data streaming applications with large receiver sets. Our scheme is based upon a hierarchical clustering of the application-layer multicast peers and can support a number of different data deliv ..."
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Cited by 731 (21 self)
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sites. In our experiments, averagegroup members established and maintained low-latency paths and incurred a maximum packet loss rate of less than 1 % as members randomly joined and left the multicast group. The average control overhead during our experiments was less than 1 Kbps for groups of size 100.
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