G-CARE: A Framework for Performance Benchmarking of Cardinality Estimation Techniques for Subgraph Matching

Park, Yeonsu; Ko, Seongyun; Bhowmick, Sourav S.; Kim, Kyoungmin; Hong, Kijae; Han, Wook-Shin

doi:10.1145/3318464.3389702

Cited by 40 publications

(22 citation statements)

References 38 publications

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“…The last item gives more insight into 𝑽 and, by extension, into 𝑪. Recall that 𝑉 𝒎 , defined by (16), is the largest possible sum of values of a consistent…”

Section: Resultsmentioning

confidence: 99%

“…For the next three items, 4, 5, and 6, we need to take a deeper look at the structure of the tensors 𝑽, 𝑪, through the lens of the dual linear program associated with the primal linear program that defines 𝑽 in Equation (16). To keep the notations readable and intuitive, we will restrict in the rest of this section the number of dimensions to 𝑑 = 3.…”

Section: Proof Of Theorem 32mentioning

confidence: 99%

“…In this paper, we will call it a cardinality bound. As expected, a cardinality bound prevents query optimizers from choosing disastrous plans for the most expensive queries [3], however, their relative error is often much larger than that of other methods [4,16]. While the appeal of a guaranteed upper bound is undeniable, in practice overly pessimistic bounds are unacceptable.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Degree Sequence Bound For Join Cardinality Estimation

Deeds¹,

Suciu²,

Balazinska³

et al. 2022

Preprint

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Recent work has demonstrated the catastrophic effects of poor cardinality estimates on query processing time. In particular, underestimating query cardinality can result in overly optimistic query plans which take orders of magnitude longer to complete than one generated with the true cardinality. Cardinality bounding avoids this pitfall by computing a strict upper bound on the query's output size using statistics about the database such as table sizes and degrees, i.e. value frequencies. In this paper, we extend this line of work by proving a novel bound called the Degree Sequence Bound which takes into account the full degree sequences and the max tuple multiplicity. This bound improves upon previous work incorporating degree constraints which focused on the maximum degree rather than the degree sequence. Further, we describe how to practically compute this bound using a learned approximation of the true degree sequences.

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“…The last item gives more insight into 𝑽 and, by extension, into 𝑪. Recall that 𝑉 𝒎 , defined by (16), is the largest possible sum of values of a consistent…”

Section: Resultsmentioning

confidence: 99%

Section: Proof Of Theorem 32mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Degree Sequence Bound For Join Cardinality Estimation

Deeds¹,

Suciu²,

Balazinska³

et al. 2022

Preprint

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show abstract

“…CECI [24] utilized the BFS-based filtering and reverse-BFS-based refinement to prune the unpromising candidates and then replaced the edge verification with set intersection to speed up the candidate verification. In addition, similar algorithms include the literature [25][26][27][28].…”

Section: G (A)mentioning

confidence: 99%

“…Real datasets include WordNet, DBLP, YouTube, DBpedia, and LiveJournal. WordNet, DBLP, and YouTube were used in [15,36], DBpedia was used in [36], and LiveJournal was used in [27]. We obtained the DBLP, YouTube, and LiveJournal datasets from Stanford Large Network Dataset Collection (http://snap.stanford.edu/).…”

Section: Datasetsmentioning

confidence: 99%

Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs

Shan

Jia

et al. 2021

Complexity

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Interesting subgraph query aims to find subgraphs that are isomorphic to the given query graph from a data graph and rank the subgraphs according to their interestingness scores. However, the existing subgraph query approaches are inefficient when dealing with large-scale labeled data graph. This is caused by the following problems: (i) the existing work mainly focuses on unweighted query graphs, while ignoring the impact of query constraints on query results. (ii) Excessive number of subgraph candidates or complex joins between nodes in the subgraph candidates reduce the query efficiency. To solve these problems, this paper proposes an intelligent solution. Firstly, an Isotype Structure Graph Compression (ISGC) strategy is proposed to compress similar nodes in a graph to reduce the size of the graph and avoid unnecessary matching. Then, an auxiliary data structure Supergraph Topology Feature Index (STFIndex) is designed to replace the storage of the original data graph and improve the efficiency of an online query. After that, a partition method based on Edge Label Step Value (ELSV) is proposed to partition the index logically. In addition, a novel Top-K interest subgraph query approach is proposed, which consists of the multidimensional filtering (MDF) strategy, upper bound value (UBV) (Size-c) matching, and the optimizational join (QJ) method to filter out as many false subgraph candidates as possible to achieve fast joins. We conduct experiments on real and synthetic datasets. Experimental results show that the average performance of our approach is 1.35 higher than that of the state-of-the-art approaches when the query graph is unweighted, and the average performance of our approach is 2.88 higher than that of the state-of-the-art approaches when the query graph is weighted.

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Learning with Small Data: Subgraph Counting Queries

Zhao

et al. 2023

Lecture Notes in Computer Science

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G-CARE: A Framework for Performance Benchmarking of Cardinality Estimation Techniques for Subgraph Matching

Cited by 40 publications

References 38 publications

Degree Sequence Bound For Join Cardinality Estimation

Degree Sequence Bound For Join Cardinality Estimation

Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs

Learning with Small Data: Subgraph Counting Queries

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