Name
Abstract | ||
|---|---|---|
With an increasing popularity of graph data and graph processing systems, the need of efficient graph processing and graph query optimization becomes more important. Subgraph isomorphism queries, one of the fundamental graph query types, rely on an accurate cardinality estimation of a single edge of a pattern for efficient query processing. State of the art approaches do not consider two important aspects for cardinality estimation of graph queries on property graphs: the existence of nodes with a high outdegree and functional dependencies between attributes. In this paper we focus on these two challenges and integrate the detection of high-outdegree nodes and functional dependency analysis into the cardinality estimation. We evaluate our approach on two real data sets and compare it against a state-of-the-art query optimizer for property graphs as implemented in NEO4J. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-41576-5_14 | BIOMEDICAL DATA MANAGEMENT AND GRAPH ONLINE QUERYING |
Field | DocType | Volume |
Query optimization,Data mining,Graph isomorphism,Maximum common subgraph isomorphism problem,Computer science,Cardinality,Induced subgraph isomorphism problem,Theoretical computer science,Functional dependency,Degree distribution,Subgraph isomorphism problem | Conference | 9579 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcus Paradies | 1 | 82 | 10.36 |
| Elena Vasilyeva | 2 | 20 | 2.57 |
| Adrian Mocan | 3 | 885 | 67.82 |
| Wolfgang Lehner | 4 | 2243 | 294.69 |