- Titel
- Robust Cardinality Estimation for Subgraph Isomorphism Queries on Property Graphs
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-836515
- Konferenz
- Biomedical Data Management and Graph Online Querying: VLDB 2015 Workshops, Big-O(Q) and DMAH. Waikoloa, 31.08-04.09.2015
- Quellenangabe
- Biomedical Data Management and Graph Online Querying
; VLDB 2015 Workshops, Big-O(Q) and DMAH
Erscheinungsort: Cham
Verlag: Springer
Erscheinungsjahr: 2016
Titel Schriftenreihe: Lecture Notes in Computer Science
Bandnummer Schriftenreihe: 9579
Seiten: 184-198
ISBN: 978-3-319-41575-8 - Erstveröffentlichung
- 2016
- Abstract (EN)
- 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.
- Andere Ausgabe
- Link zum Artikel, der zuerst bei Springer Link erschienen ist
DOI: 10.1007/978-3-319-41576-5_14 - Freie Schlagwörter (DE)
- Funktionale Abhängigkeit, Gradverteilung, Eigenschaftsdiagramm, Abfrage-Optimierung, Kanten-Typ
- Freie Schlagwörter (EN)
- Functional Dependency, Degree Distribution, Property Graph, Query Optimization, Edge Type
- Klassifikation (DDC)
- 004
- Verlag
- Springer, Berlin [u. a.]
- Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-836515
- Veröffentlichungsdatum Qucosa
- 15.03.2023
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
