The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space
Abstrak
Graph data management is instrumental for several use cases such as recommendation, root cause analysis, financial fraud detection, and enterprise knowledge representation. Efficiently supporting these use cases yields a number of unique requirements, including the need for a concise query language and graph-aware query optimization techniques. The goal of the Linked Data Benchmark Council (LDBC) is to design a set of standard benchmarks that capture representative categories of graph data management problems, making the performance of systems comparable and facilitating competition among vendors. LDBC also conducts research on graph schemas and graph query languages. This paper introduces the LDBC organization and its work over the last decade.
Topik & Kata Kunci
Penulis (33)
Gábor Szárnyas
Brad Bebee
Altan Birler
Alin Deutsch
George Fletcher
Henry A. Gabb
Denise Gosnell
Alastair Green
Zhihui Guo
Keith W. Hare
Jan Hidders
Alexandru Iosup
Atanas Kiryakov
Tomas Kovatchev
Xinsheng Li
Leonid Libkin
Heng Lin
Xiaojian Luo
Arnau Prat-Pérez
David Püroja
Shipeng Qi
Oskar van Rest
Benjamin A. Steer
Dávid Szakállas
Bing Tong
Jack Waudby
Mingxi Wu
Bin Yang
Wenyuan Yu
Chen Zhang
Jason Zhang
Yan Zhou
Peter Boncz
Akses Cepat
- Tahun Terbit
- 2023
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓