Pith

open record

sign in

arxiv: 2306.15975 · v2 · pith:AGGVX46H · submitted 2023-06-28 · cs.DB · cs.PF

The LDBC Financial Benchmark

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 reserved pith:AGGVX46Hrecord.jsonopen to challenge →

classification cs.DB cs.PF
keywords benchmarkldbcdatafinancialfinbenchqueriesworkloaddetailed
0
0 comments X
read the original abstract

The Linked Data Benchmark Council's Financial Benchmark (LDBC FinBench) is a new effort that defines a graph database benchmark targeting financial scenarios such as anti-fraud and risk control. The benchmark has one workload, the Transaction Workload, currently. It captures OLTP scenario with complex, simple read queries and write queries that continuously insert or delete data in the graph. Compared to the LDBC SNB, the LDBC FinBench differs in application scenarios, data patterns, and query patterns. This document contains a detailed explanation of the data used in the LDBC FinBench, the definition of transaction workload, a detailed description for all queries, and instructions on how to use the benchmark suite.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. PIPE-Cypher: Automatic Enterprise Benchmark Generation for Text-to-Cypher Systems

    cs.LG 2026-06 unverdicted novelty 6.0

    PIPE-Cypher is a pipeline that automatically generates balanced, executable NL-to-Cypher benchmarks from live enterprise graphs via schema profiling, constrained generation, validation, and local LLM judging.