1,876 research outputs found
The LDBC Social Network Benchmark Interactive workload v2: A transactional graph query benchmark with deep delete operations
The LDBC Social Network Benchmark's Interactive workload captures an OLTP
scenario operating on a correlated social network graph. It consists of complex
graph queries executed concurrently with a stream of updates operation. Since
its initial release in 2015, the Interactive workload has become the de facto
industry standard for benchmarking transactional graph data management systems.
As graph systems have matured and the community's understanding of graph
processing features has evolved, we initiated the renewal of this benchmark.
This paper describes the Interactive v2 workload with several new features:
delete operations, a cheapest path-finding query, support for larger data sets,
and a novel temporal parameter curation algorithm that ensures stable runtimes
for path queries
The LDBC Social Network Benchmark Interactive workload v2: A transactional graph query benchmark with deep delete operations
The LDBC Social Network Benchmark’s Interactive workload
captures an OLTP scenario operating on a correlated social network graph.
It consists of complex graph queries executed concurrently with a stream
of updates operation. Since its initial release in 2015, the Interactive
workload has become the de facto industry standard for benchmarking
transactional graph data management systems. As graph systems have
matured and the community’s understanding of graph processing features
has evolved, we initiated the renewal of this benchmark. This paper
describes the draft Interactive v2 workload with several new features:
delete operations, a cheapest path-finding query, support for larger data
sets, and a novel temporal parameter curation algorithm that ensures
stable runtimes for path queries
The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space
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
gMark: Schema-Driven Generation of Graphs and Queries
Massive graph data sets are pervasive in contemporary application domains.
Hence, graph database systems are becoming increasingly important. In the
experimental study of these systems, it is vital that the research community
has shared solutions for the generation of database instances and query
workloads having predictable and controllable properties. In this paper, we
present the design and engineering principles of gMark, a domain- and query
language-independent graph instance and query workload generator. A core
contribution of gMark is its ability to target and control the diversity of
properties of both the generated instances and the generated workloads coupled
to these instances. Further novelties include support for regular path queries,
a fundamental graph query paradigm, and schema-driven selectivity estimation of
queries, a key feature in controlling workload chokepoints. We illustrate the
flexibility and practical usability of gMark by showcasing the framework's
capabilities in generating high quality graphs and workloads, and its ability
to encode user-defined schemas across a variety of application domains.Comment: Accepted in November 2016. URL:
http://ieeexplore.ieee.org/document/7762945/. in IEEE Transactions on
Knowledge and Data Engineering 201
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