Third TUC Meeting

by Peter Boncz / on 04 Apr 2021
Location: London, United Kingdom
Event date: 19 Nov 2013 08:00 (local timezone)

The LDBC consortium is pleased to announce the third Technical User Community (TUC) meeting!

This will be a one day event in London on the 19 November
running in collaboration with the
event (18/19 November). Registered TUC
participants that would like a free pass to all of GraphConnect should
register for GraphConnect using this following coupon
code: LDBCTUC.

The TUC event will include:

  • Introduction to the objectives and progress of the LDBC project
  • Description of the progress of the benchmarks being evolved through
    Task Forces
  • Users explaining their use-cases and describing the limitations they
    have found in current technology
  • Industry discussions on the contents of the benchmarks

We will also be launching the LDBC non-profit organization, so anyone
outside the EU project will be able to
join as a member.

We will kick off new benchmark development task forces in the coming
year, and talks at this coming TUC will play an important role in
deciding the use case scenarios that will drive those

All users of RDF and graph databases are welcome to attend. If you are
interested, please contact: ldbc AT ac DOT upc DOT edu


November 19th - Public TUC Meeting

8:00 Breakfast and registration will open for Graph Connect/TUC at 8:00 am (Dexter House)

short LDBC presentation (Peter Boncz) during GraphConnect keynote by Emil Eifrem (09:00-09:30 Dexter House)

NOTE: the TUC meeting is at the Tower Hotel, nearby Dexter House.

10:00 TUC Meeting Opening (Peter Boncz)

10:10 TUC Presentations (RDF Application Descriptions)

11:30 Semantic Publishing Benchmark (SPB)

12:00-13:00 Lunch at the Graph Connect venue

Talks During Lunch:

13:00 TUC Presentations (Graph
Application Descriptions)

14:00 Social Network Benchmark (SNB)

14:30 Break

14:45 TUC Presentations (Graph Analytics)

  • Keith Houck (IBM): Benchmarking experiences with [System G Native
    Store (tentative title)]
  • Abraham Bernstein (University of Zurich): Streams and Advanced
    Processing: Benchmarking RDF querying beyond the Standard SPARQL
    Triple Store
  • Luis Ceze (University of Washington): Grappa and GraphBench
    Status Update

15:45 Break

16:00 TUC Presentations* (Possible Future RDF Benchmarking Topics)*

17:20 Meeting Conclusion (Josep Larriba Pey)

17:30 End of TUC meeting

19:00 Social dinner

November 20th - Internal LDBC Meeting

10:00 Start

12:30 End of meeting

  • coffee and lunch provided



19th November 2013


The TUC meeting will be held in The Tower hotel (Google Maps
) approximately 4 minutes
walk from
the GraphConnect conference
in London.

Getting there

  • From City Airport is the easiest: short ride on the DLR to Tower
    Gateway. Easy.
  • From London Heathrow: first need to take the Heathrow Express to
    Paddington. Then take the Circle line to Tower Hill. See


Tower Hill is
nice - book
early to get a good rate

Social Dinner

The social dinner will take place at 7 pm on Nov 19.** TODO more

Travel costs

[There is some small budget available that can be used to assist some
attendees that are otherwise unable to fund their trip. Please contact
us using the following email address if you would like more
information: ldbcgrants AT ac DOT upc DOT

LDBC/TUC Background

Looking back, we have been working on two benchmarks for the past year:
a Social Network Benchmark (SNB) and a Semantic Publishing Benchmark
(SPB). While below we provide a short summary, all the details of the
work on these benchmark development efforts can be found in the first
yearly progress

A summary of these efforts can be read below or, for a more detailed
account, please refer to: The Linked Data Benchmark Council: a Graph
and RDF industry benchmarking effort

Annual reports about the progress, results, and future work of these two
efforts will soon be available for download here, and will be discussed
in depth at the

Social Network Benchmark

The Social Network Benchmark (SNB) is designed for evaluating a broad
range of technologies for tackling graph data management
workloads. The systems
targeted are quite broad: from graph, RDF, and relational database
systems to Pregel-like graph compute
frameworks. The social
network scenario was chosen with the following goals in mind:

  • it should be understandable, and the relevance of managing such
    data should be
  • it should cover the complete range of interesting challenges,
    according to the benchmark
  • the queries should be realistic, i.e., similar data and workloads
    are encountered in

SNB includes a data generator for creation of synthetic social network
data with the following characteristics:

  • data schema is representative of real social networks
  • data generated includes properties occurring in real data, e.g.
    irregular structure, structure/value correlations, power-law
  • the software generator is easy-to-use, configurable and

SNB is intended to cover a broad range of aspects of social network data
management, and therefore includes three distinct workloads:

  • Interactive
    • Tests system throughput with relatively simple queries and
      concurrent updates, it is designed to test ACID features and
      scalability in an online operational
    • The targeted systems are expected to be those that offer
  • Business Intelligence
    • Consists of complex structured queries for analyzing online
      behavior of users for marketing purposes, it is designed to
      stress query execution and
    • The targeted systems are expected to be those that offer an
      abstract query
  • Graph Analytics
    • Tests the functionality and scalability of systems for graph
      analytics, which typically cannot be expressed in a query
    • Analytics is performed on most/all of the data in the graph as
      a single operation and produces large intermediate results, and
      it is not not expected to be transactional or need
    • The targeted systems are graph compute frameworks though
      database systems may compete, for example by using iterative
      implementations that repeatedly execute queries and keep
      intermediate results in temporary data

Semantic Publishing Benchmark

The Semantic Publishing Benchmark (SPB) simulates the management and
consumption of RDF metadata that describes media assets, or creative

The scenario is a media organization that maintains RDF descriptions of
its catalogue of creative works – input was provided by actual media
organizations which make heavy use of RDF, including the BBC. The
benchmark is designed to reflect a scenario where a large number of
aggregation agents provide the heavy query workload, while at the same
time a steady stream of creative work description management operations
are in progress. This benchmark only targets RDF databases, which
support at least basic forms of semantic inference. A tagging ontology
is used to connect individual creative work descriptions to instances
from reference datasets,
e.g. sports, geographical, or political information. The data used will
fall under the following categories: reference data, which is a
combination of several Linked Open Data datasets, e.g. GeoNames and
DBpedia; domain ontologies, that are specialist ontologies used to
describe certain areas of expertise of the publishing, e.g., sport and
education; publication asset ontologies, that describe the structure and
form of the assets that are published, e.g., news stories, photos,
video, audio, etc.; and tagging ontologies and the metadata, that links
assets with reference/domain ontologies.

The data generator is initialized by using several ontologies and
datasets. The instance data collected from these datasets are then used
at several points during the execution of the benchmark. Data generation
is performed by generating SPARQL fragments for create operations on
creative works and executing them against the RDF database system.

Two separate workloads are modeled in SPB:

  • Editorial
    Simulates creating, updating and deleting creative work metadata
    Media companies
    use both manual and semi-automated processes for efficiently and
    correctly managing asset descriptions, as well as annotating
    them with relevant
    instances from reference
  • Aggregation
    Simulates the
    dynamic aggregation of content for consumption by the distribution
    pipelines (e.g. a web-site). The publishing
    activity is described as “dynamic”, because the content is not
    manually selected and arranged on, say, a web page. Instead, templates
    for pages are defined and the content is selected when a consumer
    accesses the page.


article.pdf (application/pdf)






LDBC London 19
Nov 2013 - Telenor Resource






LDBC_Status of
the Semantic Publishing Benchmark.pdf