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11th TUC Meeting, University of Texas at Austin

  • Posted on: 21 February 2018
  • By: Damaris Coll

The LDBC consortium is pleased to announce its Eleventh Technical User Community (TUC) meeting.

This will be a one-day event preceding the SIGMOD/PODS 2018 conference in Houston, Texas the previous day.  TUC meeting will take place the 8th June, 2018 at University of Texas in Austin.

LDBC Technical User Community meetings serve to:

  1. Learn about progress in the LDBC task forces on graph benchmark development
  2. Give feedback on graph benchmark developments
  3. Hear about user experiences with graph data management technologies



1st Joint GRADES and NDA event held in Houston the 10th of June 2018

  • Posted on: 20 February 2018
  • By: Damaris Coll

The 1st joint international workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) will take place the 10th of June 2018. This event will be co-located with the ACM SIGMOND International Conference on Management of Data at Houston, Texas.

The focus of the GRADES-NDA workshop is the application areas, usage scenarios and open challenges in managing large-scale graph-shaped data. The workshop is a forum for:

Fifth GRADES workshop held at SIGMOD/PODS 2017 in Chicago

  • Posted on: 23 May 2017
  • By: Damaris Coll

On Friday May 19 the fifth GRADES workshop was held at the primary database conference SIGMOD/PODS 2017 in Chicago.

GRADES is sponsored by LDBC, as the workshop aims to stimulate research and development of new graph data management technologies, the sharing of user experience and new graph data management scenarios, as well as indeed performance benchmarking of these systems. All these topics, and specifically the latter are also in the scope of LDBC.

8th TUC Meeting - Juan Sequeda (Capsenta). Integrating Data using Graphs and Semantics

  • Posted on: 8 February 2017
  • By: Adrian Diaz

Juan Sequeda, Co-founder of Capsenta, gave an interesting talk on how can we integrate data using graphs and semantics (semantic data virtualization). As Mr. Sequeda said, the idea is to integrate data without needing to move it around. Juan started off his presentation talking about the huge gap that exists between the IT departments, guardians of the data and the business development departments, trying to extract insights about the data. He used a clear example to illustrate this gap:

8th TUC Meeting - Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph Data Models.

  • Posted on: 7 February 2017
  • By: Adrian Diaz

During the 8th TUC Meeting held at Oracle’s facilities in Redwood City, California, Zhe Wu, Software Architect at Oracle Spatial and Graph, explained how is his team trying to bridge RDF Graph and Property Data Models.

After making a brief overview about what is a graph he presented Oracle’s Graph strategy, they basically treat graphs as another data type on every platform (Hadoop, Oracle’s own database and, of course, in the Cloud). He also explained that his team is developing in 3 directions at the same time:

8th TUC Meeting – Yinglong Xia (Huawei), Big Graph Analytics Engine

  • Posted on: 3 February 2017
  • By: Adrian Diaz

Yinlong started his talk with an introduction of his new position at Huawei, what is the company doing and more specifically how is it involved with Big Data Research and graphs. He also explained that his research center is currently working on Big Data Analytics and Management from 4 sides: Natural Language Processing, Graph analyrics, Machine Learning and Deep Learning. His team at the same time, focuses on 4 market segments that include financial graph analytics, consumer data gathered from smartphones and other portable devices, telecommunications and cloud technology.

8th TUC Meeting – George Fletcher (TU Eindhoven), gMark: Schema-driven data and workload generation for graph databases

  • Posted on: 31 January 2017
  • By: Adrian Diaz

George Fletcher, Associate Professor at the Eindhoven University of Technology, presented gMark, an open-source framework for generating synthetic graph instances and workloads. The main focus of gMark has been to tailor different graph data management scenarios, often driven by query workloads. Such as multi-query optimization, workload-driven graph database physical design or mapping discovery and query rewriting data integration systems.

8th TUC Meeting – Marcus Paradies (SAP) Social Network Benchmark, Business Intelligence Workload

  • Posted on: 27 January 2017
  • By: Adrian Diaz

Marcus Paradies, Software developer at SAP extended the talk Arnau Prat gave about the SNB, in this case about the Intelligence workload. In contrast with the 17+4 queries the Interactive workload has, the Business Intelligence (BI) workload consists on 24 queries that can be seen as OLAP-style against the OLTP-style of the Interactive one. The BI focuses on analytic queries and they touch the whole graph.

8th TUC Meeting - Sergey Edunov (Facebook). Generating realistic trillion-edge graphs.

  • Posted on: 24 January 2017
  • By: Adrian Diaz

Sergey Edunov, Software Engineer at Facebook gave a great talk on how and why his company generating large-scale social graphs. The underlying reasons to start such an ambitious project are capacity planning to make sure that their system will be able to handle a graph that keeps growing year after year and fair evaluation of their system against the ones being implemented by other companies.

8th TUC Meeting - Weining Qian (ECNU). On Statistical Characteristics of Real-Life Knowledge Graphs.

  • Posted on: 23 January 2017
  • By: Adrian Diaz

Weining Qian, professor at East China Normal University presented his talk on Statistical Characteristics of Real-Life Knowledge graphs during the 8th TUC Meeting held at Oracle’s facilities in Redwood City, California.

Qian explained that term knowledge graph was introduced by Google in 2012 and it has been an evolution of the semantic web. Professor Qian then introduced the main question of his talk: how can we efficiently manage knowledge graphs? Are the existing benchmarks sufficient to test them since most of these benchmarks focus only on Social Networks?