The LDBC consortium is pleased to announce its 10th Technical User Community (TUC) meeting. The event will take place on the 1st of September in Munich where the VLDB (Very Large Data Bases) 43rd international conference takes also place.
The venue information, call for papers and more details will be communicated in the following weeks.
We encourage everyone form the Graph Databases and RDF community to book their agendas!
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.
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:
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:
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.
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.
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.
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.
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?