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.

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.

Weining Qian, professor at East China Normal University presented his talk on Statistical Characteristics of Real-Life Knowledge graphs during the 8^th 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?

Lijun Chang, DECRA Fellow at the University of New South Wales talked about how to make subgraph matching more efficient thanks to postponing Cartesian products. They key problem he explained was the extraction of subgraph isomorphic embeddings. The applications of this process are wide enough to cover protein interaction research, social network analysis and even chemical compound investigation. The testing of subgraph isomorphism is an NP-complete type of problem however, his team is focusing on enumerating all subgraph embeddings which, he explains, is even harder.