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?
Peter Boncz, Research Scientist at the Centrum Wiskunde & Informatica in the Netherlands, talked about the updates on the Graph Query Language Task Force after being alive for a year. This Task Force was created to answer an issue detected during the benchmark meetings, all the workload is created in English text because there is no common graph query language.
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.
During the 8th TUC Meeting Eugene Chong from Oracle USA explained what his team and himself had done to improve RDF query processing in their database.
Jerven Bolleman, Lead Software Developer at Swiss-Prot Group, explained why are they offering a free SPARQL and RDF endpoint for the world to use and why is it hard to optimize it. The data biologists use tends to be extremely ambiguous and dirty, additionally, scientists are always trying to find new questions to ask, thus why the difficulty regarding the optimization of UniProt, they wouldn’t be offering the right service to their users by optimizing the query patterns. Furthermore, since UniProt is publicly funded, all the data needs to be public.
Martin Zand, Professor of Medicine and Public Health Sciences at the Rochester enter for Health Informatics, switched the focus of the presentations talking as a user of graph databases. Zand pinpointed the relevance of using graph in healthcare comparing 3 characteristics of healthcare to their counterpart with graphs:
- Healthcare is delivered by networks.
- Patients traverse those networks.
- The topology of the networks influences outcomes.
The talk of Dr. Zand was structured around the presentation of 3 uses cases:
The LDBC consortium is pleased to announce its Ninth Technical User Community (TUC) meeting.
This will be a two-day event at the SAP Headquarters in Walldorf, Germany on Thursday 9th to Friday 10th of Frebuary 2017.
This will be the third TUC meeting after the finalisation of the LDBC FP7 EC funded project. The event will basically set the following aspects:
Last 22nd and 23rd of June took place the 8th edition of the Technical User Community Meeting held in Oracle headquarters at Redwood Shore (California).
During these two days LDBC hosted more than 20 presentations from key members of the industry such as Oracle, Facebook, Neo4j, SAP or Huawei and research regarding the updates on the work within the council, and graphs & RDF applications. We are going to share all of them as independent blog posts during the following weeks.
Thanks to Oracle for hosting this event!
LDBC is proud to announce the new LDBC Graphalytics Benchmark draft specification. LDBC Graphalytics is the first industry-grade graph data management benchmark.