© 2020 Strange Loop
Problem solving in the 21st century increasingly depends on the analysis of complex systems. Developing new drugs, understanding risk in financial networks, searching for answers in knowledge graphs, personalization and recommendation in social networks all require the analysis of systems composed of interconnected entities that exhibit complex behavior as a whole. Graph computing provides a conceptual model and practical platform for developing such analyses. This talk presents graph computing as an important component of every developer's toolbox. We introduce the Aurelius graph cluster which is an open-source stack enabling graph computing at scale by building on distributed systems like Cassandra, HBase, and Hadoop. This stack addresses challenging problems in graph partitioning, graph query language design and graph algorithm development with solutions inspired by physics, biology and neuroscience.
Dr. Matthias Broecheler is the lead developer of the distributed graph database Titan and has researched large scale graph database systems for more than 5 years. His award-winning research includes high performance index structures and query answering algorithms for graph structured data. In addition, he developed the Probabilistic Similarity Logic (PSL) machine learning framework to analyze and reason about multi-relational data. Matthias holds a Ph.D. in Computer Science from the University of Maryland.