In today’s area of Big Data, data is collected to a large extent automatically by hard- and software sensors in fine granularity and low abstraction. Where users interact with data, e.g. for data analytics, they typically think, reason, and talk about entities of larger granularity and higher abstraction. For instance, a graph of twitter communication contains individual messages, retweet relationships, etc. while social network analysis done on such data is interested in discussions, topics, communities and so on. User-level concepts are often multiple abstraction levels higher than the concepts data is captured and stored in. The query language of database systems and data processing systems is the main means for users to bridge this concept chasm easily and repetitively. To do so, a query language has to (1) be able to create entirely new entities within its data model to lift data into higher level concepts and (2) be composable over its data model, so that the users can express a stack of multiple such abstraction steps with the same language. In this talk, I will show how this can be accomplished in graph query languages particularly for the property graph model.
Bio
Hannes Voigt is a post-doctoral researcher at the Dresden Database Systems Group, Technische Universität Dresden and obtained his PhD from the same university in 2014. He worked on various database topics such as physical design, management of schema-flexible data, and self-adapting indexes. From 2010 to 2011, he worked at SAP Labs, Palo Alto contributing to a predecessor of SAP HANA Graph Project. His current research focuses on database evolution and versioning, declarative graph query languages and efficient graph processing on NUMA in-memory storage systems. He is also member of the LDBC Graph Query Language Standardization Task Force.
