Upcoming talks

16th Seminar

December 17, 2021, 4:00PM-5:30PM (CET)

The 16th seminar of DSDSD will feature talks by
Kaijie Zhu (TU Eindhoven)
Michael Schmidt (Amazon Web Services) .

Leveraging temporal and topological selectivities in temporal-clique subgraph query processing

Kaijie Zhu (TU Eindhoven)

We study the problem of temporal-clique subgraph pattern matching. In such patterns, edges are required to jointly overlap in time within a given temporal window in addition to forming a topological sub-structure. This problem arises in many application domains, e.g., in social networks, life sciences, smart cities, telecommunications, and others. State-of-the-art subgraph matching techniques, however, are shown to be limited and inefficient in processing queries with both temporal and topological constraints. We propose an approach that takes full advantage of both topological and temporal selectivities during the processing of temporal-clique subgraph queries. Additionally, we investigate a number of optimizations that can be introduced into our approach to improve its efficiency. Our experimental results demonstrate that our approach outperforms the existing methods by a wide margin at a small additional storage cost. For more details, you can read our ICDE 2021 paper [1].

[1] Kaijie Zhu, George Fletcher, and Nikolay Yakovets. Leveraging temporal and topological selectivities in temporal-clique subgraph query processing. In proceedings of the IEEE International Conference on Data Engineering (ICDE). 2021: 672-683

Kaijie Zhu started a PhD project “Temporal graph query processing in scalable datasets” in the Department of Mathematics and Computer Science at Eindhoven University of Technology under the supervision of prof.dr. George Fletcher and dr. Nikolay Yakovets at Eindhoven, the Netherlands. And on November 9, 2021, he received his Ph.d degree. Now his interest lies in database query processing and database security.

OneGraph to Rule them All

Michael Schmidt (Amazon Web Services)

At Amazon Neptune, we work backwards from our customers. One insight that we got from listening to customers is that, in many cases where they explore Neptune as a solution to their problems, it’s primarily “just about graph”: they want to use the relationships in their data to solve business problems using knowledge graphs, identity graphs, fraud graphs, and more. The choice of technology, selecting Property Graph or RDF and choosing a specific query language, is often a secondary consideration and creates friction throughout the adoption process. To reduce this friction, we are working towards a unified graph database landscape that enables interoperability between data models and query languages, which we call OneGraph. In this presentation, we discuss resulting challenges at implementation and architectural level across all layers of the database stack — from the need for a unified storage model, data model independent statistics, a unified query execution runtime that overcomes conceptual differences of the query languages, our approach towards building a unified query execution and translation layer, up to the challenges in enabling widespread graph adoption and establishing a unified customer experience via data and query level interoperability.

Michael Schmidt is a Principal Engineer with Amazon Web Services. On his mission to improve Amazon Neptune’s performance, scalability, and user experience, he is the tech lead for activities centered around graph query statistics, optimization, and execution. Prior to joining Amazon Neptune, Michael was involved in the development of the Blazegraph triple store and worked as CTO for metaphacts, building an end-user focused platform that helps customers building and utilizing Enterprise Knowledge Graphs. Michael holds a PhD from University of Freiburg and was awarded the ICDT 2020 Test of Time Award for his work on “Foundations of SPARQL query optimization”