Celebrating DIAS' Achievements: Two more Doctors in the family

Viktor's thesis entitled Efficient Approximate Analytics via Adaptive Context-Conscious Query Processing, redesigns approximate analytical processing to support interactive data exploration over structured and unstructured data while exploiting hardware and workload-level optimizations. Instead of being limited to static data, query, and workload assumptions, this thesis embraces runtime adaptivity. Overall, our design enables efficient and scalable analytics for ad-hoc workloads by exposing execution primitives amenable to inter and intra-query optimization. As a result, users benefit from simpler and faster data exploration and richer insights from structured and unstructured data, improving the overall data utility and system efficiency.

Aunn's thesis entitled Efficient Operational Analytics on Fresh Data, designs adaptive HTAP that enables efficient operational analytics on fresh data. Instead of presuming workload access patterns and data freshness requirements at design time, this thesis embraces runtime adaptivity as a core design principle for scaling mixed workloads in data management systems. Specifically, adaptive HTAP elastically schedules workload and adapts fresh data access paths at runtime. As a result, this design provides flexibility and coverage for unpredictable workload- and freshness requirements by adapting system design decisions at runtime.

The successful defense of Viktor and Aunn’s PhD theses marks a pivotal moment in the field of data management and analytics. Their research not only contributes to the academic community but also opens new avenues for practical applications in industries that rely heavily on real-time, dynamic data processing. As they move forward in their careers, Viktor and Aunn are set to lead the next wave of innovation, continuing to push the boundaries of what’s possible in the ever-evolving landscape of data analytics.