DataTech: Techniques for scalable data storage and analysis

The proposal aims to do theoretical research on scalable solutions for data storage and analysis. It aims to unify two approaches: that of classical data structures and the relatively newer field of data sketches.

These two fields study questions that lie at the heart of every algorithm, data analysis pipeline or data management system.

The aim is to identify and fix performance bottlenecks in both fields, and make claims that are backed by strong, provable theoretical guarantees.

This will be achieved through a cross-pollination of techniques between the two fields while capitalising on recent major advances in data structures. This is a previously unexplored, but timely relationship.

As applications become more data-intensive, it becomes crucial to ensure that the performance of the underlying infrastructure does not grow with the size of the data.

By building a common frame of reference for the two fields, the proposal will lead to conceptual insights that can lead to improvements in a plethora of existing data storage and analysis systems.The applicant is a tenure-track Assistant Professor in the Computer Science department at the KTH Royal Institute of Technology.

She will dedicate 50% of her research time to working on the project. The grant is meant to fund a PhD student working 100% on the project for 4-years.