We conduct research on all areas of database systems and information management.
Our projects range from the design of new user interfaces and query languages to low-level query execution issues, ranging from design of new systems for database analytics and main memory databases to query processing in next generation pervasive and ubiquitous environments, such as sensor networks, wide area information systems, personal databases, and the Web.
Starling is a scalable query execution engine built on cloud function services that computes at a fine granularity, helping people more easily match workload demand.
The goal of the project is to make database management systems resilient to workload variations (e.g., load spikes due to news events) by enabling them to automatically expand and contract the size of the database cluster and balance load across servers.
Our goal is to build a database framework in which the database predicts user queries and builds indexes in advance to allow future queries to run faster.
As part of Data Civilizer we are designing abstractions and building tools and systems to help people with their data-related tasks, from discovering, to cleaning, to transforming it. The aim is to shape the data in a way that is easy to analyzer---for example to fit a model or fill in a report.
We are building a database for autonomous vehicle sensor data that addresses the challenges presented by the potential scale of autonomous vehicle data and the unique characteristics of the data.
Data scientists universally report that they spend at least 80% of their time finding data sets of interest, accessing them, cleaning them and assembling them into a unified whole.
This project shows that we can successfully use predictive modeling to enable a database cluster to elastically expand or contract in anticipation of changes in the workload.
Our goal is to develop an adaptive storage manager for analytical database workloads in a distributed setting. It works by partitioning datasets across a cluster and incrementally refining data partitioning as queries are run.
