03/2026 BIFOLD Colloquium

Scalable Data Management and Analytics for Modern Observability

Abstract:

Observability is the ability to understand and control the behavior of large-scale software systems using run-time telemetry. Managing observability data involves handling massive volumes of heterogeneous time series while operating under tight resource constraints. Troubleshooting the observed system requires efficient exploration of this data through interactive queries with time-oriented operations. Advances in AI have enabled more sophisticated data analysis techniques such as anomaly detection and explanation. This talk presents examples from our recent research in data management for modern observability in response to these challenges and opportunities.

Short-bio:

Nesime Tatbul is a Senior Staff Research Scientist at Intel. For over a decade now, she has been based at MIT, overseeing Intel's university research programs on data systems and artificial intelligence. Previously, she received a Ph.D. from Brown University and held a faculty position at ETH Zurich. Her research contributions in data stream systems, time series analytics, and learned data management have been widely cited and recognized by awards. Nesime is an ACM Distinguished Member, an IEEE Senior Member, and a Trustee Emeritus of the VLDB Endowment.