Northwest Database Society (NWDS)

Mission Statement

The goal of NWDS is to bring together researchers and practitioners in the field of databases and data management systems working in the Pacific North-West.

One of our main activities is a talk series with a variety of distinguished speakers from academia and industry. These talks are also part of the Microsoft Database Lecture Series (sponsored by Microsoft). This quarter’s talks are organized by Alvin.

We thank our UWDB affiliates for supporting NWDS.

Upcoming Talks

Winter 2019

Speaker: Azza Abouzied

Where: University of Washington, Seattle.
Allen School of Computer Science and Engineering.
Paul G. Allen Center, CSE 305.

When: Wednesday, January 9, 2019. 3:30pm - 4:30pm

Title: Time-series Querying by Sketching

Abstract: In this talk, I’ll describe the design of Qetch: a time series querying tool, where users can freely sketch patterns on a scale-less canvas. By studying how humans (mis)-sketch time series patterns, we developed a novel matching algorithm that accounts for human sketching errors: humans preserve visually salient perceptual features but often non-uniformly scale and locally distort a pattern. Qetch enables the easy construction of complex and expressive queries with two key features: regular expressions over sketches and relative positioning of sketches to query multiple time-aligned series. Through user studies, we demonstrate the effectiveness of Qetch’s different interaction features. We also demonstrate the effectiveness of Qetch’s matching algorithm compared to popular algorithms on targeted, and exploratory query-by-sketch search tasks on a variety of data sets.

Bio: Azza Abouzied is an Assistant Professor of Computer Science at New York University, Abu Dhabi. Azza’s research work focuses on designing novel and intuitive data analytics tools and on supporting complex analytics natively within databases, such as specifying and solving objective optimization problems. Her work combines techniques from various fields such as UI-design, active learning and databases. She received her doctoral degree from Yale in 2013 and BSc (CS) from Dalhousie. She spent a year as a visiting scholar at UC Berkeley. She is the recipient of an NSERC Canada Graduate Scholarships-Doctoral Fellowship, and multiple research paper awards including a SIGMOD Research Highlight Award, a best of VLDB citation and a best CHI paper award. She is also one of the co-founders of Hadapt – a Big Data analytics platform.

Speaker: Paris Koutris

Where: University of Washington, Seattle.
Allen School of Computer Science and Engineering.
Paul G. Allen Center, CSE 305.

When: Friday, January 25, 2019. 11:00am - 12:00pm

Title: TBD

Abstract: TBD

Bio: TBD

Speaker: Michael Cafarella

Where: University of Washington, Seattle.
Allen School of Computer Science and Engineering.
Paul G. Allen Center, CSE 305.

When: Friday, February 1, 2019. 2:30pm - 3:30pm

Title: Data-Intensive Systems for the Social Sciences

Abstract: The social sciences are crucial for deciding billions in spending, and yet are often starved for data and badly underserved by modern computational tools. Building data-intensive systems for social science workloads holds the promise of enabling exciting discoveries in both computational and domain-specific fields, while also making an outsized real-world impact.

This talk will describe two data systems for the social sciences. The first is RaccoonDB, a declarative nowcasting data management system, which enables users to predict real-world time-series phenomena from social media signals. RaccoonDB’s novel query optimization methods allow it to generate useful social science predictions 123 times faster than competing systems, using just 10% of the computational resources. When applied to unemployment phenomena, the system yields predictions with accuracy that is comparable to predictions from real-world economists.

The second system is an information extraction system designed to analyze online text and help law enforcement officers identify potential human trafficking victims. This system has been successfully applied to real-world cases. In addition, the resulting extracted dataset enables several novel social science findings about behavior in an illicit and often opaque market.

Bio: Michael Cafarella is an Associate Professor of Computer Science and Engineering at the University of Michigan. His research interests include databases, information extraction, data integration, and data mining. He has published extensively in venues such as SIGMOD, VLDB, and elsewhere. Mike received his PhD from the University of Washington in 2009 with advisors Oren Etzioni and Dan Suciu. His academic awards include the NSF CAREER award, the Sloan Research Fellowship, and the VLDB Test of Time Award. In addition to his academic work, Mike cofounded (with Doug Cutting) the Hadoop open-source project. In 2015 he cofounded (with Chris Re and Feng Niu) Lattice Data, Inc., which is now part of Apple.

Past Talks

Listed in reverse chronological order. Click here for abstracts.

Fall 2018

Summer 2018

Winter 2018

Fall 2017

Spring 2017

Winter 2017

Fall 2016

Spring 2016

Winter 2016

Fall 2015

Earlier talks

Mailing List

Please sign up for the nwds mailing list here. We use this list primarily to send announcements for upcoming events. After you register, you can send mail to that list at nwds at

To become a member, please contact Magda or Alvin.


The North-West Database Society was founded on January 1st 2006 by Dan Suciu and Magdalena Balazinska. It is inspired by the New-England Database Society.