The List

Snapshot Aug 2016 from Berkeley DB Prelim

Basics and Foundational Systems

Query Processing



DBMS Architectures Revisited

Distributed Data, Weak Isolation, Relaxed Consistency

Parallel Dataflow

The Web and Data

Materialized Views, Cubes and Aggregation

Special-case Data Models: Streams, Semistructured, Graphs

Data Integration, Provenance and Transformation

Systems support for ML

Tips We Got

What it’s like

  • It could be scary
  • It could get detailed, but only for the important ones!

How to answer better

  • Get specific/concrete with examples: makes explaining your ideas faster.
  • Never bull shit (stop the out pouring of buzz words). State clearly if you don’t know the exact answer. Could ask for some guidance.
  • Don’t be afraid to shut up. No need to fill in the silence by saying anything that comes to mind. This will cause examiner to ask follow up questions about potentially false statements. Bottom line: only say correct things and nothing more.
  • When possible, present the outline and write neatly on the board so that (1) you could refer to it (2) everyone is on the same page.

How to prepare better

  • Need to look at evaluation section? Only look at evaluation to know what works! This is important for understanding the problem space.
  • Self test by trying to summarize the most important points in a paper in 5 minutes.

My Experience

I really enjoyed the reading experience. I feel a lot more confident now about the foundations of databases and feel comfortable going through a dense 40 page paper. It’s amazing how much of the details I missed when I was reading some papers during my college years.

Papers I liked particularly from the list: