Data Systems that are Easy to Design, Tune and Use

Friday, October 28, 2016 -
11:00am to 12:00pm
2310 CS

Speaker Name: 

Stratos Idreos

Speaker Institution: 

Harvard University




How far away are we from a future where a data management system sits in the critical path of everything we do? Already today we often go through a data system in order to do several basic tasks, e.g., to pay at the grocery store, to book a flight, to find out where our friends are and even to get coffee. Businesses and sciences are increasingly recognizing the value of storing and analyzing vast amounts of data. Other than the expected path towards an exploding number of data-driven businesses and scientific scenarios in the next few years, in this talk we also envision a future where data becomes readily available and its power can be harnessed by everyone. What both scenarios have in common is a need for new kinds of data systems that “just work”; they are easy to design, easy to tune, and easy to use. We will discuss this vision and our recent efforts towards 1) adaptive data systems that can adapt to data and access patterns on-the-fly, 2) self-designing data systems that make it easy to spin-off and test new data system architectures and 3) curious data systems that make it easy to explore data even if we do not know what queries to ask.

Speaker bio:
Stratos Idreos is an assistant professor of Computer Science at Harvard University where he leads DASlab, the Data Systems Laboratory @ Harvard SEAS. Stratos works on data system architectures with emphasis on how we can make it easy to design efficient data systems as applications and hardware keep evolving and on how we can make it easy to use these systems even for non-experts. For his doctoral work on Database Cracking, Stratos won the 2011 ACM SIGMOD Jim Gray Doctoral Dissertation award and the 2011 ERCIM Cor Baayen award. He is also a recipient of an IBM zEnterpise System Recognition Award, a VLDB Challenges and Visions best paper award and an NSF Career award. In 2015 he was awarded the IEEE TCDE Early Career Award from the IEEE Technical Committee on Data Engineering for his work on adaptive data systems.