Speaker: Stratos Idreos, Harvard University
Abstract: Data structures are critical in any data-driven scenario, and they define the behavior of modern data systems. However, they are notoriously hard to design due to a massive design space and the dependence of performance on workload and hardware which evolve continuously. In this talk, we ask two questions: What if we knew how many and which data structures are possible to design? What if we could compute the expected performance of a data structure design on a given workload and hardware without having to implement it and without even having access to the target machine? We will discuss our quest for 1) the first principles of data structures, 2) design continuums that make it possible to automate design, and 3) self-designing systems that can morph between what we now consider fundamentally different structures. We will draw examples from the NoSQL key-value store design space and discuss how to accelerate them and balance space-time tradeoffs.
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
Rising
Star
Award
from
the
IEEE
Technical
Committee
on
Data
Engineering
for
his
work
on
adaptive
data
systems.