Speaker: Mohammad Sadoghi, UC Davis
Abstract: Arguably
data
is
a
new
natural
resource
in
the
enterprise
world
with
an unprecedented
degree
of
proliferation
and
heterogeneity.
However,
to
derive real-time
actionable
insights
from
the
data,
it
is
important
to
bridge
the gap
between
analyzing
a
large
volume
of
data
(i.e.,
OLAP)
and
managing
the
data
that
is
being
updated
at
a
high
velocity
(i.e.,
OLTP).
Historically, there
has
been
a
divide
where
specialized
engines
were
developed
to
support either
OLAP
or
OLTP
workloads
but
not
both;
thus,
limiting
the
analysis
to stale
and
possibly
irrelevant
data.
In
this
talk,
we
present
our
proposed
architecture
to
combine
the
real-time processing
of
analytical
and
transactional
workloads
within
a
single
unified engine.
To
support
querying
and
retaining
the
current
and
historic
data, we
design
a
novel
efficient
index
maintenance
techniques
paving
the
way
to a
novel
optimistic
concurrency
control.
From
the
concurrency
perspective,
we further
pose
a
question:
is
it
possible
to
have
concurrent
execution
over shared
data
without
having
any
concurrency
control?
To
answer
this
question, we
investigate
a
deterministic
approach
to
transaction
processing
geared towards
many-core
hardware
by
proposing
a
novel
queue-oriented,
control-free concurrency
architecture
(QueCC)
that
exhibits
minimal
coordination
during execution
while
offering
serializable
guarantees.
From
the
storage
perspective, we
develop
an
update-friendly
lineage-based
storage
architecture
(LSA)
that offers
a
contention-free
and
lazy
staging
of
columnar
data
from
a
write-optimized form
(OLTP)
into
a
read-optimized
form
(OLAP)
in
a
transactionally
consistent
approach.
Finally,
we
share
our
vision
to
move
from
a
centralized
platform onto
a
secure
democratic
and
decentralized
computational
model.
Bio: Mohammad Sadoghi is an Assistant Professor of Computer Science at the University of California, Davis. Formerly, he was an Assistant Professor at Purdue University and Research Staff Member at IBM T.J. Watson Research Center. He received his Ph.D. from the Computer Science Department at the University of Toronto in 2013. His research spans all facets of secure and massive-scale data management. At UC Davis, he leads the ExpoLab research group with the aim to pioneer a new exploratory data platform—referred to as ExpoDB—a distributed ledger that unifies secure transactional and real-time analytical processing, all centered around a democratic and decentralized computational model. Prof. Sadoghi has over 60 publications and has filed 34 U.S. patents. His SIGMOD'11 paper was awarded the EPTS Innovative Principles Award, his EDBT'11 paper was selected as one of the best EDBT papers in 2011, and his ESWC'16 paper won the Best In-Use Paper Award. He is serving as Workshop/Tutorial Co-Chair at Middleware'18, has served as the PC Chair (Industry Track) at ACM DEBS'17, co-chaired a new workshop series, entitled Active, at both ICDE and Middleware, and co-chaired the Doctoral Symposium at Middleware'17. He served as the Area Editor for Transaction Processing in the Encyclopedia of Big Data Technologies by Springer. He is co-authoring a book on "Transaction Processing on Modern Hardware" as part of Morgan & Claypool Synthesis Lectures on Data Management. He regularly serves on the program committee of SIGMOD, VLDB, ICDE, EDBT, Middleware, ICDCS, DEBS, and ICSOC.