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Dan's TAB

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Oliver Kennedy 2018-02-09 13:58:15 -05:00
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@ -24,9 +24,43 @@ schedule:
who: Dan Suciu (University of Washington)
where: Location TBD
details:
abstract: TBD
abstract: |
Database engines today use the same approach to evaluate a
query as they did forty years ago: convert the query into a query
plan, then execute each operator individually, e.g. a join, followed
by another join, followed by duplicate elimination. It turns out that
converting a query into binary joins is theoretically suboptimal, and
this can lead to poor performance over very large datasets. A new
query evaluation paradigm has emerged recently (some of it coming out
of U. of Buffalo), which, in some cases, leads to provably optimal
algorithms. In this talk I will give a brief survey and some new
results of this new paradigm: I will review the AGM bound on the query
size (Atserias, Grohe and Marx), the worst-case optimal "generic join"
algorithm for full conjunctive queries (Ngo, Re, and Rudra), and our
new algorithm for aggregate queries, called PANDA, which matches the
best known running times for certain graph problems.
(Joint work with Mahmoud Abo Khamis and Hung Ngo)
bio: |
Dan Suciu is a full professor of computer science at the University of Washington. He received his Ph.D. from the University of Pennsylvania in 1995 under the supervision of Val Tannen. After graduation, he was a principal member of the technical staff at AT&T Labs until he joined the University of Washington in 2000. Suciu does research in data management, with an emphasis on Web data management and managing uncertain data. He is a co-author of an influential book on managing semistructured data.
Dan Suciu is a Professor in Computer Science at the University of
Washington. He received his Ph.D. from the University of Pennsylvania
in 1995, was a principal member of the technical staff at AT&T Labs
and joined the University of Washington in 2000. Suciu is conducting
research in data management, with an emphasis on topics related to Big
Data and data sharing, such as probabilistic data, data pricing,
parallel data processing, data security. He is a co-author of two
books Data on the Web: from Relations to Semistructured Data and XML,
1999, and Probabilistic Databases, 2011. He is a Fellow of the ACM,
holds twelve US patents, received the best paper award in SIGMOD 2000
and ICDT 2013, the ACM PODS Alberto Mendelzon Test of Time Award in
2010 and in 2012, the 10 Year Most Influential Paper Award in ICDE
2013, the VLDB Ten Year Best Paper Award in 2014, and is a recipient
of the NSF Career Award and of an Alfred P. Sloan Fellowship. Suciu
serves on the VLDB Board of Trustees, and is an associate editor for
the Journal of the ACM, VLDB Journal, ACM TWEB, and Information
Systems and is a past associate editor for ACM TODS and ACM TOIS.
Suciu's PhD students Gerome Miklau, Christopher Re and Paris Koutris
received the ACM SIGMOD Best Dissertation Award in 2006, 2010, and
2016 respectively, and Nilesh Dalvi was a runner up in 2008.
- when: May 17; Time TBD
what: Title TBD
who: Alexandra Meliou (UMass Amherst)