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Yet another great presentation at MIT, and unfortunately on the same
evening:<br>
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<th nowrap="nowrap" valign="BASELINE" align="RIGHT">Subject: </th>
<td>[GBC-ACM] Talk : Wednesday, Nov 17, 2010 7-9pm: Michael
Stonebraker on "SciDB: Big Analytics on Big Data"</td>
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<th nowrap="nowrap" valign="BASELINE" align="RIGHT">Date: </th>
<td>Thu, 11 Nov 2010 09:37:45 -0500</td>
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<th nowrap="nowrap" valign="BASELINE" align="RIGHT">From: </th>
<td>Peter Mager <a class="moz-txt-link-rfc2396E" href="mailto:p.mager@computer.org"><p.mager@computer.org></a></td>
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<th nowrap="nowrap" valign="BASELINE" align="RIGHT">To: </th>
<td><a class="moz-txt-link-abbreviated" href="mailto:GBC-ACM@mit.edu">GBC-ACM@mit.edu</a></td>
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<pre>IEEE Computer Society, GBC/ACM and DAMA
7:00 PM, Wednesday, November 17, 2010
Broad Institute Auditorium (MIT building NE-30)
SciDB: Big Analytics on Big Data
Michael Stonebraker
M.I.T. Computer Science and Artificial Intelligence Laboratory and
Zetics, Inc.
Serious analytic applications (Big Analytics) entail clustering and
transforming data as well as non-trivial computations such as finding
eigenvalues, and curve fitting. Traditionally, such applications have
been found in various natural science fields, but increasingly they are
required by large web properties for personalization of ranking
functions, optimization of advertising placement and budgets, etc. Big
Analytics should be distinguished from traditional business intelligence
(small analytics) which is well served by standard SQL aggregates and
grouping functions.
When performing big analytics on small (main memory) data, a customer is
well served by standard statistical packages such as R or MatLab. The
focus of this talk is on big (disk-based) data, and we present a new
approach in this application area, SciDB, which adopts an array data
model and a query language with DBMS as well as analytical primitives.
In addition, SciDB supports uncertain data and version control and
allows arrays to be “chunked” across multiple nodes in a cluster,
perhaps with overlap among the chunks. Early benchmarking results show
SciDB to be 10-100 times faster than an RDBMS on big analytics – big
data applications.
We sketch the design of SciDB, discuss its implementation status, open
source business model, and contrast our approach with other options,
including RDBMS, Hadoop and stat packages.
Mike Stonebraker is Adjunct Professor of Computer Science at M.I.T. and
CTO of 3 companies that are commercializing new database related
technologies that he initiated. He is widely recognized as one of the
world's foremost experts in database technology and is noted for his
insight in operating systems and expert systems. Mike received a
Bachelor of Science degree from Princeton University and Master of
Science and Doctor of Philosophy degrees from the University of
Michigan. He has held visiting professorships at the Pontifico
Universitade Catholique (PUC), Rio de Janeiro, Brazil; the University of
California, Santa Cruz; and the University of Grenoble, France. Dr.
Stonebraker was the main architect of the INGRES relational DBMS and the
object-relational DBMS, POSTGRES. These prototypes were developed at the
University of California at Berkeley, where Stonebraker was a Professor
of Computer Science for twenty five years. More recently at M.I.T. he
was a co-architect of the Aurora/Borealis stream processing engine, the
C-Store column-oriented DBMS, the H-Store transaction engine, the
Morpheus search engine and the SciDB complex analytics engine, all of
which have been commercialized. Presently he serves as Chief Technology
Officer of Zetics (commercial SciDB), VoltDB (commercial H-Store), and
Goby (commercial Morpheus). Professor Stonebraker is the author of
scores of research papers on data base technology, operating systems and
the architecture of system software services. He received the ACM System
Software Award in 1992 for his work on INGRES. Additionally, he received
the first annual Innovation award from the ACM SIGMOD special interest
group in 1994 and was elected to the National Academy of Engineering in
1997. He received the IEEE John Von Neumann award in 2005 and is
presently an Adjunct Professor of Computer Science at M.I.T., where he
is working on a variety of future-generation data-oriented projects. See
<a class="moz-txt-link-freetext" href="http://en.wikipedia.org/wiki/Michael_Stonebraker">http://en.wikipedia.org/wiki/Michael_Stonebraker</a> and
<a class="moz-txt-link-freetext" href="http://www.csail.mit.edu/user/1547">http://www.csail.mit.edu/user/1547</a> for more details.
This joint meeting of the Boston Chapter of the IEEE Computer Society,
GBC/ACM and DAMA will be held in the Broad Institute Auditorium (MIT
building NE-30). The Broad Institute is on Main St between Vassar and
Ames streets. You can see it on a map at this location.
<a class="moz-txt-link-rfc2396E" href="http://whereis.mit.edu/map-jpg?zoom=level2&centerx=710846&centery=496467&oldzoom=level3&map.x=340&map.y=72"><http://whereis.mit.edu/map-jpg?zoom=level2&centerx=710846&centery=496467&oldzoom=level3&map.x=340&map.y=72></a>
The auditorium is on the ground floor near the entrance.
Up-to-date information about this and other talks is available online at
<a class="moz-txt-link-freetext" href="http://ewh.ieee.org/r1/boston/computer/">http://ewh.ieee.org/r1/boston/computer/</a>. You can sign up to receive
updated status information about this talk and informational emails
about future talks at <a class="moz-txt-link-freetext" href="http://mailman.mit.edu/mailman/listinfo/gbc-acm">http://mailman.mit.edu/mailman/listinfo/gbc-acm</a> or
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