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[ DB Seminar: Friday 2/15, 1-2pm, 606 Soda Hall]

From: elein <elein(at)varlena(dot)com>
To: sfpug(at)postgresql(dot)org
Cc: elein <elein(at)varlena(dot)com>
Subject: [ DB Seminar: Friday 2/15, 1-2pm, 606 Soda Hall]
Date: 2008-02-13 18:48:54
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Lists: sfpug
----- Forwarded message from Alexandra Meliou <ameli(at)cs(dot)berkeley(dot)edu> -----

From: Alexandra Meliou <ameli(at)cs(dot)berkeley(dot)edu>
To: dblunch(at)triplerock(dot)CS(dot)Berkeley(dot)EDU
Subject: DB Seminar: Friday 2/15, 1-2pm, 606 Soda Hall

Friday, February 15th, 2008
606 Soda Hall

Volker Markl, IBM Almaden Research Center

Title: Learning in Query Optimization

Database Systems let users specify queries in a declarative language  
like SQL. Most modern DBMS optimizers rely upon a cost model to choose  
the best query execution plan (QEP) for any given query. Cost  
estimates are heavily dependent upon the optimizer's estimates for the  
number of rows that will result at each step of the QEP for complex  
queries involving many predicates and/or operations. These estimates,  
in turn, rely upon statistics on the database and modeling assumptions  
that may or may not be true for a given database. In the first part of  
our talk, we present research on learning in query optimization that  
we have carried out at the IBM Almaden Research Center. We introduce  
LEO, DB2's LEarning Optimizer, as a comprehensive way to repair  
incorrect statistics and cardinality estimates of a query execution  
plan. By monitoring executed queries, LEO compares the optimizer's  
estimates with actuals at each step in a QEP, and computes adjustments  
to cost estimates and statistics that may be used during the current  
and future query optimizations. LEO introduces a feedback loop to  
query optimization that enhances the available information on the  
database where the most queries have occurred, allowing the optimizer  
to actually learn from its past mistakes.

In the second part of the talk, we describe how the knowledge gleaned  
by LEO is exploited consistently in a query optimizer, by adjusting  
the optimizer's model and by maximizing information entropy. In  
addition, Volker will briefly highlight the DAMIA project and IBM's  
Mashup Starter Kit, his current research focusing on the creation of a  
Data Mashup Fabric for Intranet Applications using Web 2.0 technologies.

Dr. Markl has been working at IBM's Almaden Research Center in San  
Jose,USA since 2001, conducting research in query optimization,  
indexing, and self-managing databases. Volker Markl is spearheading  
the LEO project, an effort on autonomic computing with the goal to  
create a self-tuning optimizer for DB2 UDB. He also is the Almaden  
chair for the IBM Data Management Professional Interest Community (PIC).

From January 1997 to December 2000, Dr. Markl worked for the Bavarian  
Research Center for Knowledge-Based Systems (FORWISS) in Munich,  
Germany as deputy research group manager, leading the MISTRAL and MDA  
projects, thereby cooperating with SAP AG, NEC, Hitachi, Teijin  
Systems Technology, GfK, and Microsoft Research. His MDA project,  
jointly with TransAction Software, developed the relational database  
management system TransBase HyperCube, which was awarded the European  
IST Prize 2001 by EUROCASE and the European Commission.

Dr. Markl also initiated and co-ordinated the EDITH EU IST project  
investigating the physical clustering of multiple hierarchies and its  
applications to GIS and Data Warehousing that now is being carried out  
by FORWISS and several partners from Germany, Italy, Greece, and Poland.

Volker Markl is a graduate of the Technische Universität München,  
where he earned a Masters degree in Computer Science in 1995. He  
completed his PhD in 1999 under the supervision of Rudolf Bayer. His  
dissertation on "Relational Query Processing Using a Multidimensional  
Access Technique" was honored "with distinction" by the German  
Computer Society (Gesellschaft für Informatik). He also earned a  
degree in Business Administration from the University Hagen, Germany  
in 1995. Since 1996, Volker Markl has published more than 30 reviewed  
papers at prestigious scientific conferences and journals, filed more  
than 10 patents and has been invited speaker at many universities and  
companies. Dr. Markl is member of the German Computer Society (GI) as  
well as the Special Interest Group on Management of Data of the  
Assosication for Computing Machinery (ACM SIGMOD). He also serves as  
program committee member and reviewer for several international  
conferences and journals, including SIGMOD, ICDE, VLDB, TKDE, TODS,  
IS, and the Computer Journal. His main research interests are on  
autonomic computing, query processing, and query optimization, but  
also include applications like data warehousing, electronic commerce  
and pervasive computing.

Dr. Markl's earlier professional experience include software engineer  
for a virology laboratory, as part of his military service; lecturer  
for software-engineering courses at the University of Applied Sciences  
in Augsburg, Germany and for programming and communications at the  
Technische Universität München; and consultant for a forwarding  
agency. He was awarded a fellowship by Siemens AG, Munich and also  
worked as an international intern with Benefit Panel Services, Los  

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