Transcript Moscow ACM/SIGMOD Chapter

Spring 2014: Conferences
Review
Moscow ACM/SIGMOD Chapter
EDBT/ICDT 2014
ICDE 2014
Moscow ACM/SIGMOD Chapter
My presentation:
http://synthesis.ipi.ac.ru/sigmod/seminar/s2014
0227
Video:
https://www.youtube.com/watch?v=hnvHI2e2U
A4
(English starts at 2:38)
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One of the oldest trades..
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What is Enova doing?..
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Questions asked:
• Did we fully utilize all hardware resources
• How we did the Postgres side of it (record
type, emulation of packages, etc.)
• Code reuse
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ICDT/EDBT 2014
Athens, Greece March 24-28
Proceedings online
http://openproceedings.org/edbticdt2014/EDBT
_toc.html
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Talking to the Database in a
Semantically Rich Way A new approach to resolve ObjectRelational impedance mismatch
Henrietta Dombrovskaya, Richard Lee
Enova
Chicago IL
[email protected]
[email protected]
My presentation
http://www.youtube.com/watch?v=dhG0HuvwP
qE
Official proceedings:
http://openproceedings.org/EDBT/2014/edbticd
t2014industrial_submission_16.pdf
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Interesting Demos
SIAS-V in Action: Snapshot Isolation Append Storage - Vectors on
Flash - TU Darmstadt:
http://openproceedings.org/EDBT/2014/edbtdemo2014_submission_33
.pdf
inWalk: Interactive and Thematic Walks Inside the Web of Data –
University of Milano:
http://openproceedings.org/EDBT/2014/edbtdemo2014_submission_32
.pdf
AGGREGO SEARCH: Interactive Keyword Query Construction –
SEMSOFT, France
http://openproceedings.org/EDBT/2014/edbtdemo2014_submission_18
.pdf
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ICDE 2014
Chicago March 31- April 3
Program information:
http://ieeeicde2014.eecs.northwestern.edu/pr
ogram.html
Interesting keynotes
Anastasia Alamaki. Running with Scissors: Fast Queries on Just-in-time
Databases
Domain scientists collect data much faster than they can be transformed into valuable
information and are often forced into hasty decisions on which parts to discard, potentially
throwing away valuable data before it has been exploited fully. The reason is that query
processing, which is the mechanism to squeeze information out of data, becomes slower
as datasets grow larger.
This talk advocates a departure from the traditional “create a database, then run queries”
paradigm. Instead, data analysts should run queries on raw data, while a database is
built on the side. In fact the database should become an implementation detail,
imperceptible by the user. To achieve this paradigm shift, query processing should be
decoupled from specific data storage formats
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Interesting keynotes
Amit Shet. Transforming Big Data into Smart Data: Deriving Value via
Harnessing Volume, Variety, and Velocity Using Semantic Techniques
and Technologies
The four V’s of Big Data: Volume, Variety, Velocity, and Veracity, and technologies that
handle volume, including storage and computational techniques to support analysis
(Hadoop, NoSQL, MapReduce, etc). However, the most important feature of Big Data,
the raison d'etre, is none of these 4 V’s -- but value. In this talk, I will forward the concept
of Smart Data that is realized by extracting value from a variety of data, and how Smart
Data for growing variety (e.g., social,sensor/IoT, health care) of Big Data enable a much
larger class of applications that can benefit not just large companies but each individual.
This requires organized ways to harness and overcome the four V-challenges.
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Interesting Talks
Decorrelation of User Defined Function Invocations in Queries – Karthik
Ramachandra, et all
Example 1 Query with a scalar UDF
create function service level( int ckey) returns char(10) as
begin
float totalbusiness; string level;
select sum(totalprice) into :totalbusiness
from orders where custkey=:ckey;
if(totalbusiness > 1000000)
level = ‘Platinum’;
else if(totalbusiness > 500000)
level = ‘Gold’;
else level = ‘Regular’;
return level;
end
Query: select custkey, service level(custkey) from customer;
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How he rewrites it
Example 2 Decorrelated Form of Query in Example 1
select c.custkey, case e.totalbusiness > 1000000: ‘Platinum’
case e.totalbusiness > 500000: ‘Gold’
default: ‘Regular’
from customer c left outer join e on c.custkey=e.custkey;
where e stands for the query:
select custkey, sum(totalprice) as totalbusiness
from orders group by custkey;
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