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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/s20140227 Video: https://www.youtube.com/watch?v=hnvHI2e2UA4 (English starts at 2:38). One of the oldest trades.
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Spring 2014: Conferences Review Moscow ACM/SIGMOD ChapterEDBT/ICDT 2014ICDE 2014
My presentation: http://synthesis.ipi.ac.ru/sigmod/seminar/s20140227 Video: https://www.youtube.com/watch?v=hnvHI2e2UA4 (English starts at 2:38) Henrietta Dombrovskaya
One of the oldest trades.. Henrietta Dombrovskaya
What is Enova doing?.. Henrietta Dombrovskaya
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 Henrietta Dombrovskaya
ICDT/EDBT 2014 Athens, Greece March 24-28
Proceedings online http://openproceedings.org/edbticdt2014/EDBT_toc.html Henrietta Dombrovskaya
Talking to the Database in a Semantically Rich Way - A new approach to resolve Object-Relational impedance mismatch Henrietta Dombrovskaya, Richard LeeEnova Chicago IL hdombrovskaya@enova.comrlee@enova.com
My presentation http://www.youtube.com/watch?v=dhG0HuvwPqE Official proceedings: http://openproceedings.org/EDBT/2014/edbticdt2014industrial_submission_16.pdf Henrietta Dombrovskaya
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 Henrietta Dombrovskaya
ICDE 2014 Chicago March 31- April 3 Program information: http://ieee-icde2014.eecs.northwestern.edu/program.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 Henrietta Dombrovskaya
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. Henrietta Dombrovskaya
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; Henrietta Dombrovskaya
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; Henrietta Dombrovskaya