Spring 2014: Conferences Review

Moscow ACM/SIGMOD ChapterEDBT/ICDT 2014ICDE 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)

2014 Henrietta Dombrovskaya 3

One of the oldest trades..

2014 Henrietta Dombrovskaya 4

What is Enova doing?..

2014 Henrietta Dombrovskaya 5

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

2014 Henrietta Dombrovskaya 6

ICDT/EDBT 2014

Athens, Greece March 24-28

Proceedings online

http://openproceedings.org/edbticdt2014/EDBT_toc.html

2014 Henrietta Dombrovskaya 8

Talking to the Database in a Semantically Rich Way -

A new approach to resolve Object-Relational impedance mismatch

Henrietta Dombrovskaya, Richard Lee

Enova Chicago IL hdombrovskaya@enova.comrlee@enova.com

My presentationhttp://www.youtube.com/watch?v=dhG0HuvwPqE Official proceedings:http://openproceedings.org/EDBT/2014/edbticdt2014industrial_submission_16.pdf

2014 Henrietta Dombrovskaya 10

Interesting DemosSIAS-V in Action: Snapshot Isolation Append Storage - Vectors on Flash - TU Darmstadt:http://openproceedings.org/EDBT/2014/edbtdemo2014_submission_33.pdfinWalk: Interactive and Thematic Walks Inside the Web of Data – University of Milano:http://openproceedings.org/EDBT/2014/edbtdemo2014_submission_32.pdfAGGREGO SEARCH: Interactive Keyword Query Construction – SEMSOFT, Francehttp://openproceedings.org/EDBT/2014/edbtdemo2014_submission_18.pdf

2014 Henrietta Dombrovskaya 11

ICDE 2014

Chicago March 31- April 3

Program information:http://ieee-icde2014.eecs.northwestern.edu/program.html

Interesting keynotesAnastasia 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

2014 Henrietta Dombrovskaya 13

Interesting keynotesAmit 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.

2014 Henrietta Dombrovskaya 14

Interesting TalksDecorrelation 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) asbeginfloat 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; endQuery: select custkey, service level(custkey) from customer;

2014 Henrietta Dombrovskaya 15

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;

2014 Henrietta Dombrovskaya 16