BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Sabre//Sabre VObject 4.6.0//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Europe/Zurich
X-LIC-LOCATION:Europe/Zurich
TZURL:http://tzurl.org/zoneinfo/Europe/Zurich
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19810329T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19961027T030000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:news1852@dmi.unibas.ch
DTSTAMP;TZID=Europe/Zurich:20250430T144958
DTSTART;TZID=Europe/Zurich:20250401T161500
SUMMARY:Bringing Database Systems into Machine Learning Workflows
DESCRIPTION:Machine learning and database systems are two foundational conc
 epts in modern data science. Database systems provide efficient and reliab
 le methods for storing\, managing\, and analyzing large-scale data\, while
  machine learning techniques enable the extraction of valuable insights fr
 om this data.  In this talk\, we will demonstrate the benefits of integra
 ting database systems into machine learning workflows\, using PyDuckPGQ as
  an example. PyDuckPGQ simplifies the retrieval of relational data as grap
 h objects that can be used in machine learning workflows. This integration
  not only bridges the gap between property graph databases and machine lea
 rning frameworks but also opens up new possibilities for data modeling and
  analysis. Evaluation results on social network benchmark datasets of the 
 Linked Data Benchmark Council will be discussed to show the functionality 
 of the integration and the effectiveness of our optimizations.
X-ALT-DESC:<p>Machine learning and database systems are two foundational co
 ncepts in modern data science. Database systems provide efficient and reli
 able methods for storing\, managing\, and analyzing large-scale data\, whi
 le machine learning techniques enable the extraction of valuable insights 
 from this data.&nbsp\;<br /> In this talk\, we will demonstrate the benefi
 ts of integrating database systems into machine learning workflows\, using
  PyDuckPGQ as an example. PyDuckPGQ simplifies the retrieval of relational
  data as graph objects that can be used in machine learning workflows. Thi
 s integration not only bridges the gap between property graph databases an
 d machine learning frameworks but also opens up new possibilities for data
  modeling and analysis.<br /> Evaluation results on social network benchma
 rk datasets of the Linked Data Benchmark Council will be discussed to show
  the functionality of the integration and the effectiveness of our optimiz
 ations.</p>
END:VEVENT
END:VCALENDAR