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DTSTART:20380119T111407
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DTSTAMP:20200828T083147Z
UID:BF4D9813-CB0A-4E53-9895-B11C1058A9E0
DTSTART;TZID=Asia/Singapore:20200110T163000
DTEND;TZID=Asia/Singapore:20200110T173000
DESCRIPTION:Despite their immense size\, the multi-trillion-dollar energy c
 ommodity markets lag behind others in the use of big data to drive efficie
 ncy.\n\nHarnessing live vessel tracking data enables modelling of oil flow
 s & local fluctuations of regional tanker availability. In turn\, this can
  be used to predict the impact on the commodity and freight markets.\n\nVo
 rtexa uses billions of satellite & commercial datapoints to track 99% of a
 ll seaborne oil & refined products movements\, globally. Generating comple
 x loading/discharge events and ship-to-ship oil transfers from multiple di
 sparate data feeds in real-time is a significant Data Science and Engineer
 ing challenge. This talk discusses the ML methods used to process live AIS
  (automatic identification system) feeds\, model vessel events\, and gener
 ate real-time destination predictions & diversions. The talk also covers p
 redicting onboard products & grades\, handling contextual anomalies across
  multiple shipbroker/port-agent data sources\, modelling freight availabil
 ity & tanker supply/demand\, and future cargo loading predictions.\n\nThis
  industry application of AI provides transparency into current tanker move
 ments\, and reduces the opacity found in the commodity & freight markets.\
 n\nSpeaker(s): Kit Burgess\, \n\nRoom: S2.2-B2-42\, Bldg: School of EEE\, 
 50 Nanyang Ave\, Nanyang Technological University\, Singapore\, Singapore\
 , Singapore\, 639798
LOCATION:Room: S2.2-B2-42\, Bldg: School of EEE\, 50 Nanyang Ave\, Nanyang 
 Technological University\, Singapore\, Singapore\, Singapore\, 639798
ORGANIZER:JDAUWELS@ntu.edu.sg
SEQUENCE:0
SUMMARY:Real-time algorithmic tracking of global waterborne vessels & commo
 dities
URL;VALUE=URI:https://events.vtools.ieee.org/m/238627
X-ALT-DESC:Description: <br /><p>Despite their immense size\, the multi-tri
 llion-dollar energy commodity markets lag behind others in the use of big 
 data to drive efficiency.</p>\n<p>Harnessing live vessel tracking data ena
 bles modelling of oil flows &amp\; local fluctuations of regional tanker a
 vailability. In turn\, this can be used to predict the impact on the commo
 dity and freight markets.</p>\n<p>Vortexa uses billions of satellite &amp\
 ; commercial datapoints to track 99% of all seaborne oil &amp\; refined pr
 oducts movements\, globally. Generating complex loading/discharge events a
 nd ship-to-ship oil transfers from multiple disparate data feeds in real-t
 ime is a significant Data Science and Engineering challenge. This talk dis
 cusses the ML methods used to process live AIS (automatic identification s
 ystem) feeds\, model vessel events\, and generate real-time destination pr
 edictions &amp\; diversions. The talk also covers predicting onboard produ
 cts &amp\; grades\, handling contextual anomalies across multiple shipbrok
 er/port-agent data sources\, modelling freight availability &amp\; tanker 
 supply/demand\, and future cargo loading predictions.</p>\n<p>This industr
 y application of AI provides transparency into current tanker movements\, 
 and reduces the opacity found in the commodity &amp\; freight markets.</p>
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