Semantic eScience Meeting October 05, 2012

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Welcome to TitanPad!

General Meeting Information

  • This Titan Pad
  • Previous Meeting
  • Meeting Page
  • Call-in information
    • 1. Please join my meeting.
    • https://www1.gotomeeting.com/join/776009689
    • eScience Meeting 2012-09-21, 3PM EDT, Winslow 1140
    • 2. Use your microphone and speakers (VoIP) - a headset is recommended. Or, call in using your telephone.
    • Dial +1 (805) 309-0012
    • Access Code:
    • Audio PIN: Shown after joining the meeting
    • Meeting ID: 776-009-689

Agenda

  • Record the presentation, audio only
  • Scribe
  • Yu will present his summer work (scrolling down to 'Presentations' for abstract)
  • AGU 2012 presentation / attendance updates
    • http://tw.rpi.edu/web/event/AGU/FM/2012
      • Who is attending? (Will have to update event RDF)
      • Add event publications
    • Early Registration Deadline: November 2, 2012

Attendance

  • Stephan
  • Jin
  • Han
  • Linyun
  • Patrick
  • Yu
  • Marshall
  • Massimo

Past Action Items

  • All participants to consider when they want to give a 15 mins presentation in the bi-weekyl eScience meetings (~1 per meeting session).

Action Items

Presentations

  • Keep this list from week to week so we know who's presented and who will present
  • Stephan September 21, 2012 - Ontology documentation discussion
  • Yu Chen, October 5, 2012 - Continuous Flow Forcast in Southesk River: Where we are and how we proceed, semantically
    • Streamflow prediction is becoming a vital issue especially in areas where water for irrigation is scarce. During the summer(winter) in CSIRO, we were trying to use the rainfall catchment sensor data to predict the streamflow in a certain area in Tasmania, Australia. A previous domain model has not shown good accuracy in prediction as examined by time series comparison algorithm. Therefore, we proposed a data-driven approach using machine learning techniques that shows good performance, which improves the accuracy from 65% using domain model to the maximum of 98% with the data-driven model. The prediction service will be further encapsulated to a semantic web service that describes itself in rdf. A more ambitious goal is to abstract a general semantic service model that helps scientists to share their method and algorithm semantically.
  • Marshall X Ma, October 19, 2012 - Exploratory visualization of earth science data in a semantic web context
  • Eric Rozell, November 2, 2012 - Resource Discovery for Extreme Scale Collaboration
  • Massimo Di Stefano, November 16, 2012 - IPython Notebook (applied to the ECOOP use case)

Notes

Scriber: Jin

  • Yu presenting his work from summer
    • Continuous Flow Forecasting in the South Esk: Where we are and how to proceed, semantically
    • using Rainfall sensor to predict Streamflow (flooding? other disaster?)
    • current model is not good
    • Yu is using Neural Network model
    • skipped technical details
    • new model gives much better result
    • suggests future work: develop a web service for the work
    • include reasoning using protege, etc.