1 / 10

IT at the IRIS DMC: Synergy with the CIG

IT at the IRIS DMC: Synergy with the CIG. By Tim Ahern, IRIS DMS Program Manager. IRIS DMS - Future Directions. On-line access to all data Enhanced User Services Pre-calculated Data Metrics On-demand Data Metrics Processing Frameworks Networked Data Centers Software development.

fancy
Download Presentation

IT at the IRIS DMC: Synergy with the CIG

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. IT at the IRIS DMC: Synergy with the CIG By Tim Ahern, IRIS DMS Program Manager

  2. IRIS DMS - Future Directions • On-line access to all data • Enhanced User Services • Pre-calculated Data Metrics • On-demand Data Metrics • Processing Frameworks • Networked Data Centers • Software development

  3. Class 1 Data Class 2 Data Class 1or 2 Data BUD Expandable Primary SATA RAID 50 terabyte Tape ftp On-line dataTier 1 and Tier2 Data Electronic Reception of Data 1.2 petabyte capacity Currently 36 terabytes Growing at 10 tbyte/year

  4. Data mining:pre-calculated data metrics

  5. DBMS Tables DMC Data Services QA Parameters Pre-computed estimates Current Pre-computed Data Refinement Services Disk Resources Primary RAID Tier 1 Data User Requests Cluster Computer Return only data meeting pre-computed metrics On-demand Station-Channel-Time Return only data meeting dynamically defined metrics Enhanced Data Selection Data filtering by placing “significant” computational power next to the actual on-line data”

  6. Traditional Data Services Traditional User Requirements: 1. Select events meeting some criteria 2. Select stations with a distance constraint 3. For all broadband channels select ones with a 3 BH channels 4. Select channels with QUACK S/N > something Enhanced Data Requirements: Rotate horizontals Filter each channel through three 1 octave filters Calculate 2d synthetics in each band Perform correlation between synthetic and observed data Return data with correlation > 0.9 to user How enhanced services might be implemented! Rotate 2 D Synthetic BandPass Filter BandPass Filter BandPass Filter BandPass Filter BandPass Filter BandPass Filter Correlation >0.9 Enhanced Data Services Web Services Web Services Web Services Disk Resources Web Services Community Developed Web Services Web Services Cluster Computer

  7. Data Refinement Services Disk Resources Cluster Computer Community Developed IRIS Developed Distributed Web Services Web Services Web Services Web Services Web Services Web Services Web Services Web Services Web Services Web Services Web Services Web Services Web Services Web Services Web Services Web Services Web Services Web Services Web Services Processing Frameworks Opportunity to work with the CIG Based on Web Services or next generation paradigm

  8. DHI Data Center DHI Data Center Network Network Seismogram Seismogram Events Events DHI Data Center DHI Data Center Network Network Seismogram Seismogram Events Events Access to Distributed Data Centers Providing an API to Seismic Data Centers CORBA today. Web Services Tomorrow DHI Clients jWEED IRIS DMC - NCEDC - SCEDC - SCEPP and soon ORFEUS - ISC - NEIC

  9. Clickable access to data centers

  10. Software Development • Web Services Distributed Framework • User developed and shared tools • Providing access to Legacy Applications • Seismic Analysis Code • Get SAC to be Open-Source • Limited to IRIS members at this time • Provide Web-services access to SAC functionality • Tool for seamless access to distributed data • Basic Analysis of data with datasets such as USArray in mind

More Related