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Congrui Li

386 days ago
Unfiled. Edited by leeb5@rpi.edu , anirudh prabhu , Congrui Li 386 days ago
leeb5@rpi.edu eScience July 7, 2016
Patrick W
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Deborah M
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Marshall M 1.  Please join my meeting.
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leeb5@rpi.edu
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Benno Lee
anirudh p anirudh prabhu
 
 
 
 
  • Discussion
 
393 days ago
eScience June 23, 2016
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  • Previous Meeting  (was the last meeting may 26?  or was there another one that should be linked? - benno please update the links)
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Deborah M
  • Time and location: 
 
anirudh p anirudh prabhu
 
  • Notes
 
 
400 days ago
leeb5@rpi.edu eScience June 16, 2016
 
Patrick W
  • General Information
 
Call In
Marshall M 1.  Please join my meeting.
2.  Use your microphone and speakers (VoIP) - a headset is recommended.  Or, call in using your telephone.
Dial +1 (408) 650-3123
Access Code: 333-351-829
Audio PIN: Shown after joining the meeting 
Meeting ID: 333-351-829
 
leeb5@rpi.edu
  • Attendance
Jim M Jim McCusker
Han W Han Wang
leeb5@rpi.edu Marshall Ma
Hao Z Hao Zhong
Peter F Peter Fox (in office working on a proposal)
 
  • Notes
  • Producer vs Consumer on Data Quality
  • Quality control vs quality assessment
  • Control
  • Reflects "happiness" of the data
  • Whether the algorithms converge and so forth
  • Assessment is an analysis "after the fact" 
  • Biased by point of view
  • Facets of Quality
  • Accuracy
  • Completeness
  • Consistency
  • Resolution
  • Ease of Use
  • Latency
  • Have projects that focus on different level of quality
  • What other factors when looking at Facets of Quality?
  • Factors named before lean towards NASA data
  • What factors do we think exist that are domain independent?
  • List of steps to follow independent of domain that will allow us to analyze data quality
  • General method to extend the currently existing facets to begin to apply it to other domains
 
  • Discussion
  • Most current facets are dataset dependent
  • Cannot neccesarily use this generally for projects that use multiple datasets together
  • NASA data tends to be large, does this method still hold for smaller datasets?
  • If it has one and not the other, is the data quality still good?
  • Example: Museum Curator
  • What data quality facet is most important to you give a use case to put wildfires together
  • Current methodology: bundle the metadata with the data so that consumers can make the decision on quality
  • Marshall: Vocabulary quality developed independently and they also have Completeness, Consistency, and Accuracy
  • Spatial and Temporal commonly show up again within the main topics
  • Peter and Stephan: Completeness used to be Coverage, but it usually implies spatial, but not temporal so Completeness shifts towards including both.
  • Genetics, usually don't need spatial, but maybe temporal.  Quality may need to be separate from utility
  • Are the answers there or not?  Finding the quality of the data.
  • Use Case?  Mass Spectroscopy(sp?) - drift over a set of measurements
 
 
 
414 days ago
Congrui L Congrui Li
Hao Z Hao Zhong
Stephan Z Stephan Zednik
  •  
 
Topic: Insider Threat Ontology
 
  • Background
  • Malicious Insider - internal employee that may perform malicious actions
  • Ontology abstracts the idea of threats consistently across organizations
  • Trades expressiveness for inter-operability
  • Implementation
  • Distinguish between Actions (observable) and Events (inferrable)
  • Validation
  • "The insider stole a co-worker's password credentials to log into the system and commit fraud."
  • Start with Natural Language, then use this model to translate into a Semantic structure
 
Discussion
 
  • Difference between Asset and Information?  (Credit Card Asset vs Credit Card Information)
*Money stolen from a credit card is the asset, the card number is the information
  • Authors created semantic model in last slide manually
*Validating the ontology is expressive enough
*Possible Research: Automate NLP to generate these Semantic models
*Linkepedia?  Perhaps a more domain specific domain ontology
*Owl File not available, but forth
 
421 days ago
Deborah M Discussion Topics
  • best practices google doc <get pointer>
  • just navigating around our github content related to this
Han W
  • where are our ontologies?  and related sparql endpoints
  • where are our ontologies?  and related sparql endpoints
 
 
Marshall M 20160526 eScience Meeting 
 
Patrick W
  • General Information
  • This Meeting
  • Previous Meeting
  • Next Meeting
 
Call In
Marshall M 1.  Please join my meeting.
2.  Use your microphone and speakers (VoIP) - a headset is recommended.  Or, call in using your telephone.
Dial +1 (408) 650-3123
Access Code: 333-351-829
Audio PIN: Shown after joining the meeting 
Meeting ID: 333-351-829
 
Patrick W Attendance
Marshall M Marshall Ma
Han W Han Wang
Ian Gross
Jim M Jim McCusker
anirudh p Hao Zhong
Deborah M Zhen Liu 
Sabita Acharya  (will be remote after this week)
Liyu Pan <panl2@rpi.edu>
Rui Yan
 
 
Regrets:
  • Henrique O. Santos <hensantos@gmail.com>   
  • Paulo Pinheiro at RPI <pinhep@rpi.edu>
  • katie chastain (already on list)
Peter F
  • Ahmed (on travel, on list)
  • Sophie Kolankowski (getting married!)
 
Marshall M
  • Propose and discuss topics and questions for the coming eScience meetings
 
anirudh p Location - Place, Event, geographical etc
Ontology Change Template - May not apply for some ontology
Minimum subset of the template that needs to be filled. 
 
Patrick W Action Items
anirudh p
  • Ontology evolution talk based on Linyun's Work
  • Semantics of Quality and Bias Representations
  • Location representations in PROV 
  • Insider's Threat - Rui
 
Deborah M Please add me to the list  (the first 5 people below are all working with deborah this summer)
Patrick W
  • rashidshabbir@gmail.com
anirudh p
  • grossi2@rpi.edu
  • sachar4@uic.edu
Deborah M
  • Zhen Liu <zl1471@nyu.edu>
  • Liyu Pan <panl2@rpi.edu>
 
Patrick W Potential Topics
  • ontology best practices
  • Github Repositories
  • ontology versioning
  • ontology annotation
  • content negotiation and discoverability
Deborah M
  • registering (and advertising) our ontologies
Patrick W
  • easily creating semantic content (ontology editors as well as instance editors)
Peter F
  • Location representations (Marshall and Peter from Vespucci last June)
Marshall M
  • can also relate to work of the 'Spatial Data on the Web' OGC-W3C joint WG
Peter F
  • Location in PROV-O (Marshall and Peter)
Stephan Z
  • ?is this related to the location representations bullet above? Yes.
  • W3C PROV definition of Location: A location can be an identifiable geographic place (ISO 19112), but it can also be a non-geographic place such as a directory, row, or column. As such, there are numerous ways in which location can be expressed, such as by a coordinate, address, landmark, and so forth.
Deborah M
  • Ontology evolution and curation among Big / Broad communities
...

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