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1
Automating Semantic Metadata
Collection in the Field with
Mobile Application
Laura Kinkead*, Paulo Pinheiro,
Deborah L. ...
Motivation: Next Generation Monitored
Ecosystems
The Jefferson Project (JP) is a joint effort between Rensselaer
Polytechn...
3
Historical Sampling to Sensors, Models, Experiments
• Sampling at 12 locations
• Only water chemistry was previously mea...
4
Science to Inform Solutions
To Realize a truly Smart Lake:
We need an integrative approach to
understanding lake stresso...
5
Traditional Data Collection
Notes
Notes taken in
the field with the
use of pen and
paper
Notes are
rarely
attached to
da...
6
Mobile Context Capture for Sensor
Networks (MOCCASN)
COLLECT
METADATA
One single mobile
application capable of
taking fi...
SOLR-CCSVSOLR-CCSV
CCSV-LoaderCCSV-Loader
data
Static
metadata
CSV2CCSV
(ICS)
CSV2CCSV
(ICS)
CCSV-Annotator*CCSV-Annotator...
8
a simple example
Human-Aware Sensor Network Ontology (HASNetO)
9
vstoi:
Detector
vstoi:
Instrument
vstoi:
Platform
hasneto:
Sensing
Perspec...
10
Platform 3952
Instrument 3
D 38 D 94
RFID Type Parent Deployed Location Start Time End Time
3952 Platform NA TRUE 43.1,...
11
Platform 3952
Instrument 5
D 74
Instrument 3
D 38 D 94
RFID Type Parent Deployed Location Start Time End Time
3952 Plat...
12
Platform 3952
Instrument 5
D 74
Instrument 3
D 38 D 94
38 94
3952
74
5
3
RFID Type Parent Deployed Location Start Time ...
13
Platform 3952
Instrument 5
D 74
Instrument 3
D 38 D 94
RFID Type Parent Deployed Location Start Time End Time
3952 Plat...
14
Platform 3952
Instrument 5
D 74
Instrument 3
D 38 D 94
RFID Type Parent Deployed Location Start Time End Time
3952 Plat...
15
a more complicated
example – taking an
instrument out of
service and adding
instruments
16
RFID Type Parent Deployed Location Start Time End Time
9754 Platform NA TRUE 43.2,
-73.1
2014-10-
01T11:00
NA
8 Instrum...
17
Platform 9754
Instrument 2
D 61
Instrument 6
D 09
RFID Type Parent Deployed Location Start Time End Time
9754 Platform ...
18
Platform 9754
Instrument 2
D 61
Instrument 6
D 09
09 61
9754
6
2
RFID Type Parent Deployed Location Start Time End Time...
19
Platform 9754
Instrument 2
D 61
Instrument 6
D 09
09
61
6
2
Does Detector 09
belong to Instrument
2?
Yes No
RFID Type P...
20
RFID Type Parent Deployed Location Start Time End Time
9754 Platform NA FALSE 43.2, -73.1 2014-10-
01T11:00
2014-10-
27...
21
RFID Type Parent Deployed Location Start
Time
End Time
9754 Platform NA FALSE 43.2,
-73.1
2014-10-
01T11:00
2014-10-
27...
22
Conclusion
• Automated Metadata capture can enable current and next generation
sensor-based science by enabling ubiquit...
Extras
23
24
25
Recognized Challenges
• What do you do when there’s no cell
service?
• How do you make sure the instruments on
the boat...
26
Intelligent Deployment of
Sensor Networks
• Automates the collection of metadata
‣ faster
‣ harder to forget to do
‣ le...
The Human-Aware Sensor Network Ontology
27
Science to Inform Solutions
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Automating Semantic Metadata Collection in the Field with Mobile Application

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Presentation at Mobile Deployment of Semantic Technologies Workshop at the International Semantic Web Conference. Abstract: In the past few decades, the field of ecology has grown from a collection of disparate researchers who collected data on their local phenomenon by hand, to large ecosystems-oriented projects partially fueled by automated sensor networks and a diversity of models and experiments. These modern projects rely on sharing and integrating data to answer questions of increasing scale and complexity. Interpreting and sharing the big data sets generated by these projects relies on information about how the data was collected and what the data is about, typically stored as metadata. Metadata ensures that the data can be interpreted and shared accurately and efficiently. Traditional paper-based metadata collection methods are slow, error-prone, and non-standardized, making data sharing difficult and inefficient. Semantic technologies offer opportunities for better data management in ecology, but also may pose a challenging learning curve to already busy researchers. This paper presents a mobile application for recording semantic metadata about sensor network deployments and experimental settings in real time, in the field, and without expecting prior knowledge of semantics from the users. This application enables more efficient and less error-prone in-situ metadata collection, and generates structured and shareable metadata.

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Automating Semantic Metadata Collection in the Field with Mobile Application

  1. 1. 1 Automating Semantic Metadata Collection in the Field with Mobile Application Laura Kinkead*, Paulo Pinheiro, Deborah L. McGuinness Tetherless World Constellation Rensselaer Polytechnic Institute * Now at Athena Health
  2. 2. Motivation: Next Generation Monitored Ecosystems The Jefferson Project (JP) is a joint effort between Rensselaer Polytechnic Institute (RPI), IBM and the Fund for Lake George aimed at creating an instrumented water ecosystem along with an appropriate cyberinfrastructure that can serve as a global model for ecosystem monitoring, exploration, understanding, and prediction.
  3. 3. 3 Historical Sampling to Sensors, Models, Experiments • Sampling at 12 locations • Only water chemistry was previously measured • Key previous results:  Salt levels increasing – now dominant in the lake  Chlorophyll slowly increasing  Hypoxia in Caldwell Basin changed little • Key resulting hypotheses:  Increasing salt levels and organic nutrients may favor dominance of cyanobacteria in the phytoplankton  Ca levels may limit spread of invasive zebra mussels  Chlorophyll increase may be caused by nutrient loading  Food web mostly driven by “bottom-up” factors (i.e. nutrients, growing season length) Moving to sensors, streaming data, and a smarter, instrumented lake with the goal of providing a foundation to form and evaluate hypotheses much more effectively enabling a new generation of strategic science dedicated to fuller understanding of the Lake's ecological health.
  4. 4. 4 Science to Inform Solutions To Realize a truly Smart Lake: We need an integrative approach to understanding lake stressors, identifying correlations, hypothesizing causation, experimentally testing hypotheses, and proposing actions Science-based Solutions: Leveraging deep understanding of multiple communities and their research content to propose solutions along with evidence informs Cyberinfrastructure/Data Platform/Viz Lab Semantic Data Model Current focus has been on observations &sensor networks
  5. 5. 5 Traditional Data Collection Notes Notes taken in the field with the use of pen and paper Notes are rarely attached to data There is no community- wide consensus on how to take and reuse field notes
  6. 6. 6 Mobile Context Capture for Sensor Networks (MOCCASN) COLLECT METADATA One single mobile application capable of taking field notes and connect the notes to data as semantic annotations
  7. 7. SOLR-CCSVSOLR-CCSV CCSV-LoaderCCSV-Loader data Static metadata CSV2CCSV (ICS) CSV2CCSV (ICS) CCSV-Annotator*CCSV-Annotator* MOCASSNMOCASSN HASNetO-LoaderHASNetO-Loader Dynamic metadata Sensor network technician scientist data user (incl. scientists) maintains reports human Interventions (deployments, sensor config, calibrations) Single instrument data (csv) ccsv data (csv) Spreadsheet of static metadata ccsv static metadata turtle SPARQL and Lucene queries CCSV BrowserCCSV Browser SPARQL and Lucene queries Faceted search annotated csv Dynamic metadata IN-SITU DATA-SITE DATA-SITE WWW uses reports needs Dynamic metadata Ontologies (HASnetO, OBOE, PROV, VSTO) * Tool to be developed metadata metadata mainly data flow mainly metadata flow
  8. 8. 8 a simple example
  9. 9. Human-Aware Sensor Network Ontology (HASNetO) 9 vstoi: Detector vstoi: Instrument vstoi: Platform hasneto: Sensing Perspective oboe: Characteristic oboe: Entity vstoi: Detachable Detector vstoi: Attached Detector * * * 1 0..1 * hasPerspective Characteristic perspectiveOf prov: Activity hasneto: DataCollection vstoi: Deployment xsd:dateTime xsd:dateTime hasData Collection 1* prov: Agent wasAssociatedWith startedAtTime endedAtTime 1 1 * * * * oboe: Measurement of-characteristic hasneto: hasMeasurement 1 1 * * Platform 3952 Instrument 3 D 38 D 94
  10. 10. 10 Platform 3952 Instrument 3 D 38 D 94 RFID Type Parent Deployed Location Start Time End Time 3952 Platform NA TRUE 43.1, -73.2 2014-10- 27T12:00 NA 3 Instrument 3952 TRUE 43.1, -73.2 2014-10- 27T12:00 NA 38 Detector 3 TRUE 43.1, -73.2 2014-10- 27T12:00 NA 94 Detector 3 TRUE 43.1, -73.2 2014-10- 27T12:00 NA Example Knowledge Base
  11. 11. 11 Platform 3952 Instrument 5 D 74 Instrument 3 D 38 D 94 RFID Type Parent Deployed Location Start Time End Time 3952 Platform NA TRUE 43.1, -73.2 2014-10- 27T12:00 NA 3 Instrument 3952 TRUE 43.1, -73.2 2014-10- 27T12:00 NA 38 Detector 3 TRUE 43.1, -73.2 2014-10- 27T12:00 NA 94 Detector 3 TRUE 43.1, -73.2 2014-10- 27T12:00 NA Example Knowledge Base New instrument deployment
  12. 12. 12 Platform 3952 Instrument 5 D 74 Instrument 3 D 38 D 94 38 94 3952 74 5 3 RFID Type Parent Deployed Location Start Time End Time 3952 Platform NA TRUE 43.1, -73.2 2014-10- 27T12:00 NA 3 Instrument 3952 TRUE 43.1, -73.2 2014-10- 27T12:00 NA 38 Detector 3 TRUE 43.1, -73.2 2014-10- 27T12:00 NA 94 Detector 3 TRUE 43.1, -73.2 2014-10- 27T12:00 NA Example Knowledge Base
  13. 13. 13 Platform 3952 Instrument 5 D 74 Instrument 3 D 38 D 94 RFID Type Parent Deployed Location Start Time End Time 3952 Platform NA TRUE 43.1, -73.2 2014-10- 27T12:00 NA 3 Instrument 3952 TRUE 43.1, -73.2 2014-10- 27T12:00 NA 38 Detector 3 TRUE 43.1, -73.2 2014-10- 27T12:00 NA 94 Detector 3 TRUE 43.1, -73.2 2014-10- 27T12:00 NA 5 Instrument 3952 TRUE 43.1, -73.2 2014-10- 27T16:30 NA 74 Detector 5 TRUE 43.1, -73.2 2014-10- 27T16:30 NA Example Knowledge Base
  14. 14. 14 Platform 3952 Instrument 5 D 74 Instrument 3 D 38 D 94 RFID Type Parent Deployed Location Start Time End Time 3952 Platform NA TRUE 43.1, -73.2 2014-10- 27T12:00 NA 3 Instrument 3952 TRUE 43.1, -73.2 2014-10- 27T12:00 NA 38 Detector 3 TRUE 43.1, -73.2 2014-10- 27T12:00 NA 94 Detector 3 TRUE 43.1, -73.2 2014-10- 27T12:00 NA 5 Instrument 3952 TRUE 43.1, -73.2 2014-10- 27T16:30 NA 74 Detector 5 TRUE 43.1, -73.2 2014-10- 27T16:30 NA Example Knowledge Base
  15. 15. 15 a more complicated example – taking an instrument out of service and adding instruments
  16. 16. 16 RFID Type Parent Deployed Location Start Time End Time 9754 Platform NA TRUE 43.2, -73.1 2014-10- 01T11:00 NA 8 Instrument 9754 TRUE 43.2, -73.1 2014-10- 01T11:00 NA 43 Detector 8 TRUE 43.2, -73.1 2014-10- 01T11:00 NA Platform 9754 Instrument 8 D 43 Example Knowledge Base
  17. 17. 17 Platform 9754 Instrument 2 D 61 Instrument 6 D 09 RFID Type Parent Deployed Location Start Time End Time 9754 Platform NA TRUE 43.2, -73.1 2014-10- 01T11:00 NA 8 Instrument 9754 TRUE 43.2, -73.1 2014-10- 01T11:00 NA 43 Detector 8 TRUE 43.2, -73.1 2014-10- 01T11:00 NA Example Knowledge Base Undeploy one instrument (8) (with one detector(43)) and deploy 2 new instruments (each with a detector)
  18. 18. 18 Platform 9754 Instrument 2 D 61 Instrument 6 D 09 09 61 9754 6 2 RFID Type Parent Deployed Location Start Time End Time 9754 Platform NA TRUE 43.2, -73.1 2014-10- 01T11:00 NA 8 Instrument 9754 TRUE 43.2, -73.1 2014-10- 01T11:00 NA 43 Detector 8 TRUE 43.2, -73.1 2014-10- 01T11:00 NA Example Knowledge Base
  19. 19. 19 Platform 9754 Instrument 2 D 61 Instrument 6 D 09 09 61 6 2 Does Detector 09 belong to Instrument 2? Yes No RFID Type Parent Deployed Location Start Time End Time 9754 Platform NA TRUE 43.2, -73.1 2014-10- 01T11:00 NA 8 Instrument 9754 TRUE 43.2, -73.1 2014-10- 01T11:00 NA 43 Detector 8 TRUE 43.2, -73.1 2014-10- 01T11:00 NA Example Knowledge Base
  20. 20. 20 RFID Type Parent Deployed Location Start Time End Time 9754 Platform NA FALSE 43.2, -73.1 2014-10- 01T11:00 2014-10- 27T17:00 8 Instrument 9754 FALSE 43.2, -73.1 2014-10- 01T11:00 2014-10- 27T17:00 43 Detector 8 FALSE 43.2, -73.1 2014-10- 01T11:00 2014-10- 27T17:00 9754 Platform NA TRUE 43.2, -73.1 2014-10- 27T17:00 NA 2 Instrument 9754 TRUE 43.2, -73.1 2014-10- 27T17:00 NA 6 Instrument 9754 TRUE 43.2, -73.1 2014-10- 27T17:00 NA 61 Detector 2 TRUE 43.2, -73.1 2014-10- 27T17:00 NA 9 Detector 6 TRUE 43.2, -73.1 2014-10- 27T17:00 NA Platform 9754 Instrument 2 D 61 Instrument 6 D 09 Does Detector 09 belong to Instrument 2? Yes No Example Knowledge Base
  21. 21. 21 RFID Type Parent Deployed Location Start Time End Time 9754 Platform NA FALSE 43.2, -73.1 2014-10- 01T11:00 2014-10- 27T17:00 8 Instrument 9754 FALSE 43.2, -73.1 2014-10- 01T11:00 2014-10- 27T17:00 43 Detector 8 FALSE 43.2, -73.1 2014-10- 01T11:00 2014-10- 27T17:00 9754 Platform NA TRUE 43.2, -73.1 2014-10- 27T17:00 NA 2 Instrument 9754 TRUE 43.2, -73.1 2014-10- 27T17:00 NA 6 Instrument 9754 TRUE 43.2, -73.1 2014-10- 27T17:00 NA 61 Detector 2 TRUE 43.2, -73.1 2014-10- 27T17:00 NA 9 Detector 6 TRUE 43.2, -73.1 2014-10- 27T17:00 NA Platform 9754 Instrument 2 D 61 Instrument 6 D 09 Example Knowledge Base Automatic update from answering one simple question: lightweight use of semantics
  22. 22. 22 Conclusion • Automated Metadata capture can enable current and next generation sensor-based science by enabling ubiquitous capture of contextual information – helps eliminate forgetting to annotate • Mobile technology should and can enable contextual capture even without connectivity • Relatively light weight semantics can significantly • Improve deployment quality by using semantic constraints to check for inconsistencies and help identify / resolve ambiguities • Enable integration • Enable discovery Questions? Interested in collaborating? dlm@cs.rpi.edu pinhep@rpi.edu
  23. 23. Extras 23
  24. 24. 24
  25. 25. 25 Recognized Challenges • What do you do when there’s no cell service? • How do you make sure the instruments on the boat are excluded?
  26. 26. 26 Intelligent Deployment of Sensor Networks • Automates the collection of metadata ‣ faster ‣ harder to forget to do ‣ less error-prone
  27. 27. The Human-Aware Sensor Network Ontology 27
  28. 28. Science to Inform Solutions

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