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Page 1: Design and implementation of a system for the improved searching and accessing of real-world SOS services

Design  and  implementa.on  of  a  system  for  the  improved  searching  and  

accessing  of  real-­‐world  SOS  services    

Ben  Knoechel  ([email protected])  Chih-­‐Yuan  Huang  ([email protected])  Steve  Liang  ([email protected])  

University  of  Calgary    

Page 2: Design and implementation of a system for the improved searching and accessing of real-world SOS services

Outline  

•  Introduc.on  •  Methodology  •  Experimental  Results  •  Conclusions  

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Introduc.on  

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Introduc.on  

•  The  Open  Geospa.al  Consor.um  (OGC)  has  developed  the  Sensor  Web  Enablement  (SWE)  

•  SWE  is  a  set  of  standards  to  allow  people  to  share  and  interact  with  sensor  data  over  the  Internet  

•  The  Sensor  Observa.on  Service  (SOS)  is  a  standard  for  sharing  data  collected  by  sensors  – SOS  will  refer  to  version  1.0.0  

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Sensor  Observa.on  Service  

•  SOS  defines  three  core  opera.ons  – GetCapabili.es  – DescribeSensor  – GetObserva.on  

•  A  GetObserva.on  request  must  define  an  observa.on  offering  and  at  least  one  observed  property  

•  Addi.onal  filters  include  spa.al-­‐temporal  bounding  box,  feature  of  interest  

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Phenomenon  Layer  

•  A  SOS  service  may  provide  varied  data  •  We  define  a  phenomenon  layer  to  refer  to  data  specific  to  a  single  observa.on  offering  and  observed  property  

•  A  phenomenon  layer  can  be  used  to  generate  more  specific  data  requests  to  a  SOS  service  

•  This  defini.on  is  specific  to  our  group  

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Enhancing  SOS  

•  SOS  accomplishes  the  goal  of  transferring  sensor  data  

•  We  can  extend  the  u.lity  of  SOS  by  designing  a  system  to  collect  data  from  mul.ple  SOS  services  

•  Our  team’s  use  case:  – To  display  all  the  sensors  measuring  the  same  phenomenon  from  all  SOS  services  

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Problems  

•  A  number  of  problems  were  encountered  when  developing  a  system  for  our  use  case  

1.  Large  variety  of  the  observed  property  URI  represen.ng  the  same  phenomenon  

2.  Sensor-­‐centric  SOS  services  3.  Non-­‐compliant  SOS  services  

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Different  Observed  Property  URIs  For  Wind  Speed  

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urn:x-­‐ogc:def:property:OGC::WindSpeed

urn:ogc:def:property:universityofsaskatchewan:ip3:windspeed

urn:ogc:def:phenomenon:OGC:1.0.30:windspeed

urn:ogc:def:phenomenon:OGC:1.0.30:WindSpeeds

urn:ogc:def:phenomenon:OGC:windspeed

urn:ogc:def:property:geocens:geocensv01:windspeed

urn:ogc:def:property:noaa:ndbc:Wind  Speed

urn:ogc:def:property:OGC::WindSpeed

urn:ogc:def:property:ucberkeley:odm:Wind  Speed  Avg  MS

urn:ogc:def:property:ucberkeley:odm:Wind  Speed  Max  MS hdp://ws.sensordatabus.org/Ows/Swe.svc/?service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed hdp://marinemetadata.org/cf#wind_speed

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An  Example  of  a  Sensor-­‐Centric  SOS  Service  

 Observation Offering Observed Property

offering_44040 http://ws.sensordatabus.org/Ows/Swe.svc/?service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed

offering_nos.8411250.WL http://ws.sensordatabus.org/Ows/Swe.svc/?service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed

offering_pwaw3 http://ws.sensordatabus.org/Ows/Swe.svc/?service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed

offering_mqtt2 http://ws.sensordatabus.org/Ows/Swe.svc/?service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed

offering_nws.SBCP.metar http://ws.sensordatabus.org/Ows/Swe.svc/?service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed

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Proposed  Solu.on  

•  We  propose  a  logical  layer  service  to  manage  heterogeneous  observed  property  URIs  

•  We  propose  a  transla8on  engine  to  provide  efficient  communica.on  with  SOS  services  

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Contribu.on  

•  Our  system  has  three  contribu.ons  1.  Summarizes  and  inves.gates  the  current  status  

of  SOS  services  online  today  2.  Highlights  issues  with  developing  a  system  to  

address  a  common  use  case  3.  Presents  a  system  architecture  for  handling  

these  solu.ons  

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Related  Work  

•  The  OGC  Catalogue  Service  provides  informa.on  about  available  OGC  services  

•  Not  all  SOS  services  are  registered  in  a  Catalogue  Service  

•  Also,  the  system  must  also  account  for  communica.ng  with  mul.ple  SOS  services  

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Related  Work  

•  A  search  engine  is  commonly  used  for  answering  advanced  queries  

•  This  is  a  difficult  process  due  to  varying  URNs  of  observed  proper.es  

•  This  also  doesn’t  account  for  managing  communica.on  with  mul.ple  SOS  services  

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Methodology  

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Overview  

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Logical  Layer  Service  

•  Manages  and  maps  the  logical  concept  of  an  observed  property  to  corresponding  phenomenon  layers  

•  We  use  a  dic.onary  to  manage  our  list  of  observed  proper.es  

•  The  dic.onary  is  manually  created  by  a  data  processing  team  that  interprets  sensor  data  by  phenomenon  types  

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Logical  Layer  Service  

•  Dic.onary  terms  are  managed  by  hierarchies  to  facilitate  user  searches  

•  The  mapping  from  dic.onary  terms  to  phenomenon  layers  is  also  done  manually  

•  The  textual  informa.on  of  the  observa.on  offering  and  observed  property  URI  are  interpreted  manually  and  mapped  to  corresponding  dic.onary  terms  

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Logical  Layer  Service  

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Earth  

Atmosphere   Hydro  

Air   Soil  

.de  level  water  temp  soil  moisture  soil  temp  air  temp  

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Hierarchy   Dic.onary   Phenomenon  Layer  

(1)  hdp://app.geocens.ca:8171/sos,  Temperature,  urn:ogc:def:property:noaa:ndbc:Air  Temperature  

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Transla.on  Engine  

•  The  transla.on  engine  has  four  components  1.  Controller  2.  SOS  Connector  3.  Feeder  4.  Cache  Database  

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Transla.on  Engine  

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Client  

•  Web  based  front  end  •  Users  select  a  hierarchy,  and  use  the  hierarchy  to  select  an  observed  property  

•  The  client  automa.cally  loads  all  data  for  that  observed  property  from  all  available  SOS  services  

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Client  

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Experimental  Results  

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Data  Mapping  

•  Data  mapping  is  performed  manually,  we  will  not  focus  on  valida.ng  correctness  of  mapping  

•  Instead,  we  will  summarize  the  overall  mappings  

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Data  Mapping  

•  The  dic.onary  defines  76  observed  proper.es  •  The  mapping  defines  phenomenon  layers,  so  the  user  does  not  need  to  create  mul.ple  requests  to  mul.ple  SOS  services  

•  For  example,  there  are  369  wind  speed  phenomenon  layers,  across  7  different  SOS  services  

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Top  Ten  Observed  Proper.es  with  Highest  Number  of  Mappings  

 Observed Property URN Number of Mappings Number of Services

horizontal_wind_speed 402 2

wind_direction 369 7

wind_speed 55 12

dew_point_temperature 31 4

min_air_temperature 25 14

air_temperature 25 14

max_air_temperature 20 10

dry_bulb_temperature 19 9

mean_air_temperature 18 9

atmospheric_pressure 18 14

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Number  of  instances  in  the  real-­‐world  SOS  services  and  the  logical  layer  service  

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Data  Access  

•  To  evaluate  the  performance  of  data  access,  we  used  our  system  approach  versus  the  naïve  approach  

•  We  downloaded  recent  observa.on  data  from  sensors  

•  Tes.ng  was  performed  on  Acer  Aspire  M3910  with  Intel®  Core  ™  i7-­‐870  @  2.93GHz  and  4  gigabyte  DIMM  Synchronous  1333  MHz  

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Data  loading  latencies  by  using  naïve  solu.on  and  proposed  scheme  

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Conclusions  

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Conclusions  

•  A  logical  layer  service  and  transla.on  engine  are  proposed  to  solve  data  access  to  mul.ple,  heterogeneous  SOS  services  

•  The  data  mapping  aggregates  similar  phenomenon  layers  

•  The  proposed  data  access  scheme  is  efficient  when  compared  to  a  naïve  approach  

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Future  Work  

•  The  work  here  can  be  extended  by  inves.ga.ng  automa.c  and  semi-­‐automa.c  methods  of  mapping,  dic.onary  genera.on,  and  informa.on  retrieval  

•  We  can  leverage  recent  research  in  the  seman.c  web  and  data  mining  for  opening  the  sensor  web  to  end  users  for  data  browsing  

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Acknowledgements  

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