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

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

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Presentation by Ben Knoechel during the Sensor Web System and Visualization paper session of the Sensor Web Enablement workshop (held during the 2011 Cybera Summit).

Presentation by Ben Knoechel during the Sensor Web System and Visualization paper session of the Sensor Web Enablement workshop (held during the 2011 Cybera Summit).

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    Design and implementation of a system for the improved searching and accessing of real-world SOS services Design and implementation of a system for the improved searching and accessing of real-world SOS services Presentation Transcript

    • Design  and  implementa.on  of  a  system   for  the  improved  searching  and   accessing  of  real-­‐world  SOS  services     Ben  Knoechel  (bcknoech@ucalgary.ca)   Chih-­‐Yuan  Huang  (huangcy@ucalgary.ca)   Steve  Liang  (steve.liang@ucalgary.ca)   University  of  Calgary    
    • Outline  •  Introduc.on  •  Methodology  •  Experimental  Results  •  Conclusions   2  
    • Introduc.on   3  
    • 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   4  
    • 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   5  
    • 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   6  
    • 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   7  
    • 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   8  
    • Different  Observed  Property  URIs  For   Wind  Speed  urn:x-­‐ogc:def:property:OGC::WindSpeedurn:ogc:def:property:universityofsaskatchewan:ip3:windspeedurn:ogc:def:phenomenon:OGC:1.0.30:windspeedurn:ogc:def:phenomenon:OGC:1.0.30:WindSpeedsurn:ogc:def:phenomenon:OGC:windspeedurn:ogc:def:property:geocens:geocensv01:windspeedurn:ogc:def:property:noaa:ndbc:Wind  Speedurn:ogc:def:property:OGC::WindSpeedurn:ogc:def:property:ucberkeley:odm:Wind  Speed  Avg  MSurn:ogc:def:property:ucberkeley:odm:Wind  Speed  Max  MShdp://ws.sensordatabus.org/Ows/Swe.svc/?service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speedhdp://marinemetadata.org/cf#wind_speed 9  
    • An  Example  of  a  Sensor-­‐Centric  SOS   Service    Observation Offering Observed Property http://ws.sensordatabus.org/Ows/Swe.svc/? offering_44040 service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed http://ws.sensordatabus.org/Ows/Swe.svc/?offering_nos.8411250.WL service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed http://ws.sensordatabus.org/Ows/Swe.svc/? offering_pwaw3 service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed http://ws.sensordatabus.org/Ows/Swe.svc/? offering_mqtt2 service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed http://ws.sensordatabus.org/Ows/Swe.svc/?offering_nws.SBCP.metar service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed 10  
    • 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   11  
    • 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   12  
    • 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   13  
    • 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   14  
    • Methodology   15  
    • Overview   16  
    • 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   17  
    • 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   18  
    • Logical  Layer  Service   Hierarchy   Dic.onary   Phenomenon  Layer   Earth   .de  level   water  temp  Atmosphere   Hydro   soil  moisture   soil  temp   air  temp   Air   Soil   1(1)  hdp://app.geocens.ca:8171/sos,  Temperature,  urn:ogc:def:property:noaa:ndbc:Air  Temperature   19  
    • Transla.on  Engine  •  The  transla.on  engine  has  four  components   1.  Controller   2.  SOS  Connector   3.  Feeder   4.  Cache  Database   20  
    • Transla.on  Engine   21  
    • 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   22  
    • Client   23  
    • Experimental  Results   24  
    • Data  Mapping  •  Data  mapping  is  performed  manually,  we  will   not  focus  on  valida.ng  correctness  of   mapping  •  Instead,  we  will  summarize  the  overall   mappings   25  
    • 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   26  
    • Top  Ten  Observed  Proper.es  with  Highest  Number  of   Mappings     Observed Property URN Number of Mappings Number of Serviceshorizontal_wind_speed 402 2 wind_direction 369 7 wind_speed 55 12dew_point_temperature 31 4 min_air_temperature 25 14 air_temperature 25 14 max_air_temperature 20 10dry_bulb_temperature 19 9mean_air_temperature 18 9 atmospheric_pressure 18 14 27  
    • Number  of  instances  in  the  real-­‐world  SOS   services  and  the  logical  layer  service   28  
    • 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   29  
    • Data  loading  latencies  by  using  naïve   solu.on  and  proposed  scheme   30  
    • Conclusions   31  
    • 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   32  
    • 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   33  
    • Acknowledgements   34