Design	  and	  implementa.on	  of	  a	  system	      for	  the	  improved	  searching	  and	   accessing	  of	  real-­‐wor...
Outline	  •    Introduc.on	  •    Methodology	  •    Experimental	  Results	  •    Conclusions	                           ...
Introduc.on	                    3	  
Introduc.on	  •  The	  Open	  (OGC)	  has	     developed	  the	  Sensor	  Web	  Enablement	  (SWE)...
Sensor	  Observa.on	  Service	  •  SOS	  defines	  three	  core	  opera.ons	     –	     –  DescribeSensor	 ...
Phenomenon	  Layer	  •  A	  SOS	  service	  may	  provide	  varied	  data	  •  We	  define	  a	  phenomenon	  layer	  to	  ...
Enhancing	  SOS	  •  SOS	  accomplishes	  the	  goal	  of	  transferring	     sensor	  data	  •  We	  can	  extend	  the	 ...
Problems	  •  A	  number	  of	  problems	  were	  encountered	      when	  developing	  a	  system	  for	  our	  use	  cas...
Different	  Observed	  Property	  URIs	  For	                 Wind	  Speed	  urn:x-­‐ogc:def:property:OGC::WindSpeedurn:ogc...
An	  Example	  of	  a	  Sensor-­‐Centric	  SOS	                             Service	                                      ...
Proposed	  Solu.on	  •  We	  propose	  a	  logical	  layer	  service	  to	  manage	     heterogeneous	  observed	  propert...
Contribu.on	  •  Our	  system	  has	  three	  contribu.ons	     1.  Summarizes	  and	  inves.gates	  the	  current	  statu...
Related	  Work	  •  The	  OGC	  Catalogue	  Service	  provides	     informa.on	  about	  available	  OGC	  services	  •  N...
Related	  Work	  •  A	  search	  engine	  is	  commonly	  used	  for	     answering	  advanced	  queries	  •  This	  is	  ...
Methodology	                    15	  
Overview	                 16	  
Logical	  Layer	  Service	  •  Manages	  and	  maps	  the	  logical	  concept	  of	  an	     observed	  property	  to	  co...
Logical	  Layer	  Service	  •  Dic.onary	  terms	  are	  managed	  by	  hierarchies	     to	  facilitate	  user	  searches...
Logical	  Layer	  Service	              Hierarchy	                        Dic.onary	                       Phenomenon	  La...
Transla.on	  Engine	  •  The	  transla.on	  engine	  has	  four	  components	     1.    Controller	     2.    SOS	  Connec...
Transla.on	  Engine	                             21	  
Client	  •  Web	  based	  front	  end	  •  Users	  select	  a	  hierarchy,	  and	  use	  the	  hierarchy	     to	  select	...
Client	               23	  
Experimental	  Results	                                24	  
Data	  Mapping	  •  Data	  mapping	  is	  performed	  manually,	  we	  will	     not	  focus	  on	  correctnes...
Data	  Mapping	  •  The	  dic.onary	  defines	  76	  observed	  •  The	  mapping	  defines	  phenomenon	  layers...
Top	  Ten	  Observed	  with	  Highest	  Number	  of	                               Mappings	                  ...
Number	  of	  instances	  in	  the	  real-­‐world	  SOS	    services	  and	  the	  logical	  layer	  service	             ...
Data	  Access	  •  To	  evaluate	  the	  performance	  of	  data	  access,	     we	  used	  our	  system	  approach	  vers...
Data	  loading	  latencies	  by	  using	  naïve	    solu.on	  and	  proposed	  scheme	                                    ...
Conclusions	                    31	  
Conclusions	  •  A	  logical	  layer	  service	  and	  transla.on	  engine	     are	  proposed	  to	  solve	  data	  acces...
Future	  Work	  •  The	  work	  here	  can	  be	  extended	  by	  automa.c	  and	  semi-­‐automa.c	     m...
Acknowledgements	                         34	  
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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).

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

  1. 1. Design  and  implementa.on  of  a  system   for  the  improved  searching  and   accessing  of  real-­‐world  SOS  services     Ben  Knoechel  (   Chih-­‐Yuan  Huang  (   Steve  Liang  (   University  of  Calgary    
  2. 2. Outline  •  Introduc.on  •  Methodology  •  Experimental  Results  •  Conclusions   2  
  3. 3. Introduc.on   3  
  4. 4. Introduc.on  •  The  Open  (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  
  5. 5. Sensor  Observa.on  Service  •  SOS  defines  three  core  opera.ons   –   –  DescribeSensor   –  GetObserva.on  •  A  GetObserva.on  request  must  define  an   observa.on  offering  and  at  least  one  observed   property  •  Addi.onal  filters  include­‐temporal   bounding  box,  feature  of  interest   5  
  6. 6. 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  
  7. 7. 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  
  8. 8. Problems  •  A  number  of  problems  were  encountered   when  developing  a  system  for  our  use  case  1.  Large  variety  of  the  observed  property  URI  the  same  phenomenon  2.  Sensor-­‐centric  SOS  services  3.  Non-­‐compliant  SOS  services   8  
  9. 9. 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:// 9  
  10. 10. An  Example  of  a  Sensor-­‐Centric  SOS   Service    Observation Offering Observed Property offering_44040 service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed offering_pwaw3 service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed offering_mqtt2 service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed service=SOS&request=GetResourceByID&ResourceID=urn:renci:sdb:property:1.0.0:Wind%20Speed 10  
  11. 11. 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  
  12. 12. 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  
  13. 13. 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  with  mul.ple  SOS  services   13  
  14. 14. Related  Work  •  A  search  engine  is  commonly  used  for   answering  advanced  queries  •  This  is  a  difficult  process  due  to  varying  URNs   of  observed  •  This  also  doesn’t  account  for  managing   communica.on  with  mul.ple  SOS  services   14  
  15. 15. Methodology   15  
  16. 16. Overview   16  
  17. 17. 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  •  The  dic.onary  is  manually  created  by  a  data   processing  team  that  interprets  sensor  data   by  phenomenon  types   17  
  18. 18. 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  
  19. 19. 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://,  Temperature,  urn:ogc:def:property:noaa:ndbc:Air  Temperature   19  
  20. 20. Transla.on  Engine  •  The  transla.on  engine  has  four  components   1.  Controller   2.  SOS  Connector   3.  Feeder   4.  Cache  Database   20  
  21. 21. Transla.on  Engine   21  
  22. 22. 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  
  23. 23. Client   23  
  24. 24. Experimental  Results   24  
  25. 25. Data  Mapping  •  Data  mapping  is  performed  manually,  we  will   not  focus  on  correctness  of   mapping  •  Instead,  we  will  summarize  the  overall   mappings   25  
  26. 26. Data  Mapping  •  The  dic.onary  defines  76  observed  •  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  
  27. 27. Top  Ten  Observed  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  
  28. 28. Number  of  instances  in  the  real-­‐world  SOS   services  and  the  logical  layer  service   28  
  29. 29. 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  •  was  performed  on  Acer  Aspire  M3910   with  Intel®  Core  ™  i7-­‐870  @  2.93GHz  and  4   gigabyte  DIMM  Synchronous  1333  MHz   29  
  30. 30. Data  loading  latencies  by  using  naïve   solu.on  and  proposed  scheme   30  
  31. 31. Conclusions   31  
  32. 32. 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  
  33. 33. Future  Work  •  The  work  here  can  be  extended  by  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  
  34. 34. Acknowledgements   34  
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