Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Enterprise Multimedia Integration and Search


Published on

playence presentation called "Enterprise Multimedia Integration and Search" for the Future Enterprise Systems Workshop, held jointly with the Future Internet Symposium in Berlin, Germany, 20th September 2010

  • Be the first to comment

  • Be the first to like this

Enterprise Multimedia Integration and Search

  1. 1. Enterprise  Mul-media  Integra-on  and  Search   Future  Enterprise  Systems   September  20th,  2010   Ozelin  López,  Katharina  Siorpaes  
  2. 2. MoAvaAon   •  With  increasing  bandwidth,  cheaper  storage  of  data,   and  improved  hardware,  mulAmedia  content  is  gaining   importance.   •  40%  of  worldwide  traffic  in  Internet  in  2010  will  be   consumed  by  viewing  and/or  downloading  videos   •  In  2014,  the  sum  of  all  video  forms  will  consume  90%   of  traffic   Source:  Cisco   20  sepAembre  2010   2  
  3. 3. MoAvaAon   •  As  rich  medium,  video  can  transport  and  conserve   more  informaAon  than  text  ever  could.     •  This  type  of  content  also  creates  new  issues  with   respect  to  search,  integraAon,  management  and   preservaAon   •  A  new  challenge  arises,  when  trying  to  integrate  text   with  mulAmedia   20  sepAembre  2010   3  
  4. 4. MoAvaAon   •  So  far,  only  small  parts  of  data  comes  out  of   automaAc  analysis  on  mulAmedia  assets   •  The  work  is  finally   done,  mostly,  by   humans.   •  MulAmedia   informaAon  is   isolated   20  sepAembre  2010   4  
  5. 5. MulAmedia  HolisAc  View  in  the  Enterprise   •  Knowledge  AcquisiAon  process  is  criAcal  to  any   semanAcally  enhanced  system   •  Current  informaAon  systems  can  only  rely  on   weak  annotaAon  processes  for  mulAmedia  assets   •  When  needed,  manual  annotaAon  tagging  is   performed  with  the  help  of  shared  vocabularies   and  thesauri.   •  The  level  of  integraAon  with  exisAng  InformaAon   Systems  is  limited   20  sepAembre  2010   5  
  6. 6. MulAmedia  HolisAc  View  in  the  Enterprise   •   Interlinking   •   AnnotaAon   •   IntegraAon   •   Search   •   Management   20  sepAembre  2010   6  
  7. 7. AnnotaAon  Process   •  The  annotaAon  process  can  be  done   automaAcally  by  the  system  or  manually.  The   accuracy  and  precision  of  this  process  depends   on  the  type  of  content.   •  Textual  annotaAon  is  done  automaAcally,  using   Ontologies  and  NLP  techniques  to  pinpoint   textual  references  to  concepts  and  instances  in   the  source.   •  Several  domain  ontologies  can  be  used  to  this   purpose,  providing  a  mulA-­‐view  perspecAve  on   the  same  resource.   20  sepAembre  2010   7  
  8. 8. AnnotaAon  Process   •  In  the  case  of  video  or  audio,  automaAc  analysis   has  less  accuracy.   •  ASR  can  done  the  work  up  to  some  extent   (60-­‐80%)   •  For  video  or  image  analysis,  high-­‐level  features   can  be  obtained,  like  face  detecAon,  detecAon  of   objects,  daylight  classificaAon  and  other  basic   features.   •  In  these  cases,  collaboraAon  human  annotaAon   must  be  supported.   20  sepAembre  2010   8  
  9. 9. AnnotaAon  Process   •  In  the  case  of  video  or  audio,  automaAc  analysis   has  less  accuracy.   •  ASR  can  done  the  work  up  to  some  extent   (60-­‐80%)   •  For  video  or  image  analysis,  high-­‐level  features   can  be  obtained,  like  face  detecAon,  detecAon  of   objects,  daylight  classificaAon  and  other  basic   features.   •  In  these  cases,  collaboraAon  human  annotaAon   must  be  supported.   20  sepAembre  2010   9  
  10. 10. IntegraAon   •  AnnotaAon  process  will  leave  a  set  of  resources   linked  to  the  same  semanAc  content   •  Data  can  easily  be  located,  mashed-­‐up  and   displayed  regardless  its  original  source.   •  IntegraAon  in  playence  Media  empowers  the  user   to  locate  a  meeAng  recording  and  being  able  to   find  related  videos,  audios,  pictures  and   documents.   •  Once  annotated,  everything  can  be  queried  using   the  same  model.   20  sepAembre  2010   10  
  11. 11. IntegraAon   20  sepAembre  2010   11  
  12. 12. Search   •  playence  Media  performs  semanAc  search,  using   annotaAons  and  applying  faceted  search  and  semanAc   navigaAon  to  narrow  the  set  of  results   •  When  searching,  playence  Media  makes  use  of  Natural   Language  Processing  techniques  like  lemmaAzaAon  or   spell  check.     •  SemanAc  features  are  used  in  query  expansion,  like   synonym  expansion  through  SKOS,  or  generalizaAon-­‐ specializaAon  expansion,  using  the  “is-­‐a”  relaAonship   and  instances  from  concepts  involved,  or  using  more   complex  relaAons  in  query  expansion.   20  sepAembre  2010   12  
  13. 13. Search   20  sepAembre  2010   13  
  14. 14. playence  Media       Intelligent  mulAmedia  management  including:   automaAc  semanAc  annotaAon,   interlinking,  in-­‐video  search  and  browsing.     •  Automa-c  mul--­‐language  knowledge  extrac-on   –  LocaAon  of  relevant  key-­‐words  and  domain   knowledge  through  batch  ASR.     –  Efficient  video  annotaAon  for  later  use.   •  In-­‐video  Search     –  Land  a  search  at  the  exact  second  where  the   relevant  content  is  being  played.   –  Land  a  search  at  the  exact  second  where  the   relevant  actor  is  talking.   •  Mul-linguality     –  Support  for  a  wide  variety  of  languages.   –  Complex  cross-­‐language  query  and  mulAmedia   asset  retrieval.     •  Mul-format  support   –  AVI,  MPEG,  FLV,  etc.   20  sepAembre  2010   14  
  15. 15. playence  Media   •  Dynamic  ra-ng   –  On-­‐the-­‐go  signalling  on  the  most  interesAng   porAons  of  a  rich  media  asset.   –  Beher  locaAon  of  releant  content  and  improve   search  results.   •  Manual  annota-on  refinement   –  Refinement  of  automaAc  annotaAons.   –  AddiAon  of  annotaAons.   •  Browsing  and  asset  naviga-on     –  Powerful  browsing  engine  to  keep  sight  of   huge  amounts  of  media  content.     –  Eases  finding,  discovering  and  accessing  the   required  media  resources.   •  Rela-onship  viewer   –  Snapshot  view  of  a  porAon  of  the  subjacent   data  model.     –  Document  set  filtering  by  way  of  query   expansion  and  reducAon.   20  sepAembre  2010   15  
  16. 16. Further  Steps   •  The  challenges  associated  with  mulAmedia   integraAon  in  the  enterprise  are  manifold   •  Relevance  and  in-­‐video  search   •  Ontology  evoluAon  from  customer  perspecAve   •  Workflow  and  collaboraAve  processes  are   needed.   •  Interlinking   –  Linked  Open  Data   –  Linked  Closed  Data   20  sepAembre  2010   16  
  17. 17. Conclusion   •  Media  is  going  to  be  the  next  enterprise   communicaAon  mechanism   •  Media  is  a  bitch!   –  We  need  completely  new  ways  of  manage  it   –  Business  is  beyond  text,  but  technology  is  not   •  Providing  a  holisAc  view  empowers  enterprises  to   manage  mulAmedia  assets   •  This  holisAc  view  comprises  heavily  informaAon  related   processes:  annotaAon,  integraAon,  search,  interlinking   •  playence  Media  comes  to  the  playground  to  help   companies  dealing  with  these  challenges.   20  sepAembre  2010   17  
  18. 18. QuesAons  ?   We  understand  and   integrate  your  media   content.