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External	
  Schema	
  of	
  	
  
Topic	
  Map	
  Databases	
Keita	
  Nabeta1,	
  Takashi	
  Kojima2,	
  	
  
Yuki	
  Kuribara1,	
  Takashi	
  Yamazaki1,	
  Masaomi	
  Kimura2	
  
1Graduate	
  School	
  of	
  Engineering,	
  Shibaura	
  InsEtute	
  of	
  Technology	
  
2Faculty	
  of	
  Engineering,	
  Shibaura	
  InsEtute	
  of	
  Technology	
  
Contents	
•  IntroducEon	
  
•  Method	
  
–  Method	
  to	
  divide	
  the	
  topic	
  map	
  
–  VIEW	
  
–  Access	
  control	
  system	
  	
  
•  Experiment	
  
•  Result	
  &	
  Discussion	
  
•  Conclusion	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
 2
Topic	
  Map	
  Database	
•  A	
  topic	
  map	
  database	
  should	
  provide	
  an	
  efficient	
  
method	
  to	
  process	
  data	
  (e.g.	
  retrieval,	
  update).	
  
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
Update	
Retrieval	
Topic	
  map	
3
External	
  Schema	
•  In	
  order	
  to	
  limit	
  user	
  access	
  to	
  a	
  part	
  of	
  some	
  topic	
  
map,	
  it	
  is	
  desirable	
  that	
  the	
  database	
  has	
  external	
  
schema.	
  
–  e.g.)	
  privacy,	
  violent	
  content	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
Accessible	
4
External	
  schema	
  of	
  relaEonal	
  databases	
•  RelaEonal	
  databases	
  (RDB)	
  provide	
  us	
  with	
  an	
  external	
  
schema,	
  VIEW.	
  
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
Original	
  relaEon	
VIEW	
projecEons	
  and	
  	
  
selecEons	
Users	
  can	
  access	
  the	
  VIEW	
  as	
  if	
  it	
  is	
  a	
  table,	
  since	
  the	
  VIEW	
  has	
  
the	
  same	
  structure	
  as	
  the	
  original	
  table.	
  
5
External	
  schema	
  of	
  topic	
  maps	
•  We	
  can	
  regard	
  the	
  substructure	
  of	
  topic	
  maps	
  as	
  a	
  
topic	
  map.	
  
•  Therefore,	
  we	
  can	
  expect	
  that	
  it	
  is	
  possible	
  to	
  realize	
  
the	
  external	
  schema	
  of	
  topic	
  maps	
  by	
  defining	
  the	
  
substructure.	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
View	
6
ObjecEve	
  of	
  our	
  study	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
We	
  implement	
  the	
  VIEW	
  to	
  the	
  topic	
  map	
  
database.	
  
–  In	
  order	
  to	
  define	
  a	
  VIEW,	
  we	
  propose	
  the	
  method	
  to	
  
specify	
  the	
  substructure	
  of	
  a	
  topic	
  map.	
  
–  We	
  also	
  propose	
  the	
  way	
  to	
  realize	
  the	
  funcEon	
  to	
  access	
  
the	
  VIEW.	
7
The	
  method	
  to	
  divide	
  the	
  topic	
  map	
•  In	
  order	
  to	
  divide	
  the	
  topic	
  map	
  into	
  substructures,	
  
we	
  employed	
  a	
  network	
  clustering	
  technique	
  as	
  an	
  
example	
  to	
  define	
  substructure.	
  
–  We	
  regard	
  topics	
  and	
  associaEons	
  as	
  nodes	
  and	
  edges.	
  
–  We	
  can	
  specify	
  a	
  group	
  of	
  topics	
  connected	
  to	
  each	
  other.	
  
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
 8
Clustering	
  syntax	
•  We	
  implemented	
  clustering	
  syntax.	
  
–  The	
  query	
  in	
  this	
  syntax	
  returns	
  all	
  topics	
  that	
  belong	
  to	
  
the	
  cluster	
  including	
  an	
  input	
  topic	
  as	
  a	
  parameter.	
  
–  The	
  VIEW	
  is	
  realized	
  by	
  appending	
  this	
  syntax	
  to	
  predicates	
  
in	
  query	
  as	
  is	
  done	
  to	
  realize	
  VIEW	
  in	
  RDB.	
  
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
topicA	
cluster(topicA)?	
9
•  We	
  realized	
  VIEW	
  by	
  adding	
  the	
  cluster	
  syntax	
  to	
  
predicates	
  in	
  a	
  given	
  query.	
  
	
   	
  	
  
Views	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
topic-­‐name($TOPIC,	
  $NAME)?	
topic-­‐name($TOPIC,	
  $NAME)	
  AND	
  cluster(topicA)?	
User’s	
  query	
Append	
  ‘AND’	
  operaEor	
  and	
  cluster	
  syntax	
AND	
10
Access	
  control	
  system	
•  We	
  implemented	
  the	
  funcEon	
  to	
  access	
  the	
  VIEW	
  as	
  an	
  access	
  
control	
  system.	
  
•  For	
  the	
  access	
  control	
  system,	
  we	
  use	
  following	
  informaEon.	
  
–  User	
  list	
  
•  User	
  Name	
  
•  Password	
  
•  User	
  ID	
  
•  Group	
  ID	
  
–  Authority	
  list	
  
•  ID	
  (User	
  ID	
  /	
  Group	
  ID)	
  
•  ObjecEve	
  syntax	
  
•  Predicate	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
 11
Flow	
  of	
  access	
  control	
  mechanism	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
User	
  Name	
  	
 Password	
 User	
  ID	
 Group	
  ID	
User	
  A	
 aaaa	
 1	
 100	
User	
  B	
 bbbb	
 2	
 200	
ID	
 Objec6ve	
  syntaxes	
 Predicates	
1	
 topic-­‐name	
 cluster(topicA)?	
200	
 topic-­‐name	
 cluster(topic1178)?	
User	
  list	
Authority	
  list	
User	
  Name:	
  ‘User	
  A’	
  
Password:	
  ‘aaaa’	
Query:	
  
topic-­‐name($TOPIC,$NAME)?	
User	
  ID:	
  1	
  
Group	
  ID:	
  100	
topic-­‐name($TOPIC,$NAME)	
  	
  
	
   	
   	
  AND	
  cluster(topicA)?	
12
DemonstraEon	
  of	
  the	
  VIEW	
  and	
  the	
  
access	
  control	
  funcEon	
•  In	
  order	
  to	
  demonstrate	
  the	
  VIEW	
  and	
  the	
  access	
  
control	
  funcEon.	
  
–  Query:	
  	
  
	
   	
  topic-­‐	
  name($TOPIC,	
  $NAME)?	
  
–  User:	
  
	
   	
  a	
  user	
  without	
  access	
  limitaEon	
  (User	
  A)	
  
	
   	
  a	
  user	
  with	
  access	
  limitaEon	
  access	
  (User	
  B)	
  
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
 13
The	
  result	
  returned	
  to	
  the	
  use	
  without	
  
access	
  limitaEon	
  (User	
  A)	
Input	
  your	
  user	
  name	
  and	
  password	
  
User	
  name:	
  User	
  A	
  	
  
Password:	
  aaaa	
  	
  
You	
  succeeded	
  to	
  access	
  database	
  	
  
Select	
  Topic	
  Maps:	
  queryTM(Poke.db4o.pokmeonTM)	
  	
  
Query:	
  topic-­‐name($TOPIC,	
  $NAME)?	
  
Row:	
  174	
  
	
  $TOPIC	
  =	
  bulbasaur	
   	
   	
  $NAME	
  =	
  bulbasaur	
  
	
  $TOPIC	
  =	
  ivysaur 	
   	
   	
  $NAME	
  =	
  ivysaur	
  	
  
	
  $TOPIC	
  =	
  venusaur 	
   	
   	
  $NAME	
  =	
  venusaur	
  	
  
	
   	
   	
  . 	
   	
   	
   	
   	
   	
  .	
  
	
   	
   	
  . 	
   	
   	
   	
   	
   	
  .	
  
	
   	
   	
  . 	
   	
   	
   	
   	
   	
  .	
  
	
  $TOPIC	
  =	
  monster 	
   	
   	
  $NAME	
  =	
  monster	
  
	
  $TOPIC	
  =	
  pokemon	
   	
   	
  $NAME	
  =	
  pokemon	
  
	
  $TOPIC	
  =	
  instance-­‐of 	
   	
  $NAME	
  =	
  instance-­‐of	
  
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
UserA	
  can	
  extract	
  	
  
all	
  topics	
  and	
  	
  
their	
  names.	
14
The	
  result	
  returned	
  to	
  the	
  use	
  with	
  
access	
  limitaEon	
  (User	
  B)	
Input	
  your	
  user	
  name	
  and	
  password	
  
User	
  name:	
  User	
  B	
  	
  
Password:	
  bbbb	
  	
  
You	
  succeeded	
  to	
  access	
  database	
  	
  
Select	
  Topic	
  Maps:	
  queryTM(Poke.db4o.pokmeonTM)	
  	
  
Query:	
  topic-­‐name($TOPIC,	
  $NAME)?	
  
Row:	
  10	
  
	
  $TOPIC	
  =	
  raichu 	
   	
   	
  $NAME	
  =	
  raichu	
  
	
  $TOPIC	
  =	
  picachu 	
   	
   	
  $NAME	
  =	
  picachu	
  
	
  $TOPIC	
  =	
  magnemite 	
   	
  $NAME	
  =	
  magnemite	
  
	
  $TOPIC	
  =	
  magneton 	
   	
  $NAME	
  =	
  magneton	
  
	
  $TOPIC	
  =	
  voltorb 	
   	
   	
  $NAME	
  =	
  voltorb	
  
	
  $TOPIC	
  =	
  	
  electrode	
   	
   	
  $NAME	
  =	
  electrode	
  
	
  $TOPIC	
  =	
  	
  jolteon 	
   	
   	
  $NAME	
  =	
  jolteon	
  
	
  $TOPIC	
  =	
  electric 	
   	
   	
  $NAME	
  =	
  electric	
  
	
  $TOPIC	
  =	
  electabuzz 	
   	
  $NAME	
  =	
  electabuzz	
  
	
  $TOPIC	
  =	
  zapdos 	
  	
   	
   	
  $NAME	
  =	
  zapdos	
  
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
UserB	
  can	
  extract	
  	
  
only	
  topics	
  and	
  	
  
their	
  names	
  in	
  
the	
  cluster.	
15
Experiment	
•  We	
  evaluated	
  the	
  increase	
  of	
  execuEon	
  Eme	
  caused	
  
by	
  the	
  addiEon	
  of	
  access	
  control	
  procedures	
  
–  using	
  following	
  two	
  topic	
  maps.	
  
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
Pokemon	
  topic	
  map	
 Large-­‐scale	
  random	
  topic	
  map	
Topic	
 174	
 2,998	
Base	
  name	
 174	
 2,998	
AssociaEon	
 432	
 9,118	
Role	
 864	
 18,236	
Occurrence	
 172	
 0	
16
Verifying	
  affect	
  of	
  execuEon	
  Eme	
•  (As	
  an	
  example)	
  we	
  used	
  the	
  typical	
  query:	
  
–  ‘topic-­‐name($TOPIC,	
  $NAME)?’.	
  	
  
•  We	
  calculated	
  the	
  average	
  execuEon	
  Eme	
  of	
  100	
  
trials	
  under	
  the	
  following	
  condiEons:	
  	
  
–  query	
  execuEon	
  without	
  access	
  control	
  
–  execuEon	
  of	
  queries	
  submiked	
  by	
  user	
  without	
  access	
  
limitaEon	
  
–  execuEon	
  of	
  queries	
  submiked	
  by	
  user	
  with	
  access	
  
limitaEon	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
 17
Average	
  execuEon	
  Eme	
3,580.00	
  
1,717.19	
  
3,579.76	
  
1,696.60	
  
3,293.59	
  
1,488.61	
  
0	
   1,000	
   2,000	
   3,000	
   4,000	
  
Large-­‐scale	
  random	
  topic	
  
map	
  
Pokemon	
  topic	
  map	
  
Without	
  access	
  
control	
  
User	
  without	
  
access	
  limitaEon	
  
User	
  with	
  access	
  
limitaEon	
  
ms	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
The	
  user	
  authenEcaEon	
  does	
  not	
  affect	
  the	
  execuEon	
  Eme	
  
for	
  a	
  topic	
  map	
  that	
  has	
  up	
  to	
  3,000	
  topics.	
18
Conclusion	
•  We	
  proposed	
  a	
  method	
  to	
  create	
  VIEW.	
  
–  We	
  proposed	
  the	
  cluster	
  syntax	
  to	
  specify	
  a	
  substructure	
  
of	
  topic	
  map.	
  
–  By	
  appending	
  the	
  ‘AND’	
  operator	
  and	
  the	
  cluster	
  syntax	
  to	
  
the	
  given	
  query,	
  we	
  realized	
  the	
  external	
  schema	
  (VIEW)	
  of	
  
topic	
  maps.	
  
–  We	
  also	
  implemented	
  the	
  funcEon	
  to	
  access	
  the	
  VIEW.	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
 19
Conclusion	
•  We	
  confirmed	
  that	
  there	
  is	
  only	
  small	
  increase	
  on	
  
execuEon	
  Eme	
  caused	
  by	
  the	
  addiEon	
  of	
  the	
  access	
  
control	
  mechanism	
  	
  
–  for	
  topic	
  maps	
  that	
  have	
  up	
  to	
  3,000	
  topics.	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
 20
PerspecEve	
•  We	
  will	
  study	
  the	
  way	
  to	
  realize	
  inserEon	
  and	
  
deleEon	
  operaEons	
  to	
  the	
  VIEW.	
  	
  
•  It	
  is	
  necessary	
  to	
  discuss	
  the	
  way	
  to	
  define	
  the	
  
substructure	
  of	
  topic	
  maps	
  other	
  than	
  method	
  based	
  
on	
  clustering	
  technique.	
  
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
 21
Thank	
  you	
  for	
  your	
  akenEon!	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
 22
References	
1.  Yuki	
  Kurabara,	
  Takeshi	
  Hosoya,	
  Masaomi	
  Kimura:	
  TOME:	
  Topic	
  Maps	
  
Database	
  Extended.	
  The	
  4th	
  South	
  East	
  Asian	
  Technical	
  University	
  
ConsorEum	
  (SEATUC)	
  Symposium.	
  pp.245—248	
  (2010)	
  
2.  Versant	
  CorporaEon:	
  db4objects,	
  hkp://www.db4o.com/	
  
3.  Joerg	
  Reichardt,	
  Stefan	
  Bornhold	
  :	
  StaEsEcal	
  mechanics	
  of	
  community	
  
detecEon,Physical	
  ReVIEW	
  E,	
  vol.	
  74,	
  016110,	
  pp.1-­‐-­‐14	
  (2006)	
  	
  
4.  Pokemon	
  Topic	
  Map,hkp://www.ontopia.net/omnigator/models/
topicmap_complete.jsp?tm=pokemon.ltm	
  
5.  WANDORA,	
  hkp://www.wandora.org/	
  
6.  Motomu	
  Naito:	
  An	
  IntroducEon	
  to	
  Topic	
  Maps.	
  Tokyo	
  Denki	
  University	
  
Press(2006)	
  
7.  Ontopia:	
  tolog	
  Language	
  tutorial,	
  hkp://www.ontopia.net/	
  
8.  ISO/IEC	
  JTC1/SC34,	
  Topic	
  Map	
  –	
  Data	
  Model,hkp://
www.isotopicmaps.org/sam/sam-­‐model/	
30	
  /	
  9	
  /	
  	
  2010	
 External	
  Schema	
  of	
  Topic	
  Map	
  Database	
 23

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External Schema for Topic Map Database

  • 1. External  Schema  of     Topic  Map  Databases Keita  Nabeta1,  Takashi  Kojima2,     Yuki  Kuribara1,  Takashi  Yamazaki1,  Masaomi  Kimura2   1Graduate  School  of  Engineering,  Shibaura  InsEtute  of  Technology   2Faculty  of  Engineering,  Shibaura  InsEtute  of  Technology  
  • 2. Contents •  IntroducEon   •  Method   –  Method  to  divide  the  topic  map   –  VIEW   –  Access  control  system     •  Experiment   •  Result  &  Discussion   •  Conclusion 30  /  9  /    2010 External  Schema  of  Topic  Map  Database 2
  • 3. Topic  Map  Database •  A  topic  map  database  should  provide  an  efficient   method  to  process  data  (e.g.  retrieval,  update).   30  /  9  /    2010 External  Schema  of  Topic  Map  Database Update Retrieval Topic  map 3
  • 4. External  Schema •  In  order  to  limit  user  access  to  a  part  of  some  topic   map,  it  is  desirable  that  the  database  has  external   schema.   –  e.g.)  privacy,  violent  content 30  /  9  /    2010 External  Schema  of  Topic  Map  Database Accessible 4
  • 5. External  schema  of  relaEonal  databases •  RelaEonal  databases  (RDB)  provide  us  with  an  external   schema,  VIEW.   30  /  9  /    2010 External  Schema  of  Topic  Map  Database Original  relaEon VIEW projecEons  and     selecEons Users  can  access  the  VIEW  as  if  it  is  a  table,  since  the  VIEW  has   the  same  structure  as  the  original  table.   5
  • 6. External  schema  of  topic  maps •  We  can  regard  the  substructure  of  topic  maps  as  a   topic  map.   •  Therefore,  we  can  expect  that  it  is  possible  to  realize   the  external  schema  of  topic  maps  by  defining  the   substructure. 30  /  9  /    2010 External  Schema  of  Topic  Map  Database View 6
  • 7. ObjecEve  of  our  study 30  /  9  /    2010 External  Schema  of  Topic  Map  Database We  implement  the  VIEW  to  the  topic  map   database.   –  In  order  to  define  a  VIEW,  we  propose  the  method  to   specify  the  substructure  of  a  topic  map.   –  We  also  propose  the  way  to  realize  the  funcEon  to  access   the  VIEW. 7
  • 8. The  method  to  divide  the  topic  map •  In  order  to  divide  the  topic  map  into  substructures,   we  employed  a  network  clustering  technique  as  an   example  to  define  substructure.   –  We  regard  topics  and  associaEons  as  nodes  and  edges.   –  We  can  specify  a  group  of  topics  connected  to  each  other.   30  /  9  /    2010 External  Schema  of  Topic  Map  Database 8
  • 9. Clustering  syntax •  We  implemented  clustering  syntax.   –  The  query  in  this  syntax  returns  all  topics  that  belong  to   the  cluster  including  an  input  topic  as  a  parameter.   –  The  VIEW  is  realized  by  appending  this  syntax  to  predicates   in  query  as  is  done  to  realize  VIEW  in  RDB.   30  /  9  /    2010 External  Schema  of  Topic  Map  Database topicA cluster(topicA)? 9
  • 10. •  We  realized  VIEW  by  adding  the  cluster  syntax  to   predicates  in  a  given  query.         Views 30  /  9  /    2010 External  Schema  of  Topic  Map  Database topic-­‐name($TOPIC,  $NAME)? topic-­‐name($TOPIC,  $NAME)  AND  cluster(topicA)? User’s  query Append  ‘AND’  operaEor  and  cluster  syntax AND 10
  • 11. Access  control  system •  We  implemented  the  funcEon  to  access  the  VIEW  as  an  access   control  system.   •  For  the  access  control  system,  we  use  following  informaEon.   –  User  list   •  User  Name   •  Password   •  User  ID   •  Group  ID   –  Authority  list   •  ID  (User  ID  /  Group  ID)   •  ObjecEve  syntax   •  Predicate 30  /  9  /    2010 External  Schema  of  Topic  Map  Database 11
  • 12. Flow  of  access  control  mechanism 30  /  9  /    2010 External  Schema  of  Topic  Map  Database User  Name   Password User  ID Group  ID User  A aaaa 1 100 User  B bbbb 2 200 ID Objec6ve  syntaxes Predicates 1 topic-­‐name cluster(topicA)? 200 topic-­‐name cluster(topic1178)? User  list Authority  list User  Name:  ‘User  A’   Password:  ‘aaaa’ Query:   topic-­‐name($TOPIC,$NAME)? User  ID:  1   Group  ID:  100 topic-­‐name($TOPIC,$NAME)          AND  cluster(topicA)? 12
  • 13. DemonstraEon  of  the  VIEW  and  the   access  control  funcEon •  In  order  to  demonstrate  the  VIEW  and  the  access   control  funcEon.   –  Query:        topic-­‐  name($TOPIC,  $NAME)?   –  User:      a  user  without  access  limitaEon  (User  A)      a  user  with  access  limitaEon  access  (User  B)   30  /  9  /    2010 External  Schema  of  Topic  Map  Database 13
  • 14. The  result  returned  to  the  use  without   access  limitaEon  (User  A) Input  your  user  name  and  password   User  name:  User  A     Password:  aaaa     You  succeeded  to  access  database     Select  Topic  Maps:  queryTM(Poke.db4o.pokmeonTM)     Query:  topic-­‐name($TOPIC,  $NAME)?   Row:  174    $TOPIC  =  bulbasaur      $NAME  =  bulbasaur    $TOPIC  =  ivysaur      $NAME  =  ivysaur      $TOPIC  =  venusaur      $NAME  =  venusaur          .            .        .            .        .            .    $TOPIC  =  monster      $NAME  =  monster    $TOPIC  =  pokemon      $NAME  =  pokemon    $TOPIC  =  instance-­‐of    $NAME  =  instance-­‐of   30  /  9  /    2010 External  Schema  of  Topic  Map  Database UserA  can  extract     all  topics  and     their  names. 14
  • 15. The  result  returned  to  the  use  with   access  limitaEon  (User  B) Input  your  user  name  and  password   User  name:  User  B     Password:  bbbb     You  succeeded  to  access  database     Select  Topic  Maps:  queryTM(Poke.db4o.pokmeonTM)     Query:  topic-­‐name($TOPIC,  $NAME)?   Row:  10    $TOPIC  =  raichu      $NAME  =  raichu    $TOPIC  =  picachu      $NAME  =  picachu    $TOPIC  =  magnemite    $NAME  =  magnemite    $TOPIC  =  magneton    $NAME  =  magneton    $TOPIC  =  voltorb      $NAME  =  voltorb    $TOPIC  =    electrode      $NAME  =  electrode    $TOPIC  =    jolteon      $NAME  =  jolteon    $TOPIC  =  electric      $NAME  =  electric    $TOPIC  =  electabuzz    $NAME  =  electabuzz    $TOPIC  =  zapdos        $NAME  =  zapdos   30  /  9  /    2010 External  Schema  of  Topic  Map  Database UserB  can  extract     only  topics  and     their  names  in   the  cluster. 15
  • 16. Experiment •  We  evaluated  the  increase  of  execuEon  Eme  caused   by  the  addiEon  of  access  control  procedures   –  using  following  two  topic  maps.   30  /  9  /    2010 External  Schema  of  Topic  Map  Database Pokemon  topic  map Large-­‐scale  random  topic  map Topic 174 2,998 Base  name 174 2,998 AssociaEon 432 9,118 Role 864 18,236 Occurrence 172 0 16
  • 17. Verifying  affect  of  execuEon  Eme •  (As  an  example)  we  used  the  typical  query:   –  ‘topic-­‐name($TOPIC,  $NAME)?’.     •  We  calculated  the  average  execuEon  Eme  of  100   trials  under  the  following  condiEons:     –  query  execuEon  without  access  control   –  execuEon  of  queries  submiked  by  user  without  access   limitaEon   –  execuEon  of  queries  submiked  by  user  with  access   limitaEon 30  /  9  /    2010 External  Schema  of  Topic  Map  Database 17
  • 18. Average  execuEon  Eme 3,580.00   1,717.19   3,579.76   1,696.60   3,293.59   1,488.61   0   1,000   2,000   3,000   4,000   Large-­‐scale  random  topic   map   Pokemon  topic  map   Without  access   control   User  without   access  limitaEon   User  with  access   limitaEon   ms 30  /  9  /    2010 External  Schema  of  Topic  Map  Database The  user  authenEcaEon  does  not  affect  the  execuEon  Eme   for  a  topic  map  that  has  up  to  3,000  topics. 18
  • 19. Conclusion •  We  proposed  a  method  to  create  VIEW.   –  We  proposed  the  cluster  syntax  to  specify  a  substructure   of  topic  map.   –  By  appending  the  ‘AND’  operator  and  the  cluster  syntax  to   the  given  query,  we  realized  the  external  schema  (VIEW)  of   topic  maps.   –  We  also  implemented  the  funcEon  to  access  the  VIEW. 30  /  9  /    2010 External  Schema  of  Topic  Map  Database 19
  • 20. Conclusion •  We  confirmed  that  there  is  only  small  increase  on   execuEon  Eme  caused  by  the  addiEon  of  the  access   control  mechanism     –  for  topic  maps  that  have  up  to  3,000  topics. 30  /  9  /    2010 External  Schema  of  Topic  Map  Database 20
  • 21. PerspecEve •  We  will  study  the  way  to  realize  inserEon  and   deleEon  operaEons  to  the  VIEW.     •  It  is  necessary  to  discuss  the  way  to  define  the   substructure  of  topic  maps  other  than  method  based   on  clustering  technique.   30  /  9  /    2010 External  Schema  of  Topic  Map  Database 21
  • 22. Thank  you  for  your  akenEon! 30  /  9  /    2010 External  Schema  of  Topic  Map  Database 22
  • 23. References 1.  Yuki  Kurabara,  Takeshi  Hosoya,  Masaomi  Kimura:  TOME:  Topic  Maps   Database  Extended.  The  4th  South  East  Asian  Technical  University   ConsorEum  (SEATUC)  Symposium.  pp.245—248  (2010)   2.  Versant  CorporaEon:  db4objects,  hkp://www.db4o.com/   3.  Joerg  Reichardt,  Stefan  Bornhold  :  StaEsEcal  mechanics  of  community   detecEon,Physical  ReVIEW  E,  vol.  74,  016110,  pp.1-­‐-­‐14  (2006)     4.  Pokemon  Topic  Map,hkp://www.ontopia.net/omnigator/models/ topicmap_complete.jsp?tm=pokemon.ltm   5.  WANDORA,  hkp://www.wandora.org/   6.  Motomu  Naito:  An  IntroducEon  to  Topic  Maps.  Tokyo  Denki  University   Press(2006)   7.  Ontopia:  tolog  Language  tutorial,  hkp://www.ontopia.net/   8.  ISO/IEC  JTC1/SC34,  Topic  Map  –  Data  Model,hkp:// www.isotopicmaps.org/sam/sam-­‐model/ 30  /  9  /    2010 External  Schema  of  Topic  Map  Database 23