In order to cope with large-scale topic maps that store a lot of information, it is necessary to utilize topic map databases. Although, database management systems should provide users with external schema functions such as views, topic map databases do not have such functions. In this paper, we propose a method of implementing a view function, by focusing on the fact that the substructure of topic maps can be regarded as a topic map. In order to realize the idea, we developed an access control system based on the view function. Through an experiment to measure the execution time, we confirmed that these functions work correctly and have little effect on the execution time.

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
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Schema
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Map
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2

3. Topic
Map
Database
• A
topic
map
database
should
provide
an
efficient
method
to
process
data
(e.g.
retrieval,
update).
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Schema
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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
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Schema
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Map
Database
Accessible
4

5. External
schema
of
relaEonal
databases
• RelaEonal
databases
(RDB)
provide
us
with
an
external
schema,
VIEW.
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External
Schema
of
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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.
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Map
Database
View
6

7. ObjecEve
of
our
study
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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.
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Schema
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Map
Database
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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.
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Schema
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Topic
Map
Database
topicA
cluster(topicA)?
9

10. • We
realized
VIEW
by
adding
the
cluster
syntax
to
predicates
in
a
given
query.
Views
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Schema
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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
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Map
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12. Flow
of
access
control
mechanism
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Schema
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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,
$NAME)?
– User:
a
user
without
access
limitaEon
(User
A)
a
user
with
access
limitaEon
access
(User
B)
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Map
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14. The
result
returned
to
the
use
without
access
limitaEon
(User
A)
Input
your
user
name
and
password
User
name:
User
A
Password:
aaaa
You
succeeded
to
access
database
Select
Topic
Maps:
queryTM(Poke.db4o.pokmeonTM)
Query:
topic-­‐name($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
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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
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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.
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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
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Map
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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
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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.
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Schema
of
Topic
Map
Database
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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.
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of
Topic
Map
Database
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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.
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Map
Database
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22. Thank
you
for
your
akenEon!
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Map
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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/
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Topic
Map
Database
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