External Schema for Topic Map Database

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

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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


Accessible

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External schema of relaEonal databases

•  RelaEonal databases (RDB) provide us with an external schema, VIEW.


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.

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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.


View

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ObjecEve of our study


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.

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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.


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.


topicA

cluster(topicA)?

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•  We realized VIEW by adding the cluster syntax to predicates in a given query.

Views


topic-‐name($TOPIC, $NAME)?

topic-‐name($TOPIC, $NAME) AND cluster(topicA)?

User’s query

Append ‘AND’ operaEor and cluster syntax

AND

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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


Flow of access control mechanism


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)?

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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)


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


UserA can extract all topics and their names.

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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


UserB can extract only topics and their names in the cluster.

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Experiment

•  We evaluated the increase of execuEon Eme caused by the addiEon of access control procedures –  using following two topic maps.


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

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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


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


The user authenEcaEon does not affect the execuEon Eme for a topic map that has up to 3,000 topics.

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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.


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.


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.


Thank you for your akenEon!


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/


External Schema for Topic Map Database

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