This document describes a system called Personalized Location Information System (PLIS) that uses rule-based policies to personalize location-based services. PLIS combines a user's context such as location, time, occupation with rules associated with nearby Points of Interest (POIs) to deliver personalized information. It was implemented using standard web technologies, Reaction RuleML for rule representation, and Jess as the inference engine. The system allows users to register profiles, insert their own POIs with rules, and receives customized information based on the rules evaluated against their context. A demonstration showed how different information was presented to two users based on the rules fired for a sample POI given their profiles and environment. Future work proposed enhancing the interface and

1. Personalizing Location
Information
through Rule-Based
Policies
Iosif Viktoratos1, Athanasios Tsadiras1, Nick Bassiliades2,
1
Department of Economics, 2Department of Informatics,
Aristotle University of Thessaloniki
GR-54124 Thessaloniki, Greece
{viktorat, tsadiras, nbassili}@auth.gr

2. Contents
• Rules and policies
• Location Based Services (LBS)
• Motivation-Overview
• Design and Implementation
• Demonstration
• Future work
• Conclusions
2

3. Rules and policies
• Human understandable rule based policies are
an important sector of everyday life
• Used consistently by various types of
businesses to deploy their marketing strategy
• A museum offering free entrance to students, or
a coffee shop decreasing prices on Mondays
are such examples
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4. Rules and policies
• In order to be executed and adopted by an
information service, such kind of policies should
be translated into a computer understandable
language
• A general rule representation language and a
rule engine are needed
• Various efforts has been made
4

5. Rules and policies
• Rule representation • Rule engines
languages -Jess
-RIF -Drools
-RuleML -Prova
-Swrl
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6. Rules and policies
RuleML was widely accepted by scientific community because:
• It is a powerful markup language (XML with a predefined
Schema)
• Easily understandable
• Supports various types of rules such as deductive, reactive
and normative
• Addresses the issues of interoperability and flexibility, by
allowing rules to be encoded in a standard way
• It could easily translated to a rule engine by XSLT
transformation
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7. Location Based Services(LBS)
• Very popular due to Smartphones and related
technologies (such as semantics)
• Used by millions of people for
– Navigation
– Tracking
– Information
– emergency situations
• Researchers and industries are working in
various sectors to evolve such services
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8. Location Based Services (LBS)
• Latest LBS combine semantics with
Smartphone’s capabilities
• Use social media data for personalized
POI recommendations
• Others offer high quality mobile search
capabilities by personalizing query results
or search tag recommendations
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9. Motivation-Overview
• Aim : combine rules with location information
services to deliver personalized and
contextualized information to users
• Implement “Personalized Location Information
System” or PLIS
9

10. Motivation-Overview
• PLIS uses semantics for knowledge sharing and
interoperability
• A rule-based approach was followed for
– higher quality context perception
– autonomy
• An xml-based user friendly language (RuleML) used
because of the fact that PLIS users are capable of
adding rules at run-time
• Could easily combined with most of existing approaches
• Differs by enabling a dynamic rule base (by offering
users the option to add rules at run time)
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11. Design and implementation
• General idea : combine POI’S rule-based policies with
user’s context to deliver personalized, up-to-date
information
• Every time a user logs into the system
– PLIS gets user’s context
– evaluates the rules associated with nearby POIs
– delivers personalized information to user, depending
on the rules fired
• PLIS is able to handle rules concerning
– user’s occupation (e.g. a restaurant offers discount to
students)
– gender
– age
– location (e.g. a coffee shop decreases prices for
users who are less than 200 meters away)
– day
– time 11

12. Design and implementation
For PLIS implementation were used:
• Standard web technologies such as
– JSP
– html
– Javascript
– Google maps
• Reaction RuleML(a subcategory of RuleML) was used
for rules representation because such kinds of policies
are usually represented by production rules
• Jess was chosen as an inference engine because
– it is a lightweight rule engine
– connects well with standard web technologies
• A transformation from rules in RuleML format to Jess
was done by using XSLT technology
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13. Design and implementation
PLIS functionalities-layers
• User registration
• Insertion of Points of Interest
• Rule evaluation and presentation of
personalized information
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14. Design and implementation
User registration
• User completes a registration form so as PLIS
to build a profile (registration time user)
• User profile data such as first name, last name,
occupation, gender, age, city, state, e.t.c are
stored in the database
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15. Design and implementation
Rule evaluation and presentation of personalized
information
• After registration, user is able to log into the system
• System checks user profile database for authentication
• JSP collects user context (profile, location, time, day e.t.c.)
• For every POI, rules (if any) and relevant attribute values are being fetched
(by JSP)
• Rules (after being transformed to a computer understandable language),
POI data and user context attribute values are asserted to the Jess rule
engine
• Jess evaluates rules using the asserted facts and updates POIs’ attribute
values according to the rules fired depending on user’s context
• The new values are fetched by JSP
• Finally, data transfer to client is performed for visualization and per-
sonalized information provision
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16. Design and implementation
Insertion of Points of Interest
• User is able to insert his own POI’s
accompanied by their own rule based policy
• All the data concerning the POI accompanied
by its rules, are saved to the corresponding
database
16

17. 17

18. Demonstration
A scenario considering two completely different
user profile snapshots and a random place will be
presented for better demonstration
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19. Demonstration
Profile Environment
Nam Occupatio Gende Age Time Day Location
e n r
User A Bob Student Male 22 22:4 Thursda Location
5 y A
User B Mary Unemploy Femal 35 19:1 Friday Location B
ed e 0
19

20. Demonstration
Name Average Minimum Rule 1 Rule 2
price per order (€)
person (€)
Place A Pasta 10 5 Decrease Discount
Pizza minimum average price
order 20% 10% for
for students unemployed
which are women on
closer than Fridays
200m after
22:00
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21. Demonstration
Bob(User A) Mary(User B)
• Rule 1 is fired for place A • One rule (rule 2) is fired for
because: place A because Mary:
– he is a student – is unemployed
– his current distance from place – is a woman
A is closer than 200m – current day is Friday
– time is after 22:00 o’clock • Taking these under
• Considering this rule, minimum consideration, average price
order for Bob is 20% less (4€) per person for Mary at place A
for this place is 10% less (9€).
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22. Demonstration
Place A information for Bob Place A information for Mary
22

23. Demonstration
Updated version at:
http://platon.econ.auth.gr/examples/plis/login.jsp
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24. Future work
• A user-friendlier environment has to be
implemented. Either a convenient (probably
visual) RuleML editor could be embedded or a
form based web interface could be implemented
• Use OWL and/or RDF data (as in linked data) to
represent user profiles and POI related
information, for greater flexibility and
interoperabillity
• A mobile application
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25. Conclusions
• Embedding rules to location-based information
systems can offer a boost to the quality of
delivered information
• By developing PLIS, the viability of this idea was
clearly demonstrated
• A capability of adding rules on the fly can not
only lead to powerful, autonomous and
intelligent services, but also to the evolution of
these services
• Experimental testing, confirmed PLIS evolution
without developers intervention
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26. Thank you!!!
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