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Ruleml2012 - personalizing location information through rule based policies


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Ruleml2012 - personalizing location information through rule based policies

  1. 1. Personalizing Location Information through Rule-Based PoliciesIosif Viktoratos1, Athanasios Tsadiras1, Nick Bassiliades2, 1 Department of Economics, 2Department of Informatics, Aristotle University of Thessaloniki GR-54124 Thessaloniki, Greece {viktorat, tsadiras, nbassili}
  2. 2. Contents• Rules and policies• Location Based Services (LBS)• Motivation-Overview• Design and Implementation• Demonstration• Future work• Conclusions 2
  3. 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 3
  4. 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. 5. Rules and policies• Rule representation • Rule engines languages -Jess -RIF -Drools -RuleML -Prova -Swrl 5
  6. 6. Rules and policiesRuleML 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 6
  7. 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 7
  8. 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 8
  9. 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. 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) 10
  11. 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. 12. Design and implementationFor 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 12
  13. 13. Design and implementation PLIS functionalities-layers• User registration• Insertion of Points of Interest• Rule evaluation and presentation of personalized information 13
  14. 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 14
  15. 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 15
  16. 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
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  18. 18. DemonstrationA scenario considering two completely differentuser profile snapshots and a random place will bepresented for better demonstration 18
  19. 19. Demonstration Profile Environment Nam Occupatio Gende Age Time Day Location e n rUser A Bob Student Male 22 22:4 Thursda Location 5 y AUser B Mary Unemploy Femal 35 19:1 Friday Location B ed e 0 19
  20. 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 20
  21. 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€). 21
  22. 22. DemonstrationPlace A information for Bob Place A information for Mary 22
  23. 23. DemonstrationUpdated version at: 23
  24. 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 24
  25. 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 25
  26. 26. Thank you!!! 26