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Data Mining for Personal Navigation

This paper explores advanced data mining techniques for personal navigation systems, focusing on location-based services. It highlights the importance of retrieving relevant information based on a user's context, preferences, and real-time location. By integrating traditional relevance factors such as keywords and user profiles with additional criteria like geographic coordinates, the study presents a novel approach to keep mobile users informed and equipped. It aims to improve navigational guidance and information retrieval, ensuring users receive tailored suggestions during their journeys.

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Data Mining for Personal Navigation

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  1. Data Mining for Personal Navigation Gurushyam Hariharan Pasi Fränti Sandeep Mehta DYNAMAP PROJECT University of Joensuu, FINLAND http://cs.joensuu.fi/pages/franti/dynamap/

  2. Personal Navigation Location information is used for: • Plotting location of user on a map • Navigational guidance to given destination • Provide data related to location

  3. Data mining required • For retrieval of location-related information from www (Web mining) • For Task-oriented data extraction from web documents • For user profiling (additional parameters for defining what is relevant)

  4. Overall Scheme

  5. Traditional definition of relevance • Keyword • Web-Based Search Engines • User Profile • Past Behavior of self and community define the profile • Automatic suggestions (e.g. Amazon.com proposed other “relevant” books)

  6. Novel Approach to Data(Web)-Mining for a MOBILE USER • Key is to find RELEVANT information • Re-defining Relevance for Mining Web • Relevance depends on • User request at the moment • User preferences • Relevance = Traditional Parameters (Keywords, Profile) + LOCATION

  7. Additional relevance factor: Location • Co-ordinates of mobile User  City/Street address • Relevance=Location + Keywords (+Profile) • For example: • Helsinki downtown • “Restaurant” • “Budget prize” “Vegetarian”

  8. Issues for a Personal Navigation System with the NEW Definition • Spot the client on the Globe • Co-ordinate  Location interconvertion • Data Extraction: Task oriented search of web • Scalability (in accordance with User’s Mobile Device) • User profile learning • Pass Relevant Information to the Mobile User

  9. Possible use scenario

  10. Scenarion including user profiling

  11. WE REGRET... ... the absence of the authors. • Gurushyam did not get VISA to USA • Pasi is busy elsewhere and could not change his plans in such short notice:

  12. THANK YOU ! For more information, contact: • franti@cs.joensuu.fi • http://cs.joensuu.fi/pages/franti/dynamap/

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