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  Data Management Support for
Location Based Services
 
    Background  
   
   

Several trends in hardware technologies combine to provide the enabling foundation for location-based services. These trends include continued advances in miniaturization of electronics technologies, display devices, and wireless communications. Other trends are the improved performance of general computing technologies and the general improvement in the performance/price ratio of electronics hardware. Perhaps most importantly, positioning technologies such as GPS (global positioning system) are becoming increasingly accurate: in a few years, the accuracy of GPS is expected to reach 5 meters.

It is expected that the coming years will witness very large quantities of on-line (i.e., Internet-worked), position-aware, wireless objects capable of continuous movement. Examples of such objects include consumers using WAP-enabled mobile-phone terminals and PDAs (personal digital assistants), tourists carrying on-line and position-aware "cameras" and "wrist watches", vehicles with computing and navigation equipment, etc.

These developments pave the way to a range of qualitatively new types of Internet-based services. These types of services - which either make little sense or are of limited interest in the traditional context of fixed-location, PC- or workstation-based
computing - include the following:

  • traffic coordination, management, and way-finding,
  • location-aware advertising,
  • integrated information services, e.g., tourist services,
  • safety-related services, and
  • location-based games that merge virtual and physical spaces.

The major application area for the research performed in this project will be traffic-related services, but other areas such as games will also be relevant.

One single generic scenario may be envisioned for such services. Moving objects use services that involve location information. The objects disclose their positional information (position, speed, velocity, etc.) to the services, which in turn use this and other information to provide specific functionality. Each service maintains a log of the requests made to it, a so-called "click-stream", (or "touch-point log") and uses this for analysing user interaction with the service. The service accumulates data derived from the click-streams and integrates this with other customer data in a data warehouse, a very large repository of integrated information that may be used for data analysis. The warehouse data is used for masscustomization of the service, so that each user receives a service customized to the user's specific situation, preferences, and needs, e.g., dynamic, user-specific web-page content. In addition, the warehouse is used for delayed modification of the services provided, and for longer-term strategic decision making. Business intelligence techniques such as on-line analytical processing and data mining are used for these purposes. For example, a data warehouse may capture requests to a traffic service along with the positions of the users. This may be used to analyze the traffic movement pattern of the users, e.g., in order to cluster them into groups of similarly-behaving users. These groups may then be used to filter the information directed to the users so that users in the same group receive the same information. Also, users in the same group can recommend information to each other, so-called "collaborative filtering".

The integration of location information into this scenario has received little attention and offers a number of fundamental challenges. Common to these challenges is the task of extending techniques that work well for static data to support dynamic, continuously evolving data.

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   CfPC©, updated: 14-nov-05