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
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
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.