Conclusions and Open Issues

Due to the great interest that LBS are attracting in today’s wireless applications, positioning technologies are becoming of primary importance in wireless networks, having increasing technological support and improvements. On the other hand, tracking technologies have very little support by wireless networks. Indeed, only few and ad hoc methods may allow to collect user movements. However, we believe that the great potential of new generation movement-aware applications may push ven-dors to design and implement new tracking procedure as well as improving current ones.

From the point of view of the accuracy level (in space and time), the availabil-ity of new generation satellite positioning systems and the use of the associated receivers are becoming of a widespread use in everyday life for an ever-increasing number of mobile users. As already pointed out in Sect. 3.2, the accuracy level is going to decrease down to a few meters, so we can expect, in the near future, to have a great amount of highly accurate user trajectories.

As far as synthetic trajectory generators are concerned, many direction can be followed to make these tools more GeoPKDD oriented. For example, synthetic trajectory generators can be extended, to support more realistic movements, by allowing the user to configure the movement of real user movements. For example, the memory-less approach employed by GSTD (which is also used in other genera-tors) is a rather artificial methodology hardly found in real world’s conditions, since the majority of the real spatiotemporal objects is moving from a particular origin to a prespecified destination. Furthermore, the movement of real moving objects is determined by other parameters, such as speed and direction, which cannot be fully controlled by the existing GSTD’s interface. In addition, there are certain types of query processing algorithms and indexing techniques that require the manage-ment of other parameters influencing the performance of the algorithms; as such, algorithms exploiting object’s speed and direction would have to be tested against moving objects with known speed or direction distributions. Another direction that can to be followed is the extension of tools toward data mining needs, thus com-bining realistic behavior with more specific algorithm requirements, following the preliminary ideas of CENTRE 2.0.

Furthermore, another development direction for “GeoPKDD data synthesizers”

is to produce an integration of the different tools. The level of the integration can vary from loose to tight. A loose integration means to design a set of interface specifications to make these tools producing a common output format (standard

3 Wireless Network Data Sources 99 trajectories format). A tight integration, on the other hand, means to integrate the tools in a unique software architecture, a unique language, and a unique user inter-face. This means to design an architecture where interfaces are defined to produce a common output format and where a set of guidelines is drawn to direct the user to the suitable tool based on his/her requirements.

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Chapter 4