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Alminas ˇ
Civilis, Christian S. Jensen, and Stardas Pakalnis
probabilities are global, in that the same probabilities are used for all objects; and
they are history-less, in that past choices by an object during a trip are not taken into
account when computing probabilities for an object.
Next, Wolfson and Yin [24] consider tracking with accuracy guarantees. Based
on experiments with synthetic data, generated to resemble real movement data, they
conclude that a version of the point-based tracking is outperformed by a technique
that resembles basic segment-based tracking (covered in Sect. 13.3). For a small
threshold of 80 m, the latter is a bit more than twice as good as the former; for larger
thresholds, the difference decreases. Their dependent variable is numbers of updates
per distance unit.
It should also be noted that Ding and G¨ uting [6] have recently discussed the use
of what is essentially segment-based tracking within an envisioned system based on
their own proposal for a data model for the management of road network constrained
moving objects.
When only low accuracy of predicted positions are needed, cellular techniques [1,
14, 15] may be used. With such techniques, the mobile network tracks the cells of
the mobile objects in real time to be able to deliver messages or calls to the objects.
In this approach, update is handled in the mobile network. In contrast to these techniques, this chapter assumes scenarios where higher accuracies, well beyond those
given by the cells associated with the base stations in a cellular network, are needed
and where positioning with respect to a road network is attractive.
References
1. Akyildiz IF, Ho JSM (1995) A mobile user location update and paging mechanism under
delay constraints. ACM-Baltzer Journal of Wireless Networks 1:244–255
2. Brilingait˙ e A, Jensen CS, Zokait˙ e N (2004) Enabling routes as context in mobile services.
In: Proceedings of the 12th ACM International Workshop on Geographic Information
Systems, 127–136
3. Chung JD, Paek OH, Lee JW, Ryu KH (2002) Temporal pattern mining of moving objects for location-based services. In: Proceedings of the 13th International Conference on
Database and Expert Systems Applications, 331–340
4. ˇ
Civilis A, Jensen CS, Nenortaite N, and Pakalnis S (2004) Efficient tracking of moving
objects with precision guarantees. In: Proceedings of the International Conference on
Mobile and Ubiquitous Systems: Networking and Services, 164–173. Extended version
available as DB-TR-5, www.cs.aau.dk/DBTR/DBPublications/DBTR-5.pdf
5. ˇ
Civilis A, Jensen CS, Pakalnis S (2005) Techniques for efficient road-network-based
tracking of moving objects. IEEE Transactions on Knowledge and Data Engineering,
17(5): 698–712
6. Ding Z, G¨ uting RH (2004) Managing moving objects on dynamic transportation networks. In: Proceedings of the 16th International Conference on Scientific and Statistical
Database Management, 287–296
7. GloVentures (2006) Glofun Games. www.glofun.com
8. Groundspeak (2006) Geocaching. www.geocaching.com
9. G¨ uting RH, Schneider M (2005) Moving objects databases. Morgan Kaufman
Alminas ˇ
Civilis, Christian S. Jensen, and Stardas Pakalnis
probabilities are global, in that the same probabilities are used for all objects; and
they are history-less, in that past choices by an object during a trip are not taken into
account when computing probabilities for an object.
Next, Wolfson and Yin [24] consider tracking with accuracy guarantees. Based
on experiments with synthetic data, generated to resemble real movement data, they
conclude that a version of the point-based tracking is outperformed by a technique
that resembles basic segment-based tracking (covered in Sect. 13.3). For a small
threshold of 80 m, the latter is a bit more than twice as good as the former; for larger
thresholds, the difference decreases. Their dependent variable is numbers of updates
per distance unit.
It should also be noted that Ding and G¨ uting [6] have recently discussed the use
of what is essentially segment-based tracking within an envisioned system based on
their own proposal for a data model for the management of road network constrained
moving objects.
When only low accuracy of predicted positions are needed, cellular techniques [1,
14, 15] may be used. With such techniques, the mobile network tracks the cells of
the mobile objects in real time to be able to deliver messages or calls to the objects.
In this approach, update is handled in the mobile network. In contrast to these techniques, this chapter assumes scenarios where higher accuracies, well beyond those
given by the cells associated with the base stations in a cellular network, are needed
and where positioning with respect to a road network is attractive.
References
1. Akyildiz IF, Ho JSM (1995) A mobile user location update and paging mechanism under
delay constraints. ACM-Baltzer Journal of Wireless Networks 1:244–255
2. Brilingait˙ e A, Jensen CS, Zokait˙ e N (2004) Enabling routes as context in mobile services.
In: Proceedings of the 12th ACM International Workshop on Geographic Information
Systems, 127–136
3. Chung JD, Paek OH, Lee JW, Ryu KH (2002) Temporal pattern mining of moving objects for location-based services. In: Proceedings of the 13th International Conference on
Database and Expert Systems Applications, 331–340
4. ˇ
Civilis A, Jensen CS, Nenortaite N, and Pakalnis S (2004) Efficient tracking of moving
objects with precision guarantees. In: Proceedings of the International Conference on
Mobile and Ubiquitous Systems: Networking and Services, 164–173. Extended version
available as DB-TR-5, www.cs.aau.dk/DBTR/DBPublications/DBTR-5.pdf
5. ˇ
Civilis A, Jensen CS, Pakalnis S (2005) Techniques for efficient road-network-based
tracking of moving objects. IEEE Transactions on Knowledge and Data Engineering,
17(5): 698–712
6. Ding Z, G¨ uting RH (2004) Managing moving objects on dynamic transportation networks. In: Proceedings of the 16th International Conference on Scientific and Statistical
Database Management, 287–296
7. GloVentures (2006) Glofun Games. www.glofun.com
8. Groundspeak (2006) Geocaching. www.geocaching.com
9. G¨ uting RH, Schneider M (2005) Moving objects databases. Morgan Kaufman