1. © Christian S. Jensen - CAMM workshop, Providence, RI, January 24-25, 2002 Data Representation and Indexing in Location-Enabled M-Services Christian S. Jensen Nykredit Center for Database Research Aalborg University www.cs.auc.dk/NDB

2. © Christian S. Jensen - CAMM workshop, Providence, RI, January 24-25, 20022 Background Hardware-related advances are key drivers Miniaturization, positioning, communications, displays, performance/price We don’t sort twice as fast each year on the same hardware Mobile objects include phones, ”PDAs,” vehicles Applications include Safety, metered services, games Traffic, personal productivity, professional services We don’t yet know what the big applications will be! Cf. the SMS experience in Europe

3. © Christian S. Jensen - CAMM workshop, Providence, RI, January 24-25, 20023 Data Representation The objects are “movements” in (space, time) space. Uncertainty is a fundamental part of the game Every measuring technique has an associated precision. Sampling also introduces uncertainty. Issues Choice of sampling policy and interpolation technique Representation of movements Querying Space is not Euclidean, but has infrastructure Hinders (enables) movement or takes the objects to a lower- dimensional space. Issues Exploiting this to reduce uncertainty and to obtain better performance Exploiting this to improve querying behavior

4. © Christian S. Jensen - CAMM workshop, Providence, RI, January 24-25, 20024 Indexing and Precomputation The scenarios considered involve hyper-dynamic data. Indexing and precomputation are still called for. Spatial indices (e.g., R-trees) exhibit poor update performance. Many precomputation techniques assume bulkloading. Improvements are needed. Modeling data as functions.  Updates are needed only when parameters change. Aggressive use of fast memory  Techniques should exploit all available fast memory. Full utilization of the I/O bandwidth  Buffering techniques Approximation techniques Parallellization/partitioning