Probabilistic Granule-Based Inside and Nearest Neighbor Queries Sergio Ilarri 1 , Antonio Corral 2 , Carlos Bobed 1 , and Eduardo Mena 1 1 Dept. of Computer Science and Systems Engineering, University of Zaragoza, 50018 Zaragoza, Spain. {silarri,cbobed,emena}@unizar.es 2 Dept. of Languages and Computing, University of Almeria, 04120 Almeria, Spain. acorral@ual.es Abstract. The development of location-based services and advances in the field of mobile computing have motivated an intensive research effort devoted to the efficient processing of location-dependent queries. In this context, it is usually assumed that location data are expressed at a fine geographic precision. Adding support for location granules means that the user is able to use his/her own terminology for locations (e.g., GPS, cities, states, provinces, etc.), which may have an impact in the semantics of the query, the way the results are presented, and the performance of the query processing. Along with its advantages, the management of the so-called location granules introduces new challenges for query processing. In this paper, we analyze two popular location-dependent constraints, inside and nearest neighbors, and enhance them with the possibility to specify location granules. In this context, we study the problem that arises when the locations of the objects are subject to some imprecision. 1 Introduction Nowadays, there is a great interest in mobile computing, motivated by an ever- increasing use of mobile devices, that aims at providing data access anywhere and at anytime. In the mobile computing field, there has been an intensive research effort in location-based services (LBS). These services provide value-added by considering the locations of the mobile users in order to offer more customized information. How to efficiently process continuous location-dependent queries (e.g., track- ing the available taxi cabs near a moving user) is one of the greatest challenges in location-based services. Thus, these queries require a continuous monitoring of the locations of relevant moving objects in order to keep the answer up-to-date efficiently. Moreover, even if the set of objects satisfying the query condition does not change, their locations and distances to the user do change continuously, and therefore the answer to the query must be updated with the new location data (e.g., to update the locations of the objects on a map). Existing works on location-dependent query processing implicitly assume GPS locations for the objects in a scenario (e.g., [1, 2]). However, precise loca- tions may be unavailable or even be inconvenient for the user. Thus, for example,