Most modern database applications involve a significant amount of time dependent data and a significant portion of this data is now-relative. Now-relative data are a natural and meaningful part of every temporal database as well as being the focus of most queries. Previous studies indicate that the choice of the representation of now significantly influences the efficiency of accessing bitemporal data. In this paper we propose and experimentally evaluate a novel approach to represent now that we termed the POINT approach, in which now-relative facts are represented as points on the transaction-time and/or valid-time line. Furthermore, in the POINT approach we propose a logical query transformation that relies on the above representation and on the geometry features of spatial access methods. Such a logical query transformation enables off-the-shelf spatial indexes to be used. We empirically prove that the POINT approach is efficient on now-relative bitemporal data, outperforming the maximum timestamp approach that has been proven to the best approach to now-relative data in the literature, independently of the indexing methodology (B + - tree vs R *- tree) being used. Specifically, if spatial indexing is used, the POINT approach outperforms the maximum timestamp approach to the extent of factor more than 10, both in number of disk accesses and CPU usage.