In traditional databases, we deal with static attributes which change very infrecpiently over time and their change is handled with an explicit update operation. In temporal databases, the time of change of attributes is also important and every update creates a new version. Attributes, typically change more frequently over time. A more agitated category of attributes are the so-called dynamic attributes whose value changes continuously over time, thus making it impractical to explicitly update them as they change. In this thesis, we conduct a performance evaluation study of two indexing methods for dynamic attributes. These are based on the key idea of using a linear function that describes the way the attribute changes over time and allows us to predict its value in the future based on any value of it in the past. The problem is rooted in the context of mobile data management and draws upon the fields of spatial and temporal indexing. We contribute various experimental results, mathematical analyses, and improvement and optimization algorithms. Finally, inspired by a few of the observed shortcomings of both of the studied techniques, we propose a novel inde.xing method which we call the FP-Index which is shown analytically to have promising prospects and to beat both methods over most performance parameters.