Orthogonal operators for user-defined symbolic periodicities

We identify a set of orthogonal properties characterizing periodicities; based on these we define a lattice of classes (of periodicities). For each property, we introduce a language operator and, this way, we propose a family of symbolic languages, one for each subset of operators, one for each point in the lattice. So, the expressiveness and meaning of each operator, and thus of each language in the family, are clearly defined, and a user can select the language that exactly covers the properties of her domain. To the best of our knowledge, our language covering the top of the lattice (i.e., all of the properties) is more expressive than any other symbolic language in the AI and DB literature.