Abstract
Data with time intervals is prominently present in finance, accounting, medicine and many other application domains. When querying such data, it is important to perform operations on aligned intervals, i.e., data is processed together only for the common interval where it is valid in the real world. For instance, an employee contributed to a project only for the time period where both the project was running and the employee was employed by the company, i.e., the employee contributed to the project only over their aligned time interval. A temporal join is thus only evaluated over the aligned interval of an employee and a project. The problem of performing temporal operations, such as temporal aggregation or temporal joins, on data with time intervals using relational database systems can be attributed to the lack of primitives for the alignment of intervals. Even more challenges arise, when the data includes attribute values that are interval-dependent, such as project budgets or cumulative costs, and need to be scaled along with the alignment of intervals during processing. The goal of this thesis is to provide systematic and built-in support for querying data with intervals in relational database systems. The solution we propose uses two temporal primitives a temporal normalizer and a temporal aligner for the alignment of intervals. Temporal operators on interval data are defined by reduction rules that map a temporal operator to an operation with a temporal primitive followed by the corresponding traditional non-temporal operator that uses equality on aligned intervals. A key feature of our approach is that operators can access the original time intervals in predicates and functions, such as join conditions and aggregation functions, using timestamp propagation. Our approach, through timestamp propagation, supports the scaling of attribute values that are interval-dependent. When intervals are aligned during query processing, scaling can be performed at query time with the help of user-defined functions. This allows users to choose whether and how attribute values should be scaled. This is necessary since they may be interested in the total value in one query and the scaled value according to days or even working days in another query. We integrated our solution into the kernel of the open source database system PostgreSQL, which allows to leverage existing query optimization techniques and algorithms.