Storing and Processing Temporal Data in a Main Memory Column Store

Managing and accessing temporal data is of increasing importance in industry. So far, most companies model the time dimension on the application layer rather than pushing down the operators to the database, which leads to a significant performance over-head. The goal of this PhD thesis is to develop a native support of temporal features for SAP HANA, which is a commercial in-memory column store database system. We investigate different alternatives to store temporal data physically and analyze the trade-offs arising from different memory layouts which cluster the data either by time or by space dimension. Taking into account the underlying physical representation, different temporal operators such as temporal aggregation, time travel and temporal join have to be executed efficiently. We present a novel data structure called Timeline Index and algorithms based on this index, which have a very competitive performance for all temporal operators beating existing best-of-breed approaches by factors, sometimes even by orders of magnitude. The results of this thesis are currently being integrated into HANA, with the goal of being shipped to the cus-tomers as a productive release within the next few months. 1