Efficient Differential Timeslice Computation

Transaction-time databases record all previous database states and are ever-growing, leading to potentially huge quantities of data. For that reason, efficient query processing is of particular importance. Due to the large size of transaction-time relations, it is advantageous to utilize cheap write-once storage media for storage. This is facilitated by adopting a log-based storage structure. Timeslices, i.e., relation states or snapshots, are computed by traversing the logs, using previously computed and cached timeslices as outsets. When computing a new timeslice, the cache will contain two candidate outsets: an earlier outset and a later outset. We provide efficient means of always picking the optimal one. Specifically, we define and investigate the use of a new data structure, the B⁺ -tree-like Insertion Tree (I-tree), for this purpose. The cost of using an I-tree for picking the optimal outset is similar to that of using a B⁺ -tree. Being sparse, I-trees require little space overhead, and they are cheap to maintain as the underlying relations are updated. I-trees also provide a basis for precisely and efficiently estimating the costs of performing timeslices in advance. This is useful for query optimization and can be essential in real-time applications. Finally, it is demonstrated how I-trees can be used in the computation of other types of queries