Temporal Aggregation Using a Multidimensional Index

We present a new method for computing temporal aggregation that uses a multi-dimensional index. The novelty of our method lies in mapping the start time and end time of a temporal tuple to a data point in a two-dimensional space to be stored in a two-dimensional index and in calculating the temporal aggregates through a temporal join between the data in the index and the base intervals (defined as the intervals delimited by the start times or end times of the tuples). To enhance the performance, this method calculates the aggregates by incrementally modifying the aggregates from that of the previous base interval without re-reading all tuples for the current base interval. We have compared our method with the SB-tree, which is the state-of-the-art method for temporal aggregation. The results show that our method is an order of magnitude more efficient than the SB-tree method in an environment with frequent updates while increasingly comparable in a read-only environment as the number of aggregates calculated in a query increases.