Retrieval of very large temporal datasets for interactive tasks

Many tasks dealing with temporal data, such as interactive browse through temporal datasets, require intensive retrieval from the database. Depending on the user's task, the data retrieved may be too large to fit in the local memory. Even if it fits, the time taken to retrieve the data may compromise user interaction. This work proposes a method, TravelLight, which improves interactive traveling across very large temporal datasets by exploring the temporal consistency of data items. The proposed method consists of two algorithms: the data retrieval and the memory management algorithm, both contributing to improve memory usage and, most important, to reduce the turnaround time. Results are reported concerning experiments with a temporally linear navigation across two datasets of one million items, which differ in the average time span of items. With the obtained results it is possible to conclude that the proposed method reduces turnaround time thus enhancing the response of interactive tasks over very large temporal datasets.