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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | TemProRA: Top-k temporal-probabilistic results analysis |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 978-1-5090-2020-1 |
| Page Range | 1382 - 1385 |
| Event Title | 32nd IEEE International Conference on Data Engineering, ICDE 2016 |
| Event Type | conference |
| Event Location | Helsinki, Finland |
| Event Start Date | May 16 - 2016 |
| Event End Date | May 20 - 2016 |
| Publisher | IEEE |
| Abstract Text | The study of time and probability, as two combined dimensions in database systems, has focused on the correct and efficient computation of the probabilities and time intervals. However, there is a lack of analytical information that allows users to understand and tune the probability of time-varying result tuples. In this demonstration, we present TemProRA, a system that focuses on the analysis of the top-k temporal probabilistic results of a query. We propose the Temporal Probabilistic Lineage Tree (TPLT), the Temporal Probabilistic Bubble Chart (TPBC) and the Temporal Probabilistic Column Chart (TPCC): for each output tuple these three tools are created to provide the user with the most important information to systematically modify the time-varying probability of result tuples. The effectiveness and usefulness of TemProRA are demonstrated through queries performed on a dataset created based on data from Migros, the leading Swiss supermarket branch. |
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