Supporting Undo and Redo in Scientific Data Analysis

This paper presents a provenance-based technique to support undoing and redoing of data analysis tasks. The technique targets scientists who experiment with combinations of approaches to processing raw data into presentable datasets. Raw data may be noisy and in need of cleaning, it may suffer from sensor drift that requires retrospective calibration and data correction, or it may need gap-filling due to sensor malfunction or environmental conditions. Different raw datasets may have different issues requiring different kinds of adjustments, and each issue may potentially be handled by different approaches. Thus, scientists must often experiment with different sequences of approaches. In our work, we show how provenance information can be used to facilitate this kind of experimentation with scientific datasets. We describe an approach that supports the ability to (1) undo a set of tasks while setting aside the artifacts and consequences of performing those tasks, (2) replace, remove, or add a data-processing technique, and (3) redo automatically those set aside tasks that are consistent with changed technique. We have implemented our technique and demonstrate its utility with a case study of a common, sensor-network, data-processing scenario showing how our approach can reduce the cost of changing intermediate data-processing techniques in a complex, dataintensive process.