In the context of interactive query sessions, it is common to issue a succession of queries, transforming a dataset to the desired result. It is often difficult to comprehend a succession of transformations, especially for complex queries. Thus, to facilitate understanding of each data transformation and to provide continuous feedback, we introduce the concept of "data tweening", i.e., interpolating between resultsets, presenting to the user a series of incremental visual representations of a resultset transformation. We present tweening methods that consider not just the changes in the result, but also the changes in the query. Through user studies, we show that data tweening allows users to efficiently comprehend data transforms, and also enables them to gain a better understanding of the underlying query operations.

With the development and advancement of new data interaction modalities, data exploration and analysis has become a highly interactive process situating the user in a session of successive queries. With rapidly changing results, it becomes difficult for the end user to fully comprehend transformations, especially the transforms corresponding to complex queries. We introduce "data tweening" as an informative way of visualizing structural data transforms, presenting the users with a series of incremental visual representations of a resultset transformation. We present transformations as ordered sequences of basic structural transforms and visual cues. The sequences are generated using an automated framework which utilizes differences between the consecutive resultsets and queries in a query session. We evaluate the effectiveness of tweening as a visualization method through a user study.

In the context of data exploration, users often interact with relational database systems in an interactive query session to form useful insights. Each query a user executes can potentially transform a resultset in complex ways. We explore some of the challenges in understanding these transformations, and how these challenges can be solved through more informative visual representations of data transforms. We present the concept of "tweening" of resultsets as a method of incrementally visualizing data transformations, and explore approaches towards generating these resultset tweens. Through a series of user studies, we evaluate tweening as an effective method of understanding the changes that result from data transformations.