Data Tweening: Incremental Visualization of Data Transforms

Joe Hellerstein

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.

Published On: July 1, 2017

Presented At/In: PVLDB

Download Paper: https://rise.cs.berkeley.edu/wp-content/uploads/2017/07/p661-khan.pdf

Link: http://www.vldb.org/pvldb/vol10/p661-khan.pdf

Authors: Larry Xu, Arnab Nandi, Joe Hellerstein