The analysis of complex spatiotemporal data such as fluid flows is a non-trivial task making knowledge discovery difficult. Conventional flow analysis methods suffer from several shortcomings: (1) a lack of interpretation in terms of physical parameters (e.g. momentum); (2) restrictions on flow conditions; (3) inconsideration of interactive controls for users; (4) disregard for users’ analysis requirements while processing data; (5) a necessity of domain expertise. The objective of this thesis is a feasibility study of a Visual Analytics (VA) platform to overcome these shortcomings. The thesis has thus two Foci: Focus 1 develops novel flow analysis techniques to address the first 3 shortcomings; and Focus 2 introduces an end-to-end automated, user-centered adaptation of data processing workflows to mitigate the remaining 2 shortcomings. Preliminary evaluation and simulation outcomes indicate that both foci together set the foundation for a VA platform where multiple users of varying experience levels can collaboratively analyze flows.