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Pattern discovery often occurs in studying correlations of data viewed from multiple perspectives. For high dimensional, low level sensory data such as images, it is especially important that the input can be studied in its raw form along with relevant attributes from the application context and all intermediate results of manual or automatic analysis. To facilitate this, flexible and effective data analysis tools that can handle diverse data types, a wide range of objectives, large data volumes, and variable demands on speed are in critical need. I will describe our experiences with Mirage ( http://www.cs.bell-labs.com/who/tkh/mirage), a research software tool for open-ended interactive pattern discovery. I will show the tool's applications in analyzing photonics simulations, evaluating performances of telecommunication systems, and in the Virtual Observatory. I will also discuss directions for future research on how to organize the tool to lay a solid foundation for meeting these needs and enabling continuous growth.