Abstract
Data-driven processes are becoming increasingly useful and affordable for decision support as large data sets become available, the computational resources become more powerful, and the data- analysis approaches become more mature. Because the development of data-driven approaches only minimally relies on domain experts, they can provide decision support aids rapidly and with low development cost. Several ongoing projects in the authors’ development group are applying data-driven applications to help satellite operators quickly and confidently build real-time situational awareness during potentially dangerous events. Among the most significant challenges in building, testing, and deploying data-driven applications are performance assessment issues. Performance assessment allows developers to determine whether a given solution meets expectations and requirements. In addition, because data-driven applications are often based on highly configurable algorithms with many nonobvious options (e.g., parameter settings, data sets, algorithm variants, etc.), performance assessment can provide crucial feedback to the developer in tuning an approach and comparing alternatives. This paper discusses performance- assessment technologies and presents a semi-automated approach to multi-objective parameter optimization; all development was in the context of data-driven application tuning for space situational awareness.
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