An Analysis Pattern Driven Requirements Modeling Method
Jingjing Ji, Rong Peng · 2016
Enormous commercial value brought by big data analysis promotes the vigorous development of big data analysis industry. Due to the difficulties existed in the modeling process, reusing existing analysis experiences becomes a good choice to find an optimal way from problem domain to solution domain efficiently. To help data analysts reuse previous experiences to elicit and model analysis requirements and find satisfactory solutions, an analysis pattern driven analysis requirements modeling method is proposed. It utilizes analysis patterns to help analysts model the relationships between data domains and machine domains, and select available analysis models under the guidance of measurable analysis goals. The modeling process is an interactive and iterative process, which uses the feedbacks from analysts to adjust its analysis behavior.