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Schools are among the primary avenues for public healthcare interventions.With resource limitations posing challenges to the routine conduct of healthand wellness checks in Philippine public schools, the deployment of achatbot-assisted health monitoring system may provide an alternative method.However, deriving insights from raw conversations is not straightforward due tothe expressiveness of natural language that causes variances in the input. Inthis paper, we present a process for transforming unstructured dialogues into astructured schema. The process comprises four stages: (i) processing thedialogues through entity extraction and data aggregation, (ii) storing them asNoSQL documents on the cloud, (iii) transforming them into a star schema foronline analytical processing and building an extract-transform-load workflow,and (iv) creating a web-based dashboard for visualizing summarized data andreports. Performance evaluation of this dashboard showed that increasing thenumber of stored dialogues by a factor of 100,000 increased the loading timefor the display of roll-up, drill-down, and filter results by around only onesecond.

From Unstructured to Structured: Transforming Chatbot Dialogues into Data Mart Schema for Visualization