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