Prompt-to-Metric: LLMs and Graph Algorithms for Platform Ecosystem Health Monitoring
DOI:
https://doi.org/10.64552/wipiec.v11i1.97Keywords:
platform ecosystem, software ecosystem, performance evaluation, analytics, graph algorithms, large language modelsAbstract
Platform ecosystems are networks of interconnected actors co-creating value through a shared technological platform. Such socio-technical systems require unique key performance indicators and health evaluation metrics to address the unique characteristics and value-creation modes they entail. Several platform ecosystems health evaluation models have been suggested in literature, along with a plethora of metrics. This study presents Prompt-to-Metric, a system that allows users, mainly platform orchestrators and decision-makers, to monitor the health of a platform ecosystem through natural language queries. The system relies on a KPI network of approximately 400 health metrics classified across four levels of hierarchy according to a model developed through a systematic literature review on the topic. In addition, the pipeline uses graph algorithms to enhance the relevancy of the responses and uncover insights regarding metrics relatedness. The system was implemented as a prototype and is being evaluated for feasibility in real-world application scenarios using data from an operational platform ecosystem. Future work includes expanding the set of calculable metrics, improving response relevance, and further evaluation in real-world settings.
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