Abstract
In this demonstration, we focus on middleware data obtained from devices like the network routers and power meters which may be of interest to a technician fixing a customer complaint or a user trying to self-diagnose their utility usage. The users in our case are often unaware of both the data details and database querying language which is in contrast to typical natural language to structured query (NL2SQL) situations where the business analyst knows their domain data but not the querying techniques. We adapt the rule-based NL2SQL approach to our problem and in particular, focus on queries about users, devices and spatio-temporal properties that are unique to this setting. We demonstrate an Alexa-based system, implemented using open-source Rasa, that can answer router usage queries in a home setting and easily extended for power usage or other utilities.
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Lakkaraju, K., Palaiya, V., Paladi, S.T., Appajigowda, C., Srivastava, B., Johri, L. (2022). Data-Based Insights for the Masses: Scaling Natural Language Querying to Middleware Data. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13247. Springer, Cham. https://doi.org/10.1007/978-3-031-00129-1_49
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DOI: https://doi.org/10.1007/978-3-031-00129-1_49
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