A natural-language chatbot for data center IoT queries builds small query-specific knowledge graphs to ground LLM-generated SPARQL, reporting 92.5% accuracy and 3.03s latency.
Managing Schema Evolution in NoSQL Data Stores
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abstract
NoSQL data stores are commonly schema-less, providing no means for globally defining or managing the schema. While this offers great flexibility in early stages of application development, developers soon can experience the heavy burden of dealing with increasingly heterogeneous data. This paper targets schema evolution for NoSQL data stores, the complex task of adapting and changing the implicit structure of the data stored. We discuss the recommendations of the developer community on handling schema changes, and introduce a simple, declarative schema evolution language. With our language, software developers and architects can systematically manage the evolution of their production data and perform typical schema maintenance tasks. We further provide a holistic NoSQL database programming language to define the semantics of our schema evolution language. Our solution does not require any modifications to the NoSQL data store, treating the data store as a black box. Thus, we want to address application developers that use NoSQL systems
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From Data Center IoT Telemetry to Data Analytics Chatbots -- Virtual Knowledge Graph is All You Need
A natural-language chatbot for data center IoT queries builds small query-specific knowledge graphs to ground LLM-generated SPARQL, reporting 92.5% accuracy and 3.03s latency.