Towards Evolutionary, Domain-Specific Query Classification Based on Policy Rules
Contribution at the LWDA 2020 conference
Many devices like smart sensors produce a vast amount of data that are still commonly stored in relational databases and are being processed using SQL queries. This data is only useable if it is processed in a fashion that results in applicable information for the users posing these queries. Thus, it can be very supportive for them to assess other queries that have already processed the targeted data. This is not a simple exercise, as SQL allows alias names and various syntactic structures to express equivalent queries. A manual assessment is also hard to accomplish due to the amount of qualified queries. We present a framework for evolutionary SQL query classification. Based on the analysis of query logs, query metadata like schema lineage and result statistics are automatically derived. Our framework enables users to define domain-specific policy rules for automatic query classification based on the query metadata. Classification is done according to domain-specific, contextual attributes that can be defined evolutionary at runtime, together with the policy rules. The classification results enrich the query metadata.
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