Abstract: Disclosed herein is an apparatus for processing a predictive spatiotemporal query based on synthetic data. The apparatus includes a query-processing unit for analyzing a predictive spatiotemporal query of a user and returning a processing result, a machine-learning unit for training a machine-learning model in response to a request from the query-processing unit and generating synthetic spatiotemporal data based on the machine-learning model, and a data storage unit for storing raw spatiotemporal data and the generated synthetic spatiotemporal data, and the raw spatiotemporal data may be stored in the form of a table including an identifier column and a position column.

Abstract: Provided are a method and apparatus for processing an approximate query based on a machine learning model. When receiving a user query through an approximate query language extension interface, a processing apparatus parses a user query. The user query is an extended query form that includes information according to a user requirements. The processing apparatus generates a basic execution plan based on a parsing result and generates a plurality of executable candidate execution plans based on the basic execution plan. Then, an optimal final execution plan reflecting user requirements is selected from among the plurality of executable candidate execution plans, and query processing is performed on the user query based on a final execution plan.

Abstract: The following description relates to a query optimization method in distributed query engine and an apparatus thereof.