Query processing and user interaction form the cornerstone of modern database systems, providing the framework for translating high‐level user queries into efficient, actionable operations.

The expanding volume and complexity of modern datasets have necessitated innovative approaches to data query processing and visualisation. Contemporary methods must efficiently extract actionable ...

Not so with DBAs! In SQL Server 2017, when the query optimizer encounters code with a multi-statement TVF, the query optimizer will pause optimization, execute the TVF subtree to get a very accurate ...

The core value of conversational BI lies in lowering the threshold for data analysis and promoting "data democratization." Traditional BI requires users to master SQL, OLAP, and possess data thinking ...

Latest version of open source Java In-Memory Data Grid Hazelcast supports entry processing, multi-thread execution, continuous queries and lazy indexing. They have also re-implemented all of the ...

Natural language queries made to the database are routed through ChatGPT's API to produce a SQL query syntax that can be run to generate results, Kinetica says.

With POLAR, the research group introduces an adaptive query processing technique that lowers the adoption barrier for existing database systems while decreasing the risk of performance cliffs from ...

With its ability to convert natural language questions into SQL queries, the addition of ChatGPT to the high-performance, real-time Kinetica database makes it possible for business users to obtain ...

Storing data in Parquet format on cloud object storage (such as AWS S3) has become mainstream, widely used in scenarios such ...

Blockchain can be inefficient in terms of data processing and query performance. Source: MDPI Another reason data queries are inefficient is the linear storage of data in blockchain networks.