Database optimization has long relied on traditional methods that struggle with the complexities of modern data environments. These methods often fail to efficiently handle large-scale data, complex ...

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results. In ...

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Newport News officials are nearly ready to present their plans for data center development. But they are still fine-tuning how they will pitch the idea to residents and the business community to ...

AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology ...

What it takes to deliver on data and AI strategy. In partnership withDatabricks Four years is a lifetime when it comes to artificial intelligence. Since the first edition of this study was published ...

Marketing, technology, and business leaders today are asking an important question: how do you optimize for large language models (LLMs) like ChatGPT, Gemini, and Claude? LLM optimization is taking ...

Abstract: This research paper investigates the transformative transition from traditional database management systems (DBMS) to self-tuning databases that automate performance optimization, ...

The explosive growth of data volume and high-concurrency demands in big data systems exposes critical limitations in traditional database connection modes and access mechanisms. Traditional approaches ...