As architecture, system, data management, and machine learning communities pay greater attention to innovative big data and data-driven artificial intelligence (in short, AI ) algorithms, architecture, and systems , the pressure of benchmarking rises. However , complexity, diversity, frequently changed workloads, and rapid evolution of big data , especially AI systems raise great challenges in benchmarking .
<coherence> As architecture, system, data management, and machine learning communities pay greater attention to innovative big data and data-driven artificial intelligence (in short, AI ) algorithms, architecture, and systems , the pressure of benchmarking rises. However , complexity, diversity, frequently changed workloads, and rapid evolution of big data , especially AI systems raise great challenges in benchmarking .
As architecture, system, data management, and machine learning communities pay greater attention to innovative big data and data-driven artificial intelligence (in short, AI ) algorithms, architecture, and systems . Unfortunately , complexity, diversity, frequently changed workloads, and rapid evolution of big data , especially AI systems raise great challenges in benchmarking .