The detection of anomalies in time series has gained ample academic and industrial
attention. However, no comprehensive benchmark exists to evaluate time-series anomaly …

Access to high-quality data repositories and benchmarks have been instrumental in
advancing the state of the art in many experimental research domains. While advanced …

Artificial intelligence (AI) and machine learning (ML) techniques have existed for years, but
new hardware trends and advances in model training and inference have radically improved …

Multi-model databases support different NoSQL data models at once, typically a
combination of relational, key-value, document, and graph models. Their expected benefits …

Despite their resource-and power-constrained nature, edge devices also exhibit an increase
in the available compute and memory resources and heterogeneity, similar to the evolution …

TPCx-AI is the latest TPC benchmark addressing some of the numerous challenges in AI
benchmarking. It has the ability to scale datasets, to emulate machine learning and deep …

In this demo, we introduce Exathlon-a new benchmarking platform for explainable anomaly
detection over high-dimensional time series. We designed Exathlon to support data …

AI is continuing to emerge as an important workload across enterprise and academia.
Benchmarking is an essential tool to understand its computational requirements and to …

Training of neural networks requires often high computational power and large memory on
Graphics Processing Unit (GPU) hardware. Many cloud providers such as Ama zon, Azure …

With the fast pace of innovation in deep learning (DL) and machine learning (ML)
algorithms, large datasets, and capable hardware, real-world applications are adopting …