MOD

Machine learning algorithms have become increasingly prevalent in multiple domains, such as autonomous driving, healthcare, and finance. In such domains, data preparation remains a significant challenge in developing accurate models, requiring ...

We propose a versatile approach to lightweight, approximate query processing by learning compact but tunably precise representations of larger quantities of original tuples, coined bubbles. Instead of working with tables of tuples, the query ...

Due to the significant increase in the size of spatial data, it is essential to use distributed parallel processing systems to efficiently analyze spatial data. In this paper, we first study learned spatial data partitioning, which effectively assigns ...

In this paper, we present our vision of OmniscientDB, a novel database that leverages the implicitly-stored knowledge in large language models to augment datasets for analytical queries or even machine learning tasks. OmiscientDB empowers its users to ...

This paper addresses the challenge of predicting the level of parallelism in distributed stream processing (DSP) systems, which are essential to deal with different high workload requirements of various industries such as e-commerce, online gaming, ...

Adversarial attack has cast a shadow on the massive success of deep neural networks. Despite being almost visually identical to the clean data, the adversarial images can fool deep neural networks into the wrong predictions with very high confidence. ...