The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One
application domain is data science. New techniques in automating the creation of AI, known …

The development of AI applications is a multidisciplinary effort, involving multiple roles
collaborating with the AI developers, an umbrella term we use to include data scientists and …

We explore trust in a relatively new area of data science: Automated Machine Learning
(AutoML). In AutoML, AI methods are used to generate and optimize machine learning …

Data science and machine learning (DS/ML) are at the heart of the recent advancements of
many Artificial Intelligence (AI) applications. There is an active research thread in AI,\autoai …

Information extraction (IE) approaches often play a pivotal role in text analysis and require
significant human intervention. Therefore, a deeper understanding of existing IE practices …

Business process redesign (BPR) is an organizational initiative for achieving competitive
multi-faceted advantages regarding business processes, in terms of cycle time, quality, cost …

There is an abundance of data, but a large volume of it is unusable. Data may be noisy,
unstructured, stored in incompatible for direct analysis medium or format, and often …

We present HYPPO, a novel system to optimize pipelines encountered in exploratory
machine learning. HYPPO exploits alternative computational paths of artifacts from past …

As AI technology continues to advance, the importance of human-AI collaboration becomes
increasingly evident, with numerous studies exploring its potential in various fields. One vital …

From a bird's eye point of view, large-scale data analytics workflows, eg, those executed in
popular tools, such as Apache Spark and Flink, are typically represented by directed acyclic …