Yiqun Diao, Yutong Yang, Qinbin Li, Bingsheng He, Mian Lu

Relational datasets are widespread in real-world scenarios and are usually delivered in a streaming fashion. This type of data stream can present unique challenges, such as distribution drifts, outliers, emerging classes, and changing features, which have recently been described as open environment challenges for machine learning. While some work has been done on incremental learning for data streams, their evaluations are mostly conducted with manually partitioned datasets. Moreover, while several real-world streaming datasets are available, it is uncertain whether these open environment challenges are prevalent and how existing incremental learning algorithms perform on real datasets. To fill this gap, we develop an Open Environment Benchmark named OEBench to evaluate open environment challenges in relational data streams. Specifically, we investigate 55 real-world streaming datasets and establish that open environment scenarios are indeed widespread in real-world datasets, which presents significant challenges for stream learning algorithms. Through benchmarks, we find that increased data quantity may not consistently enhance the model accuracy when applied in open environment scenarios, where machine learning models can be significantly compromised by distribution shifts, anomalies, or untrustworthy data within real-world data streams. The current techniques are insufficient in effectively mitigating these challenges posed by open environments. Thus, it is promising to conduct more researches to address real-world new challenges of open environment scenarios.