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Event-time based stream processing is concerned with analyzing data with respect to its generation time. In most of the cases, data gets delayed during its journey from the source(s) to the stream processing engine. This is known as late data arrival. Among the different approaches for out-of-order stream processing, low watermarks are proposed to inject special records within data streams, i.e., watermarks. A watermark is a timestamp which indicates that no data with a timestamp older than the watermark should be observed later on. Any element as such is considered a late arrival. Watermark generation is usually periodic and heuristic-based. The limitation of such watermark generation strategy is its rigidness regarding the frequency of data arrival as well as the delay that data may encounter. In this paper, we propose an adaptive watermark generation strategy. Our strategy decides adaptively when to generate watermarks and with what timestamp without a priori adjustment. We treat changes in data arrival frequency and changes in delays as concept drifts in stream data mining. We use an Adaptive Window (ADWIN) as our concept drift sensor for the change in the distribution of arrival rate and delay. We have implemented our approach on top of Apache Flink. We compare our approach with periodic watermark generation using two real-life data sets. Our results show that adaptive watermarks achieve a lower average latency by triggering windows earlier and a lower rate of dropped elements by delaying watermarks when out-of-order data is expected.

Adaptive Watermarks - A Concept Drift-based Approach for Predicting Event-Time Progress in Data Streams.