Computing aggregates over windows is at the core of virtually every stream processing job. Typical stream processing applications involve overlapping windows and, therefore, cause redundant computations. Several techniques prevent this redundancy by sharing partial aggregates among windows. However, these techniques do not support out-of-order processing and session windows. Out-of-order processing is a key requirement to deal with delayed tuples in case of source failures such as temporary sensor outages. Session windows are widely used to separate different periods of user activity from each other. In this paper, we present Scotty, a high throughput operator for window discretization and aggregation. Scotty splits streams into non-overlapping slices and computes partial aggregates per slice. These partial aggregates are shared among all concurrent queries with arbitrary combinations of tumbling, sliding, and session windows. Scotty introduces the first slicing technique which (1) enables stream slicing for session windows in addition to tumbling and sliding windows and (2) processes out-of-order tuples efficiently. Our technique is generally applicable to a broad group of dataflow systems which use a unified batch and stream processing model. Our experiments show that we achieve a throughput an order of magnitude higher than alternative stateof-the-art solutions.

The Internet of Things (IoT) presents a novel computing architecture for data management: a distributed, highly dynamic, and heterogeneous environment of massive scale. Applications for the IoT introduce new challenges for integrating the concepts of fog and cloud computing as well as sensor networks in one unified environment. In this paper, we present early approaches that address parts of the overall problem space. All approaches are incorporated into NebulaStream (NES), our novel data processing platform that addresses the heterogeneity, unreliability, and scalability challenges of the IoT and thus provides efficient data management for future applications.

The efficient processing of video streams is a key component in many emerging Internet of Things (IoT) and edge applications, such as Virtual and Augmented Reality (V/AR) and self-driving cars. These applications require real-time high-throughput video processing. This can be attained via a collaborative processing model between the edge and the cloud-called an Edge-Cloud model. To this end, many approaches were proposed to optimize the latency and bandwidth consumption of Edge-Cloud video processing, especially for Neural Networks (NN)-based methods. In this demonstration. We investigate the efficiency of these NN techniques, how they can be combined, and whether combining them leads to better performance. Our demonstration invites participants to experiment with the various NN techniques, combine them, and observe how the underlying NN changes with different techniques and how these changes affect accuracy, latency and bandwidth consumption.

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Evaluating modern stream processing systems in a reproducible manner requires data streams with different data distributions, data rates, and real-world characteristics such as delayed and outof-order tuples. In this paper, we present an open source stream generator which generates reproducible and deterministic out-oforder streams based on real data files, simulating arbitrary fractions of out-of-order tuples and their respective delays. CCS CONCEPTS • Information systems → Stream management.