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The light detection and ranging (LiDAR) technology allows to sense surrounding objects with fine-grained resolution in a large areas. Their data (aka point clouds), generated continuously at very high rates, can provide information to support automated functionality in cyberphysical systems. Clustering of point clouds is a key problem to extract this type of information. Methods for solving the problem in a continuous fashion can facilitate improved processing in e.g. fog architectures, allowing continuous, streaming processing of data close to the sources. We propose Lisco, a singlepass continuous Euclidean-distance-based clustering of LiDAR point clouds, that maximizes the granularity of the data processing pipeline. Besides its algorithmic analysis, we provide a thorough experimental evaluation and highlight its up to 3x improvements and its scalability benefits compared to the baseline, using both real-world datasets as well as synthetic ones to fully explore the worst-cases.

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This paper presents our solution to the DEBS 2015 Grand Challenge. The analysis of the Grand Challenge is partitioned among an arbitrary number of processing units by leveraging ScaleGate, a recently proposed abstract data type with its concurrent implementation which articulates data access in parallel data streaming. ScaleGate aims not only at supporting high throughput and low latency parallel streaming analysis, but also at guaranteeing deterministic processing, which is one of the biggest challenges in parallelizing computation while maintaining consistency.

We briefly describe our study on the problem of streaming multiway aggregation [5], where large data volumes are received from multiple input streams. Multiway aggregation is a fundamental computational component in data stream management systems, requiring low-latency and high throughput solutions. We focus on the problem of designing concurrent data structures enabling for low-latency and highthroughput multiway aggregation; an issue that has been overlooked in the literature. We propose two new concurrent data structures and their lock-free linearizable implementations, supporting both order-sensitive and order-insensitive aggregate functions. Results from an extensive evaluation show significant improvement in the aggregation performance, in terms of both processing throughput and latency over the commonly-used techniques based on queues.