Brief Announcement: Concurrent Data Structures for Efficient Streaming Aggregation

We briefly describe our study on the problem of streaming multiway aggregation [5], where large data volumes are re-ceived from multiple input streams. Multiway aggregation is a fundamental computational component in data stream management systems, requiring low-latency and high through-put solutions. We focus on the problem of designing con-current data structures enabling for low-latency and high-throughput multiway aggregation; an issue that has been overlooked in the literature. We propose two new concurrent data structures and their lock-free linearizable implementa-tions, 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