23 citations · 50 across the 10 of their papers we have counts for
13 papers
Out-of-Order Sliding-Window Aggregation with Efficient Bulk Evictions and Insertions (Extended Version)
Kanat Tangwongsan, Martin Hirzel, Scott Schneider
Sliding-window aggregation is a foundational stream processing primitive that efficiently summarizes recent data. The state-of-the-art algorithms for sliding-window aggregation are…
In-Order Sliding-Window Aggregation in Worst-Case Constant Time
Kanat Tangwongsan, Martin Hirzel, Scott Schneider
Sliding-window aggregation is a widely-used approach for extracting insights from the most recent portion of a data stream. The aggregations of interest can usually be expressed as…
Work-efficient Batch-incremental Minimum Spanning Trees with Applications to the Sliding Window Model
Daniel Anderson, Guy E. Blelloch, Kanat Tangwongsan
Algorithms for dynamically maintaining minimum spanning trees (MSTs) have received much attention in both the parallel and sequential settings. While previous work has given optima…
Parallel Streaming Random Sampling
Kanat Tangwongsan, Srikanta Tirthapura
This paper investigates parallel random sampling from a potentially-unending data stream whose elements are revealed in a series of element sequences (minibatches). While sampling…
Sub-O(log n) Out-of-Order Sliding-Window Aggregation
Kanat Tangwongsan, Martin Hirzel, Scott Schneider
Sliding-window aggregation summarizes the most recent information in a data stream. Users specify how that summary is computed, usually as an associative binary operator because th…
Streaming k-Means Clustering with Fast Queries
Yu Zhang, Kanat Tangwongsan, Srikanta Tirthapura
We present methods for k-means clustering on a stream with a focus on providing fast responses to clustering queries. Compared to the current state-of-the-art, our methods provide…