English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Sliding-window aggregation is a foundational stream processing primitive thatefficiently summarizes recent data. The state-of-the-art algorithms forsliding-window aggregation are highly efficient when stream data items areevicted or inserted one at a time, even when some of the insertions occurout-of-order. However, real-world streams are often not only out-of-order butalso burtsy, causing data items to be evicted or inserted in larger bulks. Thispaper introduces a new algorithm for sliding-window aggregation with bulkeviction and bulk insertion. For the special case of single insert and evict,our algorithm matches the theoretical complexity of the best previousout-of-order algorithms. For the case of bulk evict, our algorithm improvesupon the theoretical complexity of the best previous algorithm for that caseand also outperforms it in practice. For the case of bulk insert, there are noprior algorithms, and our algorithm improves upon the naive approach ofemulating bulk insert with a loop over single inserts, both in theory and inpractice. Overall, this paper makes high-performance algorithms for slidingwindow aggregation more broadly applicable by efficiently handling theubiquitous cases of out-of-order data and bursts.

Out-of-Order Sliding-Window Aggregation with Efficient Bulk Evictions and Insertions (Extended Version)