用范畴论统一解释时间序列与图像聚合，支持高效并行计算。
Aggregating time-series and image data: functors and double functors
- 将数据聚合视为范畴上的函子，构建统一数学框架。
- 可直接扩展Blelloch扫描算法实现并行化，提升计算效率。
- 适用于需要高效聚合的时序与图像数据处理场景。
对定义域子集上的时间序列或图像数据进行聚合是数据科学中的基础任务。我们证明，许多已知的聚合操作可被解释为在适当双范畴（double categories）上的（双）函子。此类函子式聚合可通过Blelloch并行扫描算法的直接扩展实现并行化。除了提供对现有操作的统一视角外，该框架还允许我们为时间序列和图像数据提出新的聚合操作。
原文摘要 · Abstract (English)
Aggregation of time-series or image data over subsets of the domain is a fundamental task in data science. We show that many known aggregation operations can be interpreted as (double) functors on appropriate (double) categories. Such functorial aggregations are amenable to parallel implementation via straightforward extensions of Blelloch's parallel scan algorithm. In addition to providing a unified viewpoint on existing operations, it allows us to propose new aggregation operations for time-series and image data.
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