Nowadays, the increased amount of users’ devices produce huge volumes of data that should be efficiently managed by modern applications. Streams are adopted to deliver data that, usually, are stored into a number of partitions. Splitting the data offers a lot of advantages as applications can process them in parallel, thus, they increase the speed of processing. Progressive analytics are also adopted to deliver partial responses, during processing, thus, saving time in the execution of applications. Data exploration and analytics queries are very significant for future applications. Usually, such queries demand for an ordered set of objects as a response and require intelligent predictive schemes to deliver the responses on top of the partial results retrieved by the distributed data partitions. A finite set of query processors are adopted to produce these partial results. Processors are placed in front of each partition and report progressive analytics to a central entity. In this paper, we envision the query controller (QC) as the central entity that collects progressive analytics and return the final response to users/applications. The QC receives partial ordered sets of objects and aggregates them to derive the final outcome. We focus on a QC that applies time-optimized techniques and aggregation operators to deliver every response, i.e., ordered sets, over streams of partial ordered subsets. We perform a comprehensive performance assessment with synthetic data and report on the performance of the QC. Our experimental evaluation reveals the pros and cons of the proposed model and a comparison assessment places this paper in the respective literature.

中文翻译：

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部分有序子集的分析聚合中的预测智能

如今，越来越多的用户设备产生了大量数据，这些数据应该由现代应用程序有效管理。采用流来传递数据，这些数据通常存储在多个分区中。拆分数据提供了很多优势，因为应用程序可以并行处理它们，从而提高处理速度。在处理过程中，还采用渐进式分析来提供部分响应，从而节省应用程序的执行时间。数据探索和分析查询对于未来的应用非常重要。通常，此类查询需要一组有序的对象作为响应，并且需要智能预测方案以在分布式数据分区检索到的部分结果之上交付响应。采用一组有限的查询处理器来产生这些部分结果。处理器位于每个分区的前面，并向中央实体报告渐进式分析。在本文中，我们将查询控制器 (QC) 设想为收集渐进分析并将最终响应返回给用户/应用程序的中央实体。QC 接收部分有序的对象集并聚合它们以得出最终结果。我们专注于应用时间优化技术和聚合运算符的 QC，以通过偏序子集流传递每个响应，即有序集。我们使用合成数据进行综合性能评估，并报告 QC 的性能。