Over the past two decades the data management community has devoted particular attention to handling data that arrives as a stream of updates. This captures a number of "big data" scenarios, ranging from monitoring networks to processing high volumes of transactions in commerce and finance. This has led to data streams becoming a mainstream data management topic, with many systems offering explicit support for handling such inputs. Within these systems, streaming algorithms are used to approximate various statistical and modeling queries, which would traditionally require random access to the full data to compute exactly.

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技术视角

在过去的二十年里，数据管理社区特别关注处理作为更新流到达的数据。这捕获了许多“大数据”场景，从监控网络到处理大量商业和金融交易。这导致数据流成为主流的数据管理主题，许多系统都为处理此类输入提供了明确的支持。在这些系统中，流式算法用于近似各种统计和建模查询，传统上这些查询需要随机访问完整数据才能精确计算。