Synopsis for microbiological data stream analysis

Gianfranco Cellarosi, Claudio Sartori
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引用次数: 1

Abstract

This paper derives from the extensive analysis of the results of microbiological laboratory output in a large hospital. In this environment data items are automatically collected by transactions linked to the analysis devices and the data flow rate can be very high. In addition, the time of the data items is extremely relevant, since a variation of the number of positive findings can be generated by dangerous events, such as outbreaks. The typical setting of data mining applications is to process data, in search of some hidden information. Unfortunately in this environment, we have many transactions and everyone of these is a potentially important information. When the data set changes, some kind of re-computation, either from scratch or according to differences only, has to be done. Things change when the change rate increases, so as to make difficult to process changes before new changes arrive. We define this particular situation as a data stream, and we devise a framework allowing efficient analysis of data streams. We propose a base set of techniques, which can be used to analyze data streams in Euclidean space, putting together information processing and statistical techniques. In particular, we are interested in detecting "alarms" in microbiological time series, that is points in time where the detected data differ from the values expected on the basis of past history. We provide also experimental results, based on real and data.
微生物数据流分析摘要
本文来源于对某大型医院微生物实验室输出结果的广泛分析。在此环境中,数据项由链接到分析设备的事务自动收集,数据流速率可能非常高。此外,数据项目的时间极为相关,因为诸如疾病暴发等危险事件可能导致阳性结果数量的变化。数据挖掘应用程序的典型设置是处理数据,以寻找一些隐藏的信息。不幸的是,在这个环境中,我们有很多事务,每个事务都是潜在的重要信息。当数据集发生变化时,必须进行某种重新计算,要么从头开始计算,要么只根据差异进行计算。当变化率增加时,事物会发生变化,因此在新变化到来之前很难处理变化。我们将这种特殊情况定义为数据流,并设计了一个允许对数据流进行有效分析的框架。我们提出了一套基本的技术,将信息处理和统计技术结合在一起,可以用于分析欧几里得空间中的数据流。我们特别感兴趣的是检测微生物时间序列中的“警报”,即检测到的数据与基于过去历史的预期值不同的时间点。我们还提供了基于实际和数据的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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