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Weighted or Non-Weighted Negative Tree Pattern Discovery from Sensor-Rich Environments

机译:在传感器丰富的环境中发现加权或非加权负树模式

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摘要

It seems to be sure that the IoT is one of promising potential topics today. Sensors are the one that lead the current IoT revolution. The advances of sensor-rich environments produce the massive volume of raw data that is enlarging faster than the rate at which it is being handled. JSON is a lightweight data-interchange format and preferred for IoT applications. Before JSON, XML was de factor standard format for interchanging data. The common point is that their structure scheme is the tree. Tree structure provides data exchangeability and heterogeneity, which encourages user-flexibilities. Therefore, JSON sensor format is an easy to use human readable format for storing and transmitting sensor values. However, it is more challenging than ever to discover valuable and hidden information from the continuously generated tree-structured data. In the paper, we define and suggest an original method to predict and evaluate from the tree-structured sensing data.
机译:似乎可以肯定,物联网是当今有希望的潜在主题之一。传感器是引领当前物联网革命的一种。传感器丰富的环境的进步产生了大量原始数据,这些原始数据的增长速度快于处理速度。 JSON是一种轻量级的数据交换格式,是IoT应用程序的首选。在JSON之前,XML是交换数据的标准格式。共同点是它们的结构方案是树。树结构提供了数据可交换性和异构性,从而提高了用户灵活性。因此,JSON传感器格式是一种易于使用的人类可读格式,用于存储和传输传感器值。但是,从连续生成的树状结构数据中发现有价值的隐藏信息比以往任何时候都更具挑战性。在本文中,我们定义并提出了一种从树状结构传感数据进行预测和评估的原始方法。

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