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Weighted or Non-Weighted Negative Tree Pattern Discovery from SensorRich Environments

Juryon Paik*

# 521, 2nd Pierson Bldg., Department of Digital Information & Statistics, Pyeongtaek University, 3825, Seodong-daro, Pyeongtaek-si, Gyeonggi-do 17869, South Korea

* Corresponding Author: Juryon Paik, email

Intelligent Automation & Soft Computing 2020, 26(1), 193-204.


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.


Cite This Article

J. Paik, "Weighted or non-weighted negative tree pattern discovery from sensorrich environments," Intelligent Automation & Soft Computing, vol. 26, no.1, pp. 193–204, 2020.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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