Open Access
ARTICLE
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,
Intelligent Automation & Soft Computing 2020, 26(1), 193-204. https://doi.org/10.31209/2019.100000140
Abstract
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.
Keywords
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. https://doi.org/10.31209/2019.100000140