An adaptive data cleaning scheme for reducing false negative reads in RFID data streams

2012 ◽  
Author(s):  
Libe Valentine Massawe ◽  
Herman Vermaak ◽  
Johnson D. M. Kinyua
Keyword(s):  
Data Streams ◽  
Data Cleaning ◽  
False Negative ◽  
Rfid Data

scholarly journals Reducing False Negative Reads in RFID Data Streams Using an Adaptive Sliding-Window Approach

Sensors ◽  
2012 ◽  
Vol 12 (4) ◽  
pp. 4187-4212 ◽  
Author(s):  
Libe Valentine Massawe ◽  
Johnson D. M. Kinyua ◽  
Herman Vermaak
Keyword(s):  
Data Streams ◽  
False Negative ◽  
Sliding Window ◽  
Rfid Data ◽  
Window Approach

Efficient RFID Data Cleaning Model Based on Dynamic Clusters of Monitored Objects

2010 ◽  
Vol 21 (4) ◽  
pp. 632-643 ◽  
Author(s):  
Yu GU ◽  
Ge YU ◽  
Xiao-Long HU ◽  
Yi WANG
Keyword(s):  
Data Cleaning ◽  
Model Based ◽  
Rfid Data

scholarly journals A RFID Data-Cleaning Algorithm Based on Communication Information among RFID Readers

2015 ◽  
Vol 8 (1) ◽  
pp. 155-164
Author(s):  
Yunhua Gu ◽  
Bao Gao ◽  
Jin Wang ◽  
Mingshu Yin ◽  
Junyong Zhang
Keyword(s):  
Data Cleaning ◽  
Rfid Data ◽  
Rfid Readers

scholarly journals IoT Based Decision Making System to Improve Veracity of Big Data

2018 ◽  
Vol 7 (3.1) ◽  
pp. 63 ◽  
Author(s):  
R Revathy ◽  
R Aroul Canessane
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Data Streams ◽  
Data Cleaning ◽  
Data Handling ◽  
Secure Data ◽  
Decision Making System ◽  
Data Investigation ◽  
Impact Data

Data are vital to help decision making. On the off chance that data have low veracity, choices are not liable to be sound. Internet of Things (IoT) quality rates big data with error, irregularity, deficiency, trickery, and model guess. Improving data veracity is critical to address these difficulties. In this article, we condense the key qualities and difficulties of IoT, which impact data handling and decision making. We audit the scene of estimating and upgrading data veracity and mining indeterminate data streams. Also, we propose five suggestions for future advancement of veracious big IoT data investigation that are identified with the heterogeneous and appropriated nature of IoT data, self-governing basic leadership, setting mindful and area streamlined philosophies, data cleaning and handling procedures for IoT edge gadgets, and protection safeguarding, customized, and secure data administration.  

Online Pattern Aggregation over RFID Data Streams

2010 ◽  
pp. 262-273 ◽  
Author(s):  
Hailong Liu ◽  
Zhanhuai Li ◽  
Qun Chen ◽  
Shanglian Peng
Keyword(s):  
Data Streams ◽  
Rfid Data

False-Negative Frequent Items Mining from Data Streams with Bursting

2005 ◽  
pp. 422-434 ◽  
Author(s):  
Zhihong Chong ◽  
Jeffrey Xu Yu ◽  
Hongjun Lu ◽  
Zhengjie Zhang ◽  
Aoying Zhou
Keyword(s):  
Data Streams ◽  
False Negative ◽  
Frequent Items
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