Data Mining for the Internet of Things: Literature Review and Challenges

2021 ◽  
Vol 9 (VII) ◽  
pp. 3351-3362
Author(s):  
Dr. Mohd Zuber
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
The Internet ◽  
Mining System ◽  
Research Issues ◽  
Data Mining Algorithms ◽  
Huge Data ◽  
Mining Algorithms ◽  
The Internet Of Things

The huge data generate by the Internet of Things (IOT) are measured of high business worth, and data mining algorithms can be applied to IOT to take out hidden information from data. In this paper, we give a methodical way to review data mining in knowledge, technique and application view, together with classification, clustering, association analysis and time series analysis, outlier analysis. And the latest application luggage is also surveyed. As more and more devices connected to IOT, huge volume of data should be analyzed, the latest algorithms should be customized to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

Data Mining for the Internet of Things

2018 ◽  
pp. 181-191 ◽  
Author(s):  
Akhil Rajendra Khare ◽  
Pallavi Shrivasta
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
System Optimization ◽  
The Internet ◽  
Mining System ◽  
Data Mining Algorithms ◽  
The World ◽  
Data Mining System ◽  
Mining Algorithms ◽  
The Internet Of Things

The Internet of Things concept arises from the need to manage, automate, and explore all devices, instruments and sensors in the world. In order to make wise decisions both for people and for the things in IoT, data mining technologies are integrated with IoT technologies for decision making support and system optimization. Data mining involves discovering novel, interesting, and potentially useful patterns from data and applying algorithms to the extraction of hidden information. Data mining is classified into three different views: knowledge view, technique view, and application view. The challenges in the data mining algorithms for IoT are discussed and a suggested big data mining system is proposed.

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

Big Data in manufacturing systems engineering – close up on a machine tool

2016 ◽  
Vol 64 (7) ◽  
Author(s):  
Christian Bauer ◽  
Zaigham-Faraz Siddiqui ◽  
Manuel Beuttler ◽  
Klaus Bauer
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
Systems Engineering ◽  
Machine Tools ◽  
Manufacturing Systems ◽  
The Internet ◽  
New Approach ◽  
New Challenges ◽  
The Internet Of Things

AbstractWith the increasing connectivity of devices, the amount of data that is recorded and ready for analysis is growing correspondingly. This is also the case for shop floors in flexible sheet metal handling and production. With the growing need for flexibility in production, the availability of machine tools is imminent. This paper shows different approaches that a classical manufacturing systems company such as TRUMPF takes in applying data mining techniques to address the new challenges which come with the Internet of things. In addition to classical methods, a new approach is introduced that does not need any alteration of the machine or its interfaces.

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