Research on Data Mining Models for the Internet of Things

Data mining is the process of sorting large data sets to identify patterns and establish relationships for solving problems through data analysis. A data mining model is created by applying an algorithm to data.

Some students of Zhejiang University have done a research on Data Mining Models for the Internet of Things (IoT). They have defined the IoT as the next generation of internet which contains trillions of nodes representing various objects from small ubiquitous sensor devices and handhelds to large web servers and super computer clusters.

They have mentioned four data models for the Internet of Things.

• Multi-layer data mining models.
• Distributed data mining model.
• Grid based data mining model.
• Data mining model from multi-technology integration perspective.

Multi-layer data mining model – They have shown four layers for the multi-layer data mining model.

• Data collection layer
• Data management layer
• Event processing layer
• Data mining service layer

Data collection layer adopts devices to collect various smart object’s data.

Data management layer applies centralized or distributed database or data warehouse to manage collected data.

Event processing layer analyses events in IoT effectively.

Data mining service layer is built based on data management and event processing. It is self-oriented.

Distributed data mining model for IoT solves the problems brought by distributed storage of nodes and decomposes the complex problems into simple problems.

Grid based data mining model supports to implement the data mining operations for IoT.

Data mining model for IoT from multi-technology integration perspective is shown in the following figure.

They have given six key issues in data mining of IoT.

They are,

• Data collection from smart objects of IoT - When conducting data collection from smart object of IoT, the special needs of smart objects should be taken into account.
• Data abstraction, compression, index, aggregation and multi-dimensional query – Since the internet of things produces a massive data of smart objects. It is better to consider how to manage data of IoT effectively and how to implement online analytical query and processing conveniently.
• Event filtering, aggregation and detection – Event filtering and complex event processing process simple events in data. First, data are aggregated accordingly to events. While primitive events are filtered, valuable events are obtained. Then the simple events are integrated into complex events.
• Centralized data processing and mining Vs. Distributed data processing and mining – centralized or distributed data processing and mining models can be adopted flexibility in different situations.
• Research on data mining algorithms for IoT – A key issue is to study the novel data mining algorithms for IoT. The main tasks are classification, forecasting, clustering, outlier detection, association analysis, spatial and temporal patterns mining for IoT.
• Data mining towards the next generation of internet – The next generation of internet has many potential direction of development such as IPv6, ubiquitous networks, trusted network, semantic web, grid, service oriented applications, optical transmission, cloud computing etc. These technologies will integrate with IoT. In that case, many new data mining problems need to be studied intensively.

In a nut shell, in their research paper, they have defined what data mining models are and what IoT means. Also they have given four models of data mining and among them, the multi-layer model has four layers. Then they have discussed the key issues in data mining of IoT.

Reference:

Shen Bin, Liu Yuan, Wang Xiaoyi, Ningbo Institute of Technology, Zhejiang University, Ningbo, China, College of Management, Zhejiang University, Hangzhou, China. “Research on Data Mining Models for the Internet of Things”