Data Mining for the Internet of Things

Outline

The Internet of Things (IoT) and its relevant technologies can seamlessly integrate classical networks with networked instruments and devices. IoT has been playing an essential role ever since it appeared, which covers from traditional equipment to general household objects and has been attracting the attention of researchers from academia, industry, and government in recent years. There is a great vision that all things can be easily controlled and monitored, can be identified automatically by other things, can communicate with each other through internet, and can even make decisions by themselves. In order to make IoT smarter, lots of analysis technologies are introduced into IoT; one of the most valuable technologies is data mining.

Data mining involves discovering novel, interesting, and potentially useful patterns from large data sets and applying algorithms to the extraction of hidden information. Many other terms are used for data mining, for example, knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archaeology, data dredging, and information harvesting. The objective of any data mining process is to build an efficient predictive or descriptive model of a large amount of data that not only best fits or explains it, but is also able to generalize to new data (Figure 1). Based on a broad view of data mining functionality, data mining is the process of discovering interesting knowledge from large amounts of data stored in either databases, data warehouses, or other information repositories (Tsai, Lai & Vasilakos, 2014).

Figure 1.

The data mining overview

On the basis of the definition of data mining and the definition of data mining functions, a typical data mining process includes the following steps (Jiawei & Kamber, 2011)

Scope

We can view data mining in a multidimensional view.

• 1.

  In knowledge view or data mining functions view, it includes characterization, discrimination, classification, clustering, association analysis, time series analysis, and outlier analysis.

• 2.

  In utilized techniques view, it includes machine learning, statistics, pattern recognition, big data, support vector machine, rough set, neural networks and evolutionary algorithms.

• 3.

  In application view, it includes industry, telecommunication, banking, fraud analysis, bio data mining, stock market analysis, text mining, web mining, social network and e-commerce.

A variety of researches focusing on knowledge view, technique view and application view can be found in the literature. However, no previous effort has been made to review the different views of data mining in a systematic way, especially in nowadays big data; mobile internet and Internet of Things grow rapidly and some data mining researchers shift their attention from data mining to big data. There are lots of data that can be mined, for example, database data (relational database, No SQL database), data warehouse, data stream, spatiotemporal, time series, sequence, text and web, multimedia, graphs, the World Wide Web, Internet of Things data, and legacy system log. Motivated by this, we attempt to make a comprehensive survey of the important recent developments of data mining research. This survey focuses on knowledge view, utilized techniques view, and application view of data mining.