In today's society, the use of mobile devices is increasing dramatically. The majority of mobile devices, initially used for telecommunications, have now been enhanced to support business needs and growth. The use of mobile devices in businesses has led to the creation of m-business/m-commerce. The increasing number of mobile device users creates a large amount of useful data for service providers. These data are valuable and can help the business with further developments and strategies if they are properly analysed.
The success of an m-business depends on the ability to deliver attractive products or services that are personalized to the individual user at the right location at the right time. These information- intensive services can only be obtained by collecting and analysing the combined demographic, geographic, and temporal information. The challenge for mobile service providers is to manage the overwhelming data that they are accumulating every day and apply data mining tools effectively to transform those data into useful information that can not be seen with traditional reporting techniques and tools. Data mining enables the user to seek out facts by identifying patterns within data. Data mining can give businesses the edge over other businesses by increasing competitiveness , in the form of marketing that is more focused on particular consumer groups or by suggesting the better use of mobile technology.
Existing applications of data mining in regards to m-business include such works as MobiMine, which enables a user to monitor stock prices from a handheld PDA. Applications like this will increase dramatically within the near future to accommodate the need for business expansion and optimisation . An investment in a data warehouse and a data mining tool is costly but can help the m-business to provide the right services to the right people at right time, thus proving to support decision making, increase customer satisfaction, and aid in marketing.
In this chapter, we explored examples of usage and the process of data mining in the m-business domain. We also discussed some of the forthcoming problems in applying data mining in the m-business domain and their possible solutions.