This section discusses some existing data mining works within the m-business domain.
MobiMine ‚ Monitoring the Stock Market From a PDA
MobiMine is an experimental mobile data mining system which enables intelligent monitoring of time-critical financial data from a handheld PDA. The system consists of several modules, with some of the modules utilising data mining techniques. The data mining component of the system employs a novel Fourier-analysis-based approach to efficiently represent, visualize, and communicate decision trees over limited bandwidth wireless networks (Kargupta et al., 2002; Kargupta, Sivakumar, & Ghosh, 2002).
MobiMine is a client-server application. The clients running mobile devices likehandheld PDAs and cell phones monitor a stream of financial data coming through the MobiMine server. The MobiMine server and client apply several advanced data mining techniques to offer the user a variety of different tools to monitor the stock market at any time from anywhere . The server collects stock market data from different sources on the Web and processes it on a regular basis. It employs several data mining techniques to sift through the data. MobiMine makes use of a collection of online mining techniques, including several statistical algorithms, clustering, Bayesian nets , and decision trees. The StockConnection module uses online statistical Fourier-spectrum-based decision trees and Bayesian learning techniques for detecting the interaction among the active stocks and the portfolio. The StockNuggets module applies a collection of different online clustering algorithms for identifying interesting stocks that are influenced by the current active stocks.
Game Usage Mining: Knowledge Discovery in Massive Multiplayer Games
Computer games are still one of the most demanded applications in the electronic environment. The primary purpose of data mining in games is to identify different patterns of behaviour, structure, or content in order to improve the overall game play. With this data, organisations can pinpoint areas that need improvement to increase player satisfaction. In return, this will increase revenue and reputation. Wireless Internet revenue models are either proportional to money per time spent by each player (e.g., WAP over GSM) or money per byte served to the player (e.g., UMTS, I-Mode, and WAP over GPRS; Tveit & Tveit, 2002).
There are three different main types of data mining approaches referring to wireless games (Tveit & Tveit, 2002):
Game content mining ‚ discovery of patterns in multimedia or textual content in games (e.g., room layout)
Game structure mining ‚ discovery of structural patterns in the form of paths and connections binding the game world together (e.g., hallways between rooms)
Game usage mining ‚ discovery of human and virtual player behaviour patterns
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.