Self-configuring data mining for ubiquitous computing

Ubiquitous computing software needs to be autonomous so that essential decisions such as how to configure its particular execution are self-determined. Moreover, data mining serves an important role for ubiquitous computing by providing intelligence to several types of ubiquitous computing applications. Thus, automating ubiquitous data mining is also crucial. We focus on the problem of automatically configuring the execution of a ubiquitous data mining algorithm. In our solution, we generate configuration decisions in a resource aware and context aware manner since the algorithm executes in an environment in which the context often changes and computing resources are often severely limited. We propose to analyze the execution behavior of the data mining algorithm by mining its past executions. By doing so, we discover the effects of resource and context states as well as parameter settings on the data mining quality. We argue that a classification model is appropriate for predicting the behavior of an algorithm?s execution and we concentrate on decision tree classifier. We also define taxonomy on data mining quality so that tradeoff between prediction accuracy and classification specificity of each behavior model that classifies by a different abstraction of quality, is scored for model selection. Behavior model constituents and class label transformations are formally defined and experimental validation of the proposed approach is also performed

Situation-aware data mining service for ubiquitous environments

The indisputable dominance of mobile and pervasive computing devices and their typical characteristics require services offered to be rethought and sometimes redesigned in order to better assist users. Considering the importance of data mining services to provide intelligence locally on devices on these environments, we propose a data mining service that adapts the embedded data mining algorithm according to situation. Resource-awareness and context-awareness are the essential features that the proposed service will have to provide. Consequently we present a model in which data mining configuration is determined based on context and resources. We separate control and functionality in order to provide more flexibility and comply with existing data mining standards. An adaptable design is attained through definition of situations and strategies. The mechanism used in definition of strategies is an important factor affecting the performance of the control part which determines the configuration of data mining algorithm. Anticipating the importance of the mechanism selection, the paper also presents comparison with three different mechanisms. We designed a situation-aware data mining service favoring adaptability and efficiency as the important features and assessed the alternative representations of its components

Research challenge of locally computed ubiquitous data mining

Advances in wireless, sensor, mobile and wearable technologies present new challenges for data mining research on providing mobile applications with intelligence. Autonomy and adaptability requirements are the two most important challenges for data mining in this new environment. In this chapter, in order to encourage the researchers on this area, we analyzed the challenges of designing ubiquitous data mining services by examining the issues and problems while paying special attention to context and resource awareness. We focused on the autonomous execution of a data mining algorithm and analyzed the situational factors that influence the quality of the result. Already existing solutions in this area and future directions of research are also covered in this chapter