Abstract
Internet of Things (IoT) has been the driving force for many smart city applications. The huge volume of IoT data generated from these applications require efficient processing to get the insight, which poses significant difficulty. Data mining and machine learning (DM) algorithms are used to minimize such difficulty. However, it is still very challenging to select a particular DM algorithm that can process a dynamic IoT dataset based on some application-specific goals to achieve better accuracy. This paper proposes a knowledge-driven framework that considers the knowledge of datasets, available DM algorithms, and application goals to select the suitable DM algorithm for performing a target data processing task. This work considers data from cultural domain, health domain, and transportation domain in the experiment. The results show that the proposed approach dynamically selects the best-suited DM algorithms for the available datasets and target goals that exhibits satisfactory performance in obtaining accurate results compared to the existing work. The proposed approach not only provides flexibility in conducting dynamic IoT data mining tasks, but also reduces the complexity that would otherwise be necessary while adopting the traditional data mining approaches.
Highlights
The advancement of Internet of Things (IoT) in the last few years has promoted its adoption in diverse application areas including healthcare, transportation, industry management, and smart cities [1]–[3]
The reason behind choosing this selection is that in this work similarity between web services has been calculated considering the services as datasets and the searches used semantic and context matching, which is important for heterogeneous IoT data mining
The knowledge attributes specified for G, D, and A can be updated, which better satisfy the dynamic context of IoT data mining
Summary
The advancement of IoT in the last few years has promoted its adoption in diverse application areas including healthcare, transportation, industry management, and smart cities [1]–[3]. The machine learning approach, which is representing the method of data mining has been integrated with IoT to unleash insights of knowledge patterns with commonly practiced supervised, unsupervised and semi-supervised method [5]. Data mining uses a plenty of established mechanisms for data processing, clustering and classification; contemporary research still lacks in providing an autonomous and robust approach to select an optimal DM algorithm for the target dataset. This has statistical variation and changeable properties [8], [11], which make the dynamic DM algorithm selection problem challenging in IoT domain.
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