With the ever-increasing diffusion of smart devices and Internet of Things (IoT) applications, a completely new set of challenges have been added to the Data Mining domain. Edge Mining and Cloud Mining refer to Data Mining tasks aimed at IoT scenarios and performed according to, respectively, Cloud or Edge computing principles. Given the orthogonality and interdependence among the Data Mining task goals (e.g., accuracy, support, precision), the requirements of IoT applications (mainly bandwidth, energy saving, responsiveness, privacy preserving, and security) and the features of Edge/Cloud deployments (de-centralization, reliability, and ease of management), we propose EdgeMiningSim, a simulation-driven methodology inspired by software engineering principles for enabling IoT Data Mining. Such a methodology drives the domain experts in disclosing actionable knowledge, namely descriptive or predictive models for taking effective actions in the constrained and dynamic IoT scenario. A Smart Monitoring application is instantiated as a case study, aiming to exemplify the EdgeMiningSim approach and to show its benefits in effectively facing all those multifaceted aspects that simultaneously impact on IoT Data Mining.

中文翻译：

---

基于边缘计算的物联网数据挖掘仿真驱动方法

随着智能设备和物联网 (IoT) 应用程序的不断普及，数据挖掘领域增加了一系列全新的挑战。边缘挖掘和云挖掘是指针对物联网场景并分别根据云或边缘计算原理执行的数据挖掘任务。鉴于数据挖掘任务目标（例如，准确性、支持、精度）、物联网应用程序的要求（主要是带宽、节能、响应性、隐私保护和安全性）以及边缘/云部署的特征之间的正交性和相互依赖性（去中心化、可靠性和易于管理），我们提出了 EdgeMiningSim，这是一种模拟驱动的方法，其灵感来自软件工程原理，用于实现物联网数据挖掘。这种方法推动领域专家披露可操作的知识，即用于在受限和动态物联网场景中采取有效行动的描述性或预测性模型。将智能监控应用程序实例化为案例研究，旨在举例说明 EdgeMiningSim 方法并展示其在有效应对同时影响物联网数据挖掘的所有这些多方面方面的优势。