Kulin Merima, Fortuna Carolina, De Poorter Eli, Deschrijver Dirk, Moerman Ingrid
Department of Information Technology, Ghent University-iMinds, Technologiepark-Zwijnaarde 15, Gent 9052, Belgium.
Sensors (Basel). 2016 Jun 1;16(6):790. doi: 10.3390/s16060790.
Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.
数据科学或“数据驱动的研究”是一种利用实际数据来深入了解系统行为的研究方法。它能够分析小型、简单以及大型和更复杂的系统，以评估它们是否按照预期设计运行以及是否如模拟中所示。数据科学方法已成功应用于分析多个研究领域中的网络交互，如大规模社交网络、先进的商业和医疗保健流程。无线网络可能会在多个协议层的算法之间、多个设备之间以及硬件特定影响方面表现出不可预测的交互。这些交互可能导致实际运行与设计时运行之间存在差异。数据科学方法有助于检测实际行为并可能有助于纠正它。数据科学在无线研究中的应用越来越广泛。为了支持无线网络中的数据驱动研究，本文阐述了从原始数据轨迹中提取知识时必须应用的逐步方法。为此，本文（i）阐明了何时、为何以及如何在无线网络研究中使用数据科学；（ii）提供了一个在无线网络中应用数据科学的通用框架；（iii）概述了在无线网络中利用数据科学方法的现有研究论文；（iv）通过一个广泛的示例说明了整体知识发现过程，在该示例中根据设备的流量模式识别设备类型；（v）为读者提供必要的数据集和脚本，以便他们自己完成教程步骤。
