## 2 System Architecture  
Besides storage of plant electrophysiological data, it is also necessary to achieve a web-based real-time online computation. In fact, since the increasing demand for storage and computing from the growing amount of data, there are both computing-intensive and data-intensive tasks in the process of plant electrophysiological data analysis. Therefore, parallel computing or distributed computing is considered. Here, we use the HDFS and HBase database for the multi-source data storage. These datasets can be distributed in multiple computer nodes, making them more suitable for the distributed computing. Furthermore, for the online computing, we choose Spark to conduct the complex computing tasks. Web-based application analysis requires not only rapid response but also an easily comprehensible friendly visualization, and then it supports easy understanding and in-depth scientific analysis. For small-scale data visualization on the web platform, it is easy to meet real-time requirements. However, when the data is processed on a large scale, the network transmission delay and the load on the browser will increase, even result in a frustrating experience in data visualization. Therefore, the visualization of a large amount of data is a challenge. In addition, data annotations enable users to mark important information on visualization results. On one hand, it facilitates multi-user communication and sharing research ideas. On the other hand, expert annotations, standardized experimental data enlighten people to develop new analysis methods and are collected for training use. For these reasons, we proposed a system architecture of the plant electrophysiological data sharing and online computing platform based on Hadoop and Spark for potential large-scale data storage and computing. The plant electrophysiological data sharing architecture is shown in Figure 1.  
The first layer is the data storage layer. Plant electrophysiological data include intracellular and extracellular recording data, MEA data, fluorescence image sequences, patch clamps data, and ion flow data. The size and type of these data are different. Therefore, the original files and experimental metadata are stored in the HDFS, HBase and MySQL database respectively. To facilitate the application of the upper application, a unified data management interface is necessary, including the read, deletion, update and adding operation on experimental data and metadata. The second layer is the computing layer, which implements complex computation tasks by Spark, including the extraction of plant electrical signals from the fluorescence image sequence, pseudo-color map generation, the waveform extraction of the action potentials and the algorithm of the template matching. Figure 1: Plant electrophysiological data sharing architecture. The third layer is the application layer, which is implemented by Struts2, Hibernate, and other libraries. It is used to run relative simple computing tasks, read HDFS, HBase and MySQL data by calling the computing layer task, and interact with a remote client through the HTTP protocol using JSON. The fourth layer is the user layer. That is the interface layer of the web user, which completes data uploading, management, online computing, analysis and other functions by providing the web access interface. Figure 2 shows a functional schematic of the web system. In this context, it supports uploading of plant electrical signals and image datasets measured by the four kinds of recording technologies to Hadoop cluster for data storage, management, and analysis. Online visualization of analysis results from the different datasets can be smoothly finished by the friendly web interface. The basic data workflow includes: (1) Users get multi-source plant electrophysiological datasets by three classic technologies in Table 1. (2) By web browser, users can create and upload datasets by the web interface. The uploaded datasets and metadata will be stored in Hadoop cluster.