## 4 Results and Discussion
### Discussion  
PlantES, a plant electrophysiological multi-source data sharing prototype system, realized the visualization for many plant electrophysiological data (extracellular recording data, plant fluorescence image series data, MEA data with typical size of several GB) and integrated various data analysis algorithms for the online analysis and visualization.  
Compared with other electrophysiological platforms for medical application, our system integrated with many signal analysis algorithms, such as signal classification, feature extraction, and fluorescence image electrical signals extraction, which are convenient for researchers' online analysis and further reduce the user threshold. In addition, we added the interactivity to the data visualization. Although plant electrical signals are similar to animal electrophysiology, the distinct difference of plant cell and animal cell makes researchers usually have to use the customization system. In particular, the difference of variety, measuring parts, growth phases, and size leads to the diversity of the plant electrophysiological data formats. To solve the problem, we adopted different storage schemas for different types of data by using HBase and Hadoop to achieve effective data management, storage, and retrieval. For the problem of latency for large-scale data analysis, Spark was used to process the parallel computing tasks. The experiment results demonstrated that Spark was more effective than MapReduce and the improvement of Spark in large-scale data analysis was more pronounced than that in small-scale data analysis. It provided a flexible and scalable solution for the plant electrophysiological data sharing, management and analysis.  
We developed the plant electrophysiological data sharing and analysis platform. To adapt to large-scale data analysis, we used the technology stack of Hadoop to read, write and compute the datasets. The parallel computing on Spark cluster enabled online analysis to be efficiently executed. Based on the web platform, the efficient distributed computing and storage architecture were designed to realize the real-time or near real-time online calculation of plant electrophysiological data, which can greatly improve the efficiency of data processing. It also integrated various data analysis algorithms for the online analysis and visualization. Researchers can comfortably visualize, analyze and share plant electrophysiological datasets online by browser.  
At the present stage, our research is only an initial trial. In the design process, the existing EEG-ECG electrophysiological platforms have given us a lot of inspiration [30; 31; 32; 33; 34; 35; 36; 37; 38; 40]. Although the functions of our system are similar to those platforms for medical applications, our system is used to deal with plant electrophysiological data. In future, we will develop a more effective storage format for plant electrophysiological data. For multi-source scientific data, e.g., MEA data, extracellular recordings data, fluorescent images and other data, standardized file format needs to be designed reasonably, and then metadata, data, and annotation data information need to be organized much more properly. As a standardized scientific data storage format, HDF5 is the potential scheme of standardized storage for the plant electrical signals data [51]. Next, the coupling between the standardized data storage format and the storage platform needs to be implemented through providing a uniform data operation interface for the upper application, designing fast retrieval algorithm of plant electrical signals, constructing new template index to reduce the time of template matching, and building standardized plant electrical signals data storage. In addition, web service interfaces provide external applications to download experimental data and invoke computational programs for data analysis and obtaining analysis results. The scientific workflow of plant electrical signals analysis also should be integrated.