Call for paper: Dynamic Improvement and Optimization of Environmental Management using Streaming Data
• 大类 : 环境科学与生态学 - 2区
• 小类 : 环境科学 - 2区
The rapid development of big data technology has unprecedented opportunities and challenges for dynamic environmental management. Big data, as a new form of information technology and service, is now becoming an important revolutionary industry. Through specialized “processing” of massive and complex data, it creates numerous new products and services (Song et al., 2017a). Because most human activities are closely related to the ecological environment, which contains massive data, further improvement of environmental management through effective processing and use of big data has become a key issue for managers. Compared with traditional data, big data displays the 5V Characteristics, which are Volume, Variety, Veracity, Velocity, and Valorization (Wang et al., 2017). This makes using big data technology in dynamic environmental management inevitable.
From the data source perspective, there are essential differences between big data and traditional data. World famous data management Statistical Analysis System (SAS), divides big data into three categories according to its source. The first is Streaming Data, including various data collected from the Internet, such as GIS remote sensing data, sensor data, and environmental monitoring data, among others. One of the key features of Steaming Data is the high-speed persistence, which requires processing in an almost real-time manner. The second category is Social Media Data. Social Media Data has gained increasing attention in fields such as product design and marketing, and is usually observed in unstructured or semi-structured form. The third category is Publicly Available Sources, which is available from open-source sources such as government and nonprofit organizations but has the characteristics of source uncertainty and unknown data characteristics. Because of its structured feature, streaming data is attracting more and more attention. Therefore, streaming data will become the basis for dynamic environmental performance improvement and optimization, risk identification, and trend prediction. It is of great theoretical and practical significance to use streaming data in dynamic environmental management.
The development of big data technology, especially the collection and application of streaming data, makes it possible to pay more attention to the features of complexity and instability of dynamic environmental management.Dynamic environmental management promotes the efficient use of resources and reduces the uncertain risk in decision-making (Ryberg et al., 2018). Facing the uncertain and rapidly changing ecological environment, managers urgently need new methods for dynamic improvement and optimization of environmental management, which can simultaneously evaluate environmental conditions in time, provide timely warning about environmental problems, and predict possible future environmental risks.
However, streaming data is different from traditional static data in that it has time continuity, high rapidity, time uncertainty, and other characteristics (Zhu et al., 2018). All these differences make incorporating streaming data into dynamic environmental management a challenging job. For example, using massive real-time monitoring data to evaluate dynamic environmental performance needs to solve at least the following three problems (Song et al., 2017b). First, how to sort and extract key index data from such a huge amount of information. Second, in the face of the continuous and rapid arrival of monitoring data, dynamic environmental management methods should realize the rapid evaluation of indicator variables. Third, the timeliness of acquired monitoring data should be further considered to avoid real-time data being affected by sudden factors. The above characteristics of streaming data greatly increase the complexity of dynamic environmental management.
The flow of data generated and passed at a high speed presents new challenges to environmental management, while also providing new opportunities (Knüppe & Knieper, 2016). On the one hand, the generation of streaming data technology allows managers to establish a set of early warning mechanisms for quick response and decision making, together with having full use of the data on environmental performance evaluation. On the other hand, the time-sensitivity of streaming data also provides a new platform for accurate understanding of environmental dynamics, real-time monitoring of environmental problems, and timely resolution of environmental crises.To develop a better understanding of the dynamic improvement and optimization of environmental management in the presence of streaming data, this special issue seeks high-quality original research papers, which innovatively contribute to emerging issues of streaming data-based dynamic environmental management.