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The goal of the scientific research big data service platform for experimental data managing is to solve the problem of data silos in the state-owned scientific research management system caused by backward informatization. The new data service platform integrates data collection, data analysis, data governance, monitoring and management, prediction and early warning, and visualization platform. We are committed to improving data management and service capability with informatization, and to grasp the material development and design situation timely and accurately. The goal of our platform is to truly use data to speak, manage and make decisions with data. [ABSTRACT FROM AUTHOR]

In any field, the security of data is extremely important, and it is even related to national security and personal privacy. Within a mature system framework, the design of data security is the most basic and challenging task, and access control is one of the main strategies for Network security prevention. Log as an indispensable part of a secure system can help us to complete traceability after a data breach and to monitor the operation of the application at any time. However, in the existing confidential data management systems, the existing access control methods are not friendly to confidential data, and there are problems of excessive administrator privileges and no confidentiality restrictions. Considering of the fact that the authority and log Module is not well implemented in most confidential data management system, we propose to design a general access control model application. We propose an access control model based on roles and object domains, combined with a security level. Through this model, we can implement three-layer filtering when users access data, thereby ensuring data security and avoiding data leakage problems. At the same time, by implementing the log module, some deficiencies in the log analysis and monitoring of existing confidential data management system can be solved. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of big data technology, more and more Internet enterprises have started to transform and upgrade into big data driven enterprises. In addition, in the industrial field, information services driven by industrial big data have also attracted widespread attention. Some traditional industries, such as state-owned chemical enterprises are also actively transforming to information management and big data management. These traditional chemical enterprises have been using simple data processing tools internally in the past, such as Microsoft Excel. Although these tools are relatively simple to use and do not involve much learning cost, they cannot support situations where the volume of data increases and data types become more complex. These data processing methods suffer from insufficient capacity, performance degradation, management confusion and other problems, so that they are no longer suitable for modern data management work. For these traditional chemical companies, on the one hand they have many types of specialist data to store, such as simulation data, measurement data, formulation data, component data, process data, etc. On the other hand, they are unable to determine the full database table structure from the outset, and need to change it dynamically during use. This paper designs a new service-oriented data processing for dynamic schema to meet these needs, and applies it to the development of a data center web application platform for a state-owned chemical company. [ABSTRACT FROM AUTHOR]

With the increase of data and information, anomaly management has been attracting much more attention and become an important research topic gradually. Previous literatures have advocated anomaly discovery and identification ignoring the fact that practice needs anomaly detection in advance (anomaly prediction) but anomaly detection with post-hoc analysis. Given this apparent gap, this research proposes a new approach for anomaly prediction based on PCA (principle component analysis) and information entropy theory, and support vector regression. The main idea of anomaly prediction is to train the historical data and to identify and recognize outlier data according to previous streams patterns and trends. The explorative results of SO 2 concentration of exhaust gas in WFGD (Wet Flue Gas Desulfurization) demonstrate a good performance (efficient and accurate) of the target data prediction approach. This robust and novel method can be used to detect and predict the anomaly in data streams, and applied to fault prediction, credit card fraud prediction, intrusion prediction in cyber-security, malignant diagnosis, etc. [ABSTRACT FROM AUTHOR]