电子政务系统的数据复杂性测量

Measuring the Data Complexity of the E-government System

电子政务系统的复杂性测量是电子政务系统信息资源管理、IT服务评估、运维服务定价以及运维持续优化等的基础。本文从数据的静态性和动态性两方面将系统的数据复杂性划分为数据结构、数据量和数据操作三个维度,构建了电子政务系统的数据复杂性测量模型,利用信息熵和复杂网格空间理论明确了电子政务系统数据复杂性计算方法,最后通过A市中小企业公共服务平台系统验证了本文提出的测量模型和计算方法的有效性。

Measuring complexity is a prerequisite for managing e-government system＇s problems, such as information resource management, IT services evaluation, operation and maintenance services pricing, and sustainable optimization. Recently, complexity research has become a hot topic in the field of e-government system. Many exploratory researches in this area have been conducted in this area, but the issue on e-government systems＇ complexity measurement has not yet been fully discussed and developed to reflect dynamic factors. Based on the data of e-government systems, this paper develops a complexity model to measure the e-government system＇s complexity, with the calculation method of information entropy and complex grid space. The paper finally conducts an empirical verification with the data of a SMEPSPS （small and medium-sized enterprises public service platform system）. The first part in this paper is to develop a complexity measurement model from the perspective of e-government system＇s data. According to the information processing theory, the e-government system can be seen as government affairs＇ data processing system, The complexity of data includes affairs data and user behavior data. The data accumulated in the e-government system to some degree can decide the complexity of the e-governmem system. With the consideration of data＇s static and dynamic features, the paper classifies the data complexity into three dimensions： data structure, data volume and data operation. Each dimension can be measured with a series of explicit indicators. After decomposing the three dimensions, we obtain the data complexity of measurement space. One contribution of the proposed complexity measurement model is its ability to understand that dynamic data about user behavior. The complexity of e-government systems cannot be ignored in the model. Our proposed model is a more complete model for measuring the data not only in the system development but also in the system operation and maintenance. The second part is about the research method. With the above measurement model, Shannon＇s information entropy was used to measure the complexity degree of the three dimensions： data structure, data volume and data operation. The data structure was measured with the E-R model, including three secondary indicators： entity, relation and attribute. The data volume was measured by the number of data record. The data operation was measured with four secondary indicators from three aspects, including the intensity, density and regularity of the operation behavior. Afterwards, we used the complex grid space theory to construct a multidimensional vector space measurement model to synthesize the captured information entropy. The third part is an analysis of empirical data. With the data collected from the SMEPSPS on February in 2013 in A city, we calculated the complexity degree of data structure, data volume and data operation respectively, which are 26.1,290.2, and 32.9. Thus, the total data complexity of the unified dimensions of this system is 434.06. Combinin~ these results, we analyzed the system from the two sides of oarts and whole. From the calculated values, it＇s obvious thatthe data volume is the reflection of affairs or businesses and contributes the most to the complexity of the whole system. In addition, different from the empirical result of ERP systems in enterprises, the complexity value of user behavior exceeds that of the data structure. This finding indicates that the complexity of user behaviors in the e-government system has relatively more impact on the complexity of whole system. The last part is the conclusion. The theoretical contribution and practical values of this paper are discussed in this part. For theoretical contributions, the data complexity is a new perspective to measure the complexity of the e-government system and expands the study on the complexity of e-government system, which also provides a theoretical basis for IT service performance evaluation, operation and maintenance pricing and sustainable optimization of the system. As for practical implications, measurement results can be used as a quantitative reference for providing high-quality services, such as data migration and the optimization of e-government system structure. In addition, they can be used as an effective index to measure IT service cost and assess the performance of IT department. This paper shows the development of a data complexity model, which consists of data structure, data volume and data operation. The model is used to measure the degree of e-government system complexity by employing the calculation method of information entropy and complex grid space theory. The data of the SMEPSPS in A city is used to validate and explain the proposed model and calculation method. The analysis results indicate that the difference between the e-government system and traditional ones is that data operation has a larger influence on the whole data complexity. This research can provide theoretical and practical foundation for solving problems in the e-government system area, including IT service evaluation, the performance evaluation of government IT department, system design evaluation and optimization, operation and maintenance pricing, and sustainable optimization.