Measuring The Complexity Of Information System Based On Data Complexity And Verifing Its Effectiveness

The measurement of complexity of information system is the basis solution of the information system’s cost estimating, workload assessment and reconstruction performance evaluation. With the rapid development of information technology, information systems gradually from lower to higher, from simple to complex, from static to dynamic, isolated from closed to open collaborative development. The complexity of information system is difficult to accurately measure. The original measurement methods from the perspective of system structure, configuration process and application software, but there are measurement models are not comprehensive enough, the lack of dynamic considerations shortcomings. Therefore, proposing a new model and method to measure the complexity of information system comprehensively and accurately has theoretical significance and practical value.From the view of data processing theory, the paper proposes the concept of data complexity, builds measurement model of information system based on data complexity, and develops calculation method of complexity of information system based on information entropy and complexity grid space. What’s more, the paper demonstrates the effectiveness of the proposed complexity measurement model and calculation methods of information system, by the manufacturing enterprise’s actual data and calculation results of information systems architecture complexity.Firstly, proposing the complexity measurement model and calculation methods of information system based on data complexity. From the view of data processing theory, the information system is essential a data processing system, based on analysis complexity of information system, the paper proposes the concept of data complexity, and measure the complexity of information system based on data complexity. And the same time, from the perspective of static and dynamic nature, the data complexity is divided into data structure, data volume, and data manipulation. Based on data complexity, a measurement model is brought forward to measure the complexity of information system. Furthermore, Information entropy models are used to compute the complexity of data structure, data volume, and data manipulation, and the grid space theory is employed to calculate the overall data complexity. An empirical study was conduct with the material management system and sale information systems in two manufacturing enterprises and the results indicate that the measurement model and computation method of data complexity are feasible to measure the complexity of information system.Secondly, the applications study on measurement modal of complexity of information system based on the architecture complexity. From what is the most widely used measurement model of complexity of the information system, the architecture complexity perspective, by the empirical application of the same case, measuring the value of architecture complexity, providing a reference point and theoretical basis to prove the validity of information system complexity measurement model and the calculation method based on the data complexity.Thirdly, verify the complexity measurement model’s effectiveness of information system based on data complexity. On the basis of proposing the new model method, and taking into account that the model presents a short time and fewer cases of application, the paper takes the information system architecture complexity measurement model as a standard. By means of the same case application, the paper verifies that it is an effective information system complexity measurement model based on data complexity. And through empirical calculation results show that the new model compared to the traditional information system complexity measurement model, has advantages of that the measurement dimensions and perspective is more reasonable, the measurement model is more comprehensive and accurate, and the measurement results is easier to reflect the structural differences of the information systems and content changes.