With the arrival of the information and networking era and the development of economic globalization enormous amounts of data, which have been collected from around the globe, can also cross into many different industries. Extracting useful information from large, diverse, distributed and heterogeneous data sets will enable acceleration in breakthrough discoveries and innovations in science, engineering, medicine, commerce, education, and national security, and make significant scientific, social, and economic impacts. There are tremendous opportunities for interdisciplinary research in developing new quantitative modeling (e.g., statistics, data mining, machine learning, simulation, and operation research) to improve data-intensive decision-making. How to understand, model, and improve the data quality based on the domain knowledge and the vast data sources while incorporating data quality with decision making, and how to model, extract, and make use of hidden knowledge to improve overall system performance and enhance the system maintainability and adaptability encompass with the extensively amount of data, pose many research and practical challenges, such as defying the state of the art methodological frameworks, algorithms, systems design, and implementation principles. The purpose of the International Symposium series on System Informatics and Engineering (ISSIE) is to bring together researchers and practitioners interested in the theory and applications of system-driven data quality enhancement, knowledge discovery, and design/operation improvement in a wide range of domains and discipline.