| Summary: | 博士 === 國立臺灣大學 === 電信工程學研究所 === 104 === Networked data can be found in many field, including social science, engineering, financial analysis and the Internet of Things. The processing of network data brings new opportunities to our society and challenges to data scientists. On the one hand, the network structure underlying the data holds great promises for utilizing the interaction among different groups of data sources, including efficient data transmission and data sharing, as well as the challenges of privacy preserving and inference attack. On the other hand, like “Big Data”, the massive sample size and high dimensionality of data introduce unique computational and statistical challenges. These opportunities are distinguished and require new computational and statistical paradigms. This dissertation gives an overview on what is networked data and how networked data impact on paradigm changes of analysis techniques and new data engineering architectures. We also provide various perspectives on the networked data analysis and computation. In particular, we emphasize the recognition on networked data, which is a new philosophy that incorporates higher order network structures to solve decision problems on networked data, and point out that decisions incorporating network structure can greatly improve the performance of systems as well as mitigate several engineering problems, including data recovery, privacy preserving and inference attack. Several applications based on networked data analysis are also introduced, including sensor network management, river dust analysis, and interaction between stock markets and exchange rate.
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