Ting Yu – författare

Database security is one of the classical topics in the research of information system security. Ever since the early years of database management systems, a great deal of research activity has been conducted. Fruitful results have been produced, many of which are widely adopted in commercial and military database management systems. In recent years, the research scope of database security has been greatly expanded due to the rapid development of the global internetworked infrastructure. Databases are no longer stand-alone systems that are only accessible to internal users of - ganizations. Instead, allowing selective access from different security domains has become a must for many business practices. Many of the assumptions and problems in traditional databases need to be revisited and readdressed in decentralized en- ronments. Further, the Internet and the Web offer means for collecting and sharing data with unprecedented flexibility and convenience. New data services are eme- ing every day, which also bring new challenges to protect of data security. We have witnessed many exciting research works toward identifying and addressing such new challenges. We feel it is necessary to summarize and systematically present works in these new areas to researchers. This book presents a collection of essays, covering a wide range of today's active areas closely related to database security organized as follows. In Part I, We review classical work in database security, and report their recent advances and necessary extensions.

Database security is one of the classical topics in the research of information system security. Ever since the early years of database management systems, a great deal of research activity has been conducted. Fruitful results have been produced, many of which are widely adopted in commercial and military database management systems. In recent years, the research scope of database security has been greatly expanded due to the rapid development of the global internetworked infrastructure. Databases are no longer stand-alone systems that are only accessible to internal users of - ganizations. Instead, allowing selective access from different security domains has become a must for many business practices. Many of the assumptions and problems in traditional databases need to be revisited and readdressed in decentralized en- ronments. Further, the Internet and the Web offer means for collecting and sharing data with unprecedented flexibility and convenience. New data services are eme- ing every day, which also bring new challenges to protect of data security. We have witnessed many exciting research works toward identifying and addressing such new challenges. We feel it is necessary to summarize and systematically present works in these new areas to researchers. This book presents a collection of essays, covering a wide range of today''s active areas closely related to database security organized as follows. In Part I, We review classical work in database security, and report their recent advances and necessary extensions.

Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems.With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors.The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handled by the current generation of climate models. It discusses potential conceptual and methodological directions to build a close integration between physical understanding, or physics-based modeling, and data-driven insights. The book then covers the conservation of species and ecologically valuable land. A case study on the Pennsylvania Dirt and Gravel Roads Program demonstrates that multiple-objective linear programming is a more versatile and efficient approach than the widely used benefit targeting selection process.Moving on to renewable energy and the need for smart grids, the book explores how the ongoing transformation to a sustainable energy system of renewable sources leads to a paradigm shift from demand-driven generation to generation-driven demand. It shows how to maximize renewable energy as electricity by building a supergrid or mixing renewable sources with demand management and storage. It also presents intelligent data analysis for real-time detection of disruptive events from power system frequency data collected using an existing Internet-based frequency monitoring network as well as evaluates a set of computationally intelligent techniques for long-term wind resource assessment.In addition, the book gives an example of how temporal and spatial data analysis tools are used to gather knowledge about behavioral data and address important social problems such as criminal offenses. It also applies constraint logic programming to a planning problem: the environmental and social impact assessment of the regional energy plan of the Emilia-Romagna region of Italy.Sustainable development problems, such as global warming, resource shortages, global species loss, and pollution, push researchers to create powerful data analysis approaches that analysts can then use to gain insight into these issues to support rational decision making. This volume shows both the data analysis and sustainable development communities how to use intelligent data analysis tools to address practical problems and encourages researchers to develop better methods.

Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems.With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors.The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handled by the current generation of climate models. It discusses potential conceptual and methodological directions to build a close integration between physical understanding, or physics-based modeling, and data-driven insights. The book then covers the conservation of species and ecologically valuable land. A case study on the Pennsylvania Dirt and Gravel Roads Program demonstrates that multiple-objective linear programming is a more versatile and efficient approach than the widely used benefit targeting selection process.Moving on to renewable energy and the need for smart grids, the book explores how the ongoing transformation to a sustainable energy system of renewable sources leads to a paradigm shift from demand-driven generation to generation-driven demand. It shows how to maximize renewable energy as electricity by building a supergrid or mixing renewable sources with demand management and storage. It also presents intelligent data analysis for real-time detection of disruptive events from power system frequency data collected using an existing Internet-based frequency monitoring network as well as evaluates a set of computationally intelligent techniques for long-term wind resource assessment.In addition, the book gives an example of how temporal and spatial data analysis tools are used to gather knowledge about behavioral data and address important social problems such as criminal offenses. It also applies constraint logic programming to a planning problem: the environmental and social impact assessment of the regional energy plan of the Emilia-Romagna region of Italy.Sustainable development problems, such as global warming, resource shortages, global species loss, and pollution, push researchers to create powerful data analysis approaches that analysts can then use to gain insight into these issues to support rational decision making. This volume shows both the data analysis and sustainable development communities how to use intelligent data analysis tools to address practical problems and encourages researchers to develop better methods.

Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems.With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors.The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handled by the current generation of climate models. It discusses potential conceptual and methodological directions to build a close integration between physical understanding, or physics-based modeling, and data-driven insights. The book then covers the conservation of species and ecologically valuable land. A case study on the Pennsylvania Dirt and Gravel Roads Program demonstrates that multiple-objective linear programming is a more versatile and efficient approach than the widely used benefit targeting selection process.Moving on to renewable energy and the need for smart grids, the book explores how the ongoing transformation to a sustainable energy system of renewable sources leads to a paradigm shift from demand-driven generation to generation-driven demand. It shows how to maximize renewable energy as electricity by building a supergrid or mixing renewable sources with demand management and storage. It also presents intelligent data analysis for real-time detection of disruptive events from power system frequency data collected using an existing Internet-based frequency monitoring network as well as evaluates a set of computationally intelligent techniques for long-term wind resource assessment.In addition, the book gives an example of how temporal and spatial data analysis tools are used to gather knowledge about behavioral data and address important social problems such as criminal offenses. It also applies constraint logic programming to a planning problem: the environmental and social impact assessment of the regional energy plan of the Emilia-Romagna region of Italy.Sustainable development problems, such as global warming, resource shortages, global species loss, and pollution, push researchers to create powerful data analysis approaches that analysts can then use to gain insight into these issues to support rational decision making. This volume shows both the data analysis and sustainable development communities how to use intelligent data analysis tools to address practical problems and encourages researchers to develop better methods.

Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems.With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors.The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handled by the current generation of climate models. It discusses potential conceptual and methodological directions to build a close integration between physical understanding, or physics-based modeling, and data-driven insights. The book then covers the conservation of species and ecologically valuable land. A case study on the Pennsylvania Dirt and Gravel Roads Program demonstrates that multiple-objective linear programming is a more versatile and efficient approach than the widely used benefit targeting selection process.Moving on to renewable energy and the need for smart grids, the book explores how the ongoing transformation to a sustainable energy system of renewable sources leads to a paradigm shift from demand-driven generation to generation-driven demand. It shows how to maximize renewable energy as electricity by building a supergrid or mixing renewable sources with demand management and storage. It also presents intelligent data analysis for real-time detection of disruptive events from power system frequency data collected using an existing Internet-based frequency monitoring network as well as evaluates a set of computationally intelligent techniques for long-term wind resource assessment.In addition, the book gives an example of how temporal and spatial data analysis tools are used to gather knowledge about behavioral data and address important social problems such as criminal offenses. It also applies constraint logic programming to a planning problem: the environmental and social impact assessment of the regional energy plan of the Emilia-Romagna region of Italy.Sustainable development problems, such as global warming, resource shortages, global species loss, and pollution, push researchers to create powerful data analysis approaches that analysts can then use to gain insight into these issues to support rational decision making. This volume shows both the data analysis and sustainable development communities how to use intelligent data analysis tools to address practical problems and encourages researchers to develop better methods.

Database security is one of the classical topics in the research of information system security. Ever since the early years of database management systems, a great deal of research activity has been conducted. Fruitful results have been produced, many of which are widely adopted in commercial and military database management systems. In recent years, the research scope of database security has been greatly expanded due to the rapid development of the global internetworked infrastructure. Databases are no longer stand-alone systems that are only accessible to internal users of - ganizations. Instead, allowing selective access from different security domains has become a must for many business practices. Many of the assumptions and problems in traditional databases need to be revisited and readdressed in decentralized en- ronments. Further, the Internet and the Web offer means for collecting and sharing data with unprecedented flexibility and convenience. New data services are eme- ing every day, which also bring new challenges to protect of data security. We have witnessed many exciting research works toward identifying and addressing such new challenges. We feel it is necessary to summarize and systematically present works in these new areas to researchers. This book presents a collection of essays, covering a wide range of today's active areas closely related to database security organized as follows. In Part I, We review classical work in database security, and report their recent advances and necessary extensions.

This book investigates emerging work attitudes among employees in China’s service sector, with a particular focus on the phenomena of “Tang Ping” (lying flat) and “Nei Juan” (involution). The research draws upon original empirical data collected in Shanghai in 2023, situating these attitudes within the context of intergenerational workforce dynamics and China’s rapidly evolving urban service economy.

This book investigates emerging work attitudes among employees in China’s service sector, with a particular focus on the phenomena of “Tang Ping” (lying flat) and “Nei Juan” (involution). The research draws upon original empirical data collected in Shanghai in 2023, situating these attitudes within the context of intergenerational workforce dynamics and China’s rapidly evolving urban service economy.

This book highlights the photogalvanic effects at low dimensions, surfaces, and interfaces, more specifically 2D materials, such as graphene and monolayer transition metal dichalcogenides. Although the phenomenology of the photogalvanic effects, which can be simply seen as photoresponse nonlinear-in-electric field, have been well-established, the microscopic understanding in each material system may vary. This book is a quick reference and a detailed roadmap starting from phenomenology and continuing with the ultimate low dimensional materials, in which the photogalvanic effects can offer a rich platform at the second-order response to an electric field. A general phenomenology of photogalvanic effect is provided in the first chapter, together with the photon drag effect which also generates a photocurrent like the photogalvanic effect, but with some distinct features, as well as somewhat puzzling similarities. Next two chapters explain these effects in graphene, starting with a necessary related background on graphene, then a particular focus on its specific phenomenology, microscopic theory, and experimental results. In a similar fashion, in chapters four and five, a necessary background for the photogalvanic effects in monolayer transition metal dichalcogenides, with symmetry analysis, microscopic theory, and experimental results is presented, along with the Berry curvature dependent photocurrent, which can also play an important role in 2D semiconductors. The second-order photogalvanic effects that have been covered so far in graphene and monolayer transition metal chalcogenides have already excited the 2D semiconductor optoelectronic research community by several means. It seems that the interests on the photogalvanic effects will continue to escalate in near future.