Innovation in the services area - especially in the electronic services （e-services） domain - can be systematically developed by first considering the strategic drivers and foci, then the tactical principles and ena...Innovation in the services area - especially in the electronic services （e-services） domain - can be systematically developed by first considering the strategic drivers and foci, then the tactical principles and enablers, and finally the operational decision attributes, all of which constitute a process or calculus of services innovation. More specifically, there are four customer drivers （i.e., collaboration, customization, integration and adaptation）, three business foci （i.e., creation-focused, solution-focused and competition-focused）, six business principles （i.e., reconstruct market boundaries, focus on the big picture not numbers, reach beyond existing demand, get strategic sequence right, overcome organizational hurdles and build execution into strategy）, eight technical enablers （i.e., software algorithms, automation, telecommunication, collaboration, standardization, customization, organization, and globalization）, and six attributes of decision informatics （i.e., decision-driven, information-based, real-time, continuously-adaptive, customer-centric and computationally-intensive）. It should be noted that the four customer drivers are all directed at empowering the individual - that is, at recognizing that the individual can, respectively, contribute in a collaborative situation, receive customized or personalized attention, access an integrated system or process, and obtain adaptive real-time or just-in-time input. The developed process or calculus serves to identify the potential white spaces or blue oceans for innovation. In addition to expanding on current innovations in services and related experiences, white spaces are identified for possible future innovations; they include those that can mitigate the unforeseen consequences or abuses of earlier innovations, safeguard our rights to privacy, protect us from the always-on, interconnected world, provide us with an authoritative search engine, and generate a GDP metric that can adequately measure the growing knowledge economy, one driven by intangible ideas and services innovation.展开更多
Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results.Monte Carlo methods are often used in simulating complex systems.Because of their reliance on ...Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results.Monte Carlo methods are often used in simulating complex systems.Because of their reliance on repeated computation of random or pseudo-random numbers,these methods are most suited to calculation by a computer and tend to be used when it is infeasible or impossible to compute an exact result with a deterministic algorithm.In finance,Monte Carlo simulation method is used to calculate the value of companies,to evaluate economic investments and financial derivatives.On the other hand,Grid Computing applies heterogeneous computer resources of many geographically disperse computers in a network in order to solve a single problem that requires a great number of computer processing cycles or access to large amounts of data.In this paper,we have developed a simulation based on Monte Carlo method which is applied on grid computing in order to predict through complex calculations the future trends in stock prices.展开更多
文摘Innovation in the services area - especially in the electronic services （e-services） domain - can be systematically developed by first considering the strategic drivers and foci, then the tactical principles and enablers, and finally the operational decision attributes, all of which constitute a process or calculus of services innovation. More specifically, there are four customer drivers （i.e., collaboration, customization, integration and adaptation）, three business foci （i.e., creation-focused, solution-focused and competition-focused）, six business principles （i.e., reconstruct market boundaries, focus on the big picture not numbers, reach beyond existing demand, get strategic sequence right, overcome organizational hurdles and build execution into strategy）, eight technical enablers （i.e., software algorithms, automation, telecommunication, collaboration, standardization, customization, organization, and globalization）, and six attributes of decision informatics （i.e., decision-driven, information-based, real-time, continuously-adaptive, customer-centric and computationally-intensive）. It should be noted that the four customer drivers are all directed at empowering the individual - that is, at recognizing that the individual can, respectively, contribute in a collaborative situation, receive customized or personalized attention, access an integrated system or process, and obtain adaptive real-time or just-in-time input. The developed process or calculus serves to identify the potential white spaces or blue oceans for innovation. In addition to expanding on current innovations in services and related experiences, white spaces are identified for possible future innovations; they include those that can mitigate the unforeseen consequences or abuses of earlier innovations, safeguard our rights to privacy, protect us from the always-on, interconnected world, provide us with an authoritative search engine, and generate a GDP metric that can adequately measure the growing knowledge economy, one driven by intangible ideas and services innovation.
文摘Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results.Monte Carlo methods are often used in simulating complex systems.Because of their reliance on repeated computation of random or pseudo-random numbers,these methods are most suited to calculation by a computer and tend to be used when it is infeasible or impossible to compute an exact result with a deterministic algorithm.In finance,Monte Carlo simulation method is used to calculate the value of companies,to evaluate economic investments and financial derivatives.On the other hand,Grid Computing applies heterogeneous computer resources of many geographically disperse computers in a network in order to solve a single problem that requires a great number of computer processing cycles or access to large amounts of data.In this paper,we have developed a simulation based on Monte Carlo method which is applied on grid computing in order to predict through complex calculations the future trends in stock prices.
