摘要 :
An analytical model for mobile-to-mobile underwater communications is presented. From the analytical model, the envelope level crossing rate is derived for a non-isotropic scattering environment. The obtained analytical results ar...
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An analytical model for mobile-to-mobile underwater communications is presented. From the analytical model, the envelope level crossing rate is derived for a non-isotropic scattering environment. The obtained analytical results are compared with measured data. The close agreement between the analytical and empirical curves confirms the utility of the proposed model.
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摘要 :
Three-phase ac systems can be transferred into dq axis, where stability analysis becomes much simpler. In order to apply the stability criterion in AC system, an equivalent DQ admittance model for the three-phase diode rectifier h...
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Three-phase ac systems can be transferred into dq axis, where stability analysis becomes much simpler. In order to apply the stability criterion in AC system, an equivalent DQ admittance model for the three-phase diode rectifier has been developed in this paper, based on the ABC impedance model in paper [1]. Compared to the literature work, this model has two better features: (1) it is valid in a wide frequency range, which is from below fundamental frequency to high number times of fundamental frequency; (2) it considers the effect of the ac line impedance and output dc impedance. At different circuit parameters, including the extreme cases, the analytical modeling results and the numerical simulation results present excellent agreement, which demonstrates the accuracy of the analytical model.
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Analytics technologies that mine large amount of structured and unstructured data to gain insights are becoming increasingly important to businesses. In particular, the growing availability of enterprise proprietary data, coupled ...
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Analytics technologies that mine large amount of structured and unstructured data to gain insights are becoming increasingly important to businesses. In particular, the growing availability of enterprise proprietary data, coupled with publically aggregated or acquired data allows analytics to gain insights not only about the enterprise itself, but also cross companies, industry, and cross industries. The impact of such analysis is that it is transforming business processes and driving strategic business decision making and business model transformations, all of which overshadow more traditional low level, siloed, tactical optimizations. Such analytics trends are driving shifts in the overall analytics ecosystem that includes data providers and aggregators, analytics technology and service providers, clients in different industries, partners, and other related communities, e.g., visualization providers, academia, open source development communities. In particular, we have observed the emergence of two new service entities in the overall ecosystem: § New forms of data services that aggregate and provide accesses to a wide range of public and private data by partnering with data providers, aggregators, and clients are emerging. We call such services "Data as a Service (DaaS)" in this paper. DaaS can leverage commonly managed Cloud and Web based infrastructure and tools as well as hosted and Web delivery models to offer rich set of data processing, management, and access services, in addition to in house implementations. § On top of DaaS, one can create high value analytics services that can boost productivity and create value for all. Such services may include Business Intelligence reporting, text analytics, and advanced analytics such as predictive modeling, all made in composable forms to allow for direct consumption, integration and customizations. We call such services "Analytics as a Service (AaaS)". DaaS and AaaS help to maximize value for the overa-
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ll ecosystem by eliminating common costs and delivering high value data and analytics services. Their emergence is transforming the overall analytics ecosystem and forcing significant cost structure and productivity model shifts, i.e., where to cut cost and where to make money -- two key metrics to a business model. As a result, they are driving the emergence of new business models across the overall analytics ecosystem. In this paper, we will analyze the major analytics ecosystem trends. We show that our analysis suggests that there is an analytics ecosystem transformation undergoing. The new ecosystem will increasingly leverage new forms of data and analytics services and roles, e.g., DaaS and AaaS, to maximize value for the overall ecosystem. Such ecosystem changes drive shifts in enterprise cost structures and productivity and value creation models and creates a force for business model innovation. We will describe an evolution of business models around the analytics ecosystem and highlight the emerging business models that are enabled by the new ecosystem, many of which have an open, collaboration, and co-developing spirit. We will also present several real-world case studies to illustrate how the new ecosystem can maximize value for all by implementing innovative business models.
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摘要 :
Analytics technologies that mine large amount of structured and unstructured data to gain insights are becoming increasingly important to businesses. In particular, the growing availability of enterprise proprietary data, coupled ...
展开
Analytics technologies that mine large amount of structured and unstructured data to gain insights are becoming increasingly important to businesses. In particular, the growing availability of enterprise proprietary data, coupled with publically aggregated or acquired data allows analytics to gain insights not only about the enterprise itself, but also cross companies, industry, and cross industries. The impact of such analysis is that it is transforming business processes and driving strategic business decision making and business model transformations, all of which overshadow more traditional low level, siloed, tactical optimizations. Such analytics trends are driving shifts in the overall analytics ecosystem that includes data providers and aggregators, analytics technology and service providers, clients in different industries, partners, and other related communities, e.g., visualization providers, academia, open source development communities. In particular, we have observed the emergence of two new service entities in the overall ecosystem: § New forms of data services that aggregate and provide accesses to a wide range of public and private data by partnering with data providers, aggregators, and clients are emerging. We call such services "Data as a Service (DaaS)" in this paper. DaaS can leverage commonly managed Cloud and Web based infrastructure and tools as well as hosted and Web delivery models to offer rich set of data processing, management, and access services, in addition to in house implementations. § On top of DaaS, one can create high value analytics services that can boost productivity and create value for all. Such services may include Business Intelligence reporting, text analytics, and advanced analytics such as predictive modeling, all made in composable forms to allow for direct consumption, integration and customizations. We call such services "Analytics as a Service (AaaS)". DaaS and AaaS help to maximize value for the overa-
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ll ecosystem by eliminating common costs and delivering high value data and analytics services. Their emergence is transforming the overall analytics ecosystem and forcing significant cost structure and productivity model shifts, i.e., where to cut cost and where to make money -- two key metrics to a business model. As a result, they are driving the emergence of new business models across the overall analytics ecosystem. In this paper, we will analyze the major analytics ecosystem trends. We show that our analysis suggests that there is an analytics ecosystem transformation undergoing. The new ecosystem will increasingly leverage new forms of data and analytics services and roles, e.g., DaaS and AaaS, to maximize value for the overall ecosystem. Such ecosystem changes drive shifts in enterprise cost structures and productivity and value creation models and creates a force for business model innovation. We will describe an evolution of business models around the analytics ecosystem and highlight the emerging business models that are enabled by the new ecosystem, many of which have an open, collaboration, and co-developing spirit. We will also present several real-world case studies to illustrate how the new ecosystem can maximize value for all by implementing innovative business models.
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Currently there is insufficient support regarding the user-oriented modeling of analytical information systems. More precisely, there is a lack of semiformal and detailed conceptual models, which could represent the functional and...
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Currently there is insufficient support regarding the user-oriented modeling of analytical information systems. More precisely, there is a lack of semiformal and detailed conceptual models, which could represent the functional and nonfunctional requirements of analytical users. Addressing this issue, this article motivates the development of a conceptual analytical service configuration model, gives a short insight into previous research and describes the range of necessary modeling content, which contains functional and nonfunctional requirements of the user context. Then, this article presents first ideas concerning the model structures and discusses potential application areas.
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摘要 :
Currently there is insufficient support regarding the user-oriented modeling of analytical information systems. More precisely, there is a lack of semiformal and detailed conceptual models, which could represent the functional and...
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Currently there is insufficient support regarding the user-oriented modeling of analytical information systems. More precisely, there is a lack of semiformal and detailed conceptual models, which could represent the functional and nonfunctional requirements of analytical users. Addressing this issue, this article motivates the development of a conceptual analytical service configuration model, gives a short insight into previous research and describes the range of necessary modeling content, which contains functional and nonfunctional requirements of the user context. Then, this article presents first ideas concerning the model structures and discusses potential application areas.
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摘要 :
Today, there is a growing interest in data and analytics in the learning environment resulting in a highly qualified research concerning models, methods, tools, technologies and analytics. This research area is referred to as lear...
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Today, there is a growing interest in data and analytics in the learning environment resulting in a highly qualified research concerning models, methods, tools, technologies and analytics. This research area is referred to as learning analytics. Metadata becomes an important item in an e-learning system, many learning analytics models are currently developed. They use metadata to tag learning materials, learning resources and learning activities. In this paper, we firstly give a detailed injection of the existing learning analytics models in the literature. We particularly observed that there is a lack of models dedicated to conceive and analyze the assessment data. That is why our objective in this paper is to propose an assessment analytics model inspired by the Experience API data model. Hence, an assessment analytics ontology model is developed supporting the analytics of assessment data by tracking the assessment activities, assessment result and assessment context of the learner.
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摘要 :
Today, there is a growing interest in data and analytics in the learning environment resulting in a highly qualified research concerning models, methods, tools, technologies and analytics. This research area is referred to as lear...
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Today, there is a growing interest in data and analytics in the learning environment resulting in a highly qualified research concerning models, methods, tools, technologies and analytics. This research area is referred to as learning analytics. Metadata becomes an important item in an e-learning system, many learning analytics models are currently developed. They use metadata to tag learning materials, learning resources and learning activities. In this paper, we firstly give a detailed injection of the existing learning analytics models in the literature. We particularly observed that there is a lack of models dedicated to conceive and analyze the assessment data. That is why our objective in this paper is to propose an assessment analytics model inspired by the Experience API data model. Hence, an assessment analytics ontology model is developed supporting the analytics of assessment data by tracking the assessment activities, assessment result and assessment context of the learner.
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This article aims to respond to growing concerns about sustainable urbanization, which in recent years have generated a need for prospective assessment in the field of transport and land-use planning, by predicting future land-use...
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This article aims to respond to growing concerns about sustainable urbanization, which in recent years have generated a need for prospective assessment in the field of transport and land-use planning, by predicting future land-use development. We introduce a Land Use model (part LU of a Land Use Transport Interaction model) which aims to simulate households and firms location choice within an urban system. We use the agent-based approach to simulate location choices in order to account for land use changes and to estimate residential and economic activities location. This is a dynamic bottom-up approach with the households and the firms as their basic components. The MUST-B model considers the agents' location choices according to the utility theory and the equilibrium between real estate supply and demand. The model is used to simulate urban land-use development in the urban area of Bordeaux, France.
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Choosing appropriate information dissemination strategies is crucial in mobile ad hoc networks (MANET) due to the frequent topology changes. Flooding-based approaches like diffusion have a strong similarity with epidemic spreading...
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Choosing appropriate information dissemination strategies is crucial in mobile ad hoc networks (MANET) due to the frequent topology changes. Flooding-based approaches like diffusion have a strong similarity with epidemic spreading of diseases. Applying epidemiological models to information diffusion allows the evaluation of such strategies depending on the MANET characteristics, e.g. the node density. In order to choose appropriate strategies at run time, the model should be easily evaluated.In this paper, an epidemic model is developed for a simple information diffusion algorithm based on simulation results. We analytically investigate the impact of node density on information diffusion. The analytical model allows the evaluation at runtime, even on devices with restricted resources, and thus enables mobile nodes to dynamically adapt their diffusion strategies depending on the local node density.
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