A Multi-Agent System for E-Business Processes Monitoring in a Web-Based Environment

In this paper, we present a multi-agent system MAGS for the e-business processes monitoring in a web-based environment. We classify the types of agents in MAGS by their monitoring capabilities. An algorithm is given to explain the mechanism of supervising and controlling the execution of business processes. An abstract model of alerts, which can give warnings of infringement on business policies, is proposed. Access control can also be realized by MAGS, which manifests in delivering different view of the business process to different roles participate in it. Being successfully adopted in a customer service management system, MAGS has been proven flexible and practical

The Volatility of the Index of Shanghai Stock Market Research Based on ARCH and Its Extended Forms

The proposed ARCH and its extension model have brought a powerful tool for the study of stock market volatility as well as verify that a “high risk brings high-yield” and the “leverage effect” of stock market. This paper gives modeling analysis by using the ARCH group models; in the last ten years Shanghai's index returns, concluded that there are significant “high-yield associated with high-risk” phenomenon and the “leverage effect” in the domestic securities market. The previous studies in fitting return series of ARMA models, mostly with low accuracy have a very subjective “observation autocorrelation and partial autocorrelation function method,” and even directly use “random walk” model. That will inevitably have some impact on the accuracy of the model. While this paper adopts the Pandit-Wu formulaic modeling method, the ARMA model is built on a strong theoretical foundation

Research of Financial Early-Warning Model on Evolutionary Support Vector Machines Based on Genetic Algorithms

A support vector machine is a new learning machine; it is based on the statistics learning theory and attracts the attention of all researchers. Recently, the support vector machines (SVMs) have been applied to the problem of financial early-warning prediction (Rose, 1999). The SVMs-based method has been compared with other statistical methods and has shown good results. But the parameters of the kernel function which influence the result and performance of support vector machines have not been decided. Based on genetic algorithms, this paper proposes a new scientific method to automatically select the parameters of SVMs for financial early-warning model. The results demonstrate that the method is a powerful and flexible way to solve financial early-warning problem

A Spatial Model of Growth: Taking Technology Seriously

This paper attempts to develop a spatial model of economic growth in which technology and externalities are assumed to be accountable for production in geographical space. Linking externalities to the extent of intensity of production across locations in continuous space, we introduce spatial range into the production function for technological, human, and physical capitals. Our model argues that the long-run growth rate of an economy is determined not just by the growth rates of the three factors of production but by their rates of change in spatial range over the territory of the economy. In other words, spatial intensity and accumulation matter for growth. Our model is consistent with studies on knowledge spillovers, geographical agglomeration, urban and regional growth, and trade. The primary policy implication of our model is the significance of establishing efficient mechanisms or channels that promote innovation, diffusion, trade, and factor mobility over the territory of an economy. It is not as if we always have it everywhere, but there is a process in which knowledge is being created all the time in different places, and is then being diffused. This evolving distribution should be reflected in a model of production, if it is to describe an entire economy in which different people know different things. As a consequence, the idea of an aggregate production function becomes very dubious, unless a new variable is introduced, representing the distribution and diffusion of new knowledge.

A Spatial Model of Growth: Taking Technology Seriously

This paper attempts to develop a spatial model of economic growth in which technology and externalities are assumed to be accountable for production in geographical space. Linking externalities to the extent of intensity of production across locations in continuous space, we introduce spatial range into the production function for technological, human, and physical capitals. Our model argues that the long-run growth rate of an economy is determined not just by the growth rates of the three factors of production but by their rates of change in spatial range over the territory of the economy. In other words, spatial intensity and accumulation matter for growth. Our model is consistent with studies on knowledge spillovers, geographical agglomeration, urban and regional growth, and trade. The primary policy implication of our model is the significance of establishing efficient mechanisms or channels that promote innovation, diffusion, trade, and factor mobility over the territory of an economy. It is not as if we always have it everywhere, but there is a process in which knowledge is being created all the time in different places, and is then being diffused. This evolving distribution should be reflected in a model of production, if it is to describe an entire economy in which different people know different things. As a consequence, the idea of an aggregate production function becomes very dubious, unless a new variable is introduced, representing the distribution and diffusion of new knowledge

The Application of SVMs Method on Exchange Rates Fluctuation

Technical indicators are very important tools in the analysis of securities investment. In this paper, considering several main technical indicators prevailed in China security market, we predict whether the price of a stock rises or falls with the support vector machines (SVMs). We represent the technical indicators of current four days as input vector. If the price of next day rises, we say that the vector belongs to opposite set, if it falls, we say it belongs to negative set. Studying the samples, the SVMs construct a classification model. Then, based on the data of today and three days before, the SVMs give a prediction of tomorrow price. The experiment shows that the predicting accuracy is all greater than 60%

A new method for identifying industrial clustering using the standard deviational ellipse

Abstract Industrial agglomeration has attracted extensive attention from economists and geographers, yet it is still a challenge to identify the multi-agglomeration spatial structure and degree of industrial agglomeration in continuous space—there is still a lack of a more targeted industrial clustering method. The clustering method and the standard deviational ellipse (simply, ellipse) model have advantages in identifying the spatial structure and representing spatial information respectively. On this basis, we propose an ellipse-based approach to identifying industrial clusters. Our ellipse-based approach rests upon group nearest neighbor using the group-based nearest neighbor (GNN) ordering and spatial compactness matrix, where a number of point sequences with varying lengths, generated under the GNN ordering, are characterized by an ellipse and the elliptical parameters of these point sequences formulate the values and structure of the compactness matrix. Clustering is reformulated to identify ellipses with a specified parameter among a number of potential candidate ellipses, with significant changes (especially in the area) used as the cutoff criterion for determining the clusters’ border point. Our approach is illustrated in the location pattern of firms in Shanghai City, China in comparison with four well-known clustering methods. With the combination of elliptical parameters and spatial compactness, our approach may bring a new analytical ground for future industrial clustering research

A Multi-Agent System for E-Business Processes Monitoring in a Web-Based Environment

In this paper, we present a multi-agent system MAGS for the e-business processes monitoring in a web-based environment. We classify the types of agents in MAGS by their monitoring capabilities. An algorithm is given to explain the mechanism of supervising and controlling the execution of business processes. An abstract model of alerts, which can give warnings of infringement on business policies, is proposed. Access control can also be realized by MAGS, which manifests in delivering different view of the business process to different roles participate in it. Being successfully adopted in a customer service management system, MAGS has been proven flexible and practical

Representation and reasoning on rbac: A description logic approach

Abstract. Role-based access control (RBAC) is recognized as an excellent model for access control in large-scale networked applications. Formalization of RBAC in a logical approach makes it feasible to reason about a specified policy and verify its correctness. We propose a formalization of RBAC by the description logic language ALCQ. We also show that the RBAC constraints can be captured by ALCQ. Furthermore, we demonstrate how to make access control decision, perform the RBAC functions as well as check the consistency of RBAC via the description logic reasoner RACER.

Using Description Logic to Formalize Role-Based Access Control Model

Role-Based Access Control (RBAC) has been recognized as a strategy which reduces the cost and complexity of security administration in large-scale networked applications. A general family of RBAC models called RBAC96 was proposed by Sandhu et al. [1], which formally defines the relations among user, role and permission using the notion of set membership. Constraints is an important aspect of RBAC, which impose restrictions on acceptable configurations of the different components of RBAC. Nevertheless, it was discussed informally in the RBAC96 model. There has been some efforts to present a logical framework for the access control models. Most of these works are based on first-order logic or its extensions. However, excessively rich expressiveness may bring on complex computation and confusion. We present a novel formalization of RBAC using a description logic approach. Compared with first-order logic, DLs achieve a better tradeoff between the computational complexity of reasoning and the expressiveness of the language. W