34460188
Incorporating proxy services into wide area cellular IP networks
Performance enhancing proxies have drawn considerable interests from the network community as an effective approach to improve user experience in cellular networks. Previous research on this subject has been focusing on the design of proxy servers as well as the functions that these servers provide. In this paper, we study the placement of proxy servers, especially the approach of incorporating proxies into cellular networks, which allows better, faster, and more secure proxy services. We discuss various factors that must be considered when placing proxies inside cellular networks, including the information requirements of the proxies, the network requirements for supporting the proxy functions, and mobility management. Using GPRS as an example, we lay out a procedure for adding proxies into data transmission paths in cellular networks. Copyright © 2001 John Wiley & Sons, Ltd.
2298581
Global Mobility Management by Replicated Databases in Personal Communication Networks
This paper explores the use of replicated databases for management of customer data (e.g., mobility data, call routing logic) in global, intelligent, and wireless networks. We propose and analyze two, full and partial, data replication schemes-which are compatible with industry protocol standards-and compare them with the traditional, centralized database scheme. By identifying a set of key teletraffic and mobility parameters, we develop a modeling framework based on queueing models and apply it to assess the relative performance and merits of these schemes. The paper also addresses some implementation issues. Numerical results reveal that the full replication scheme outperforms the centralized one over a wide range of the parameters considered in this study. Furthermore, if some customer data-such as location data for highly mobile customers in wireless networks-change frequently, and if each call launches multiple queries into the databases, the partial replication scheme offers further performance improvement. In general, however, the choice of the database design would depend on the specific characteristics of the service and user behavior under consideration.
44655302
A generic concept-based object-oriented geographical information system
Unlike most of the current object-oriented geographical information systems (OOGISs) whose designs are based on the traditional spatial conceptual model emphasizing the processing of geometric features, the concept-based OOGIS proposed in this paper provides a spatial conceptual model which comprises rich spatial semantics fundamental to spatial analysis, and an object-oriented data model (OODM) which provides an appropriate and effective representation of the spatial conceptual model for efficient database management. By structuring the cognition of space through three interrelated hierarchies: namely the spatial conceptual hierarchy, the entity hierarchy and the feature hierarchy, the generic concept-based OOGIS renders an appropriate framework for the scientific investigation of space and the design of an efficient object-oriented database management system. In addition to its generic nature, the proposed OOGIS is in line withour commonsense conceptualization of space. Furthermore, it can entertain mul...
18293423
Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks
We have developed a new approach to learning Bayesian network structures based on the minimum description length (MDL) principle and evolutionary programming. It employs a MDL metric, which is founded on information theory, and integrates a knowledge-guided genetic operator for the optimization in the search process.
18850432
A hybrid approach to discover Bayesian networks from databases using evolutionary programming
Describes a data mining approach that employs evolutionary programming to discover knowledge represented in Bayesian networks. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately, both approaches have their own drawbacks. Thus, we propose a hybrid algorithm of the two approaches, which consists of two phases, namely, the conditional independence test and the search phases. A new operator is introduced to further enhance the search efficiency. We conduct a number of experiments and compare the hybrid algorithm with our previous algorithm, MDLEP, which uses EP for network learning. The empirical results illustrate that the new approach has better performance. We apply the approach to data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with the models generated by other methods. In the comparison, the induced Bayesian networks produced by the new algorithm outperform the other models.
1885677
Learning nonlinear multiregression networks based on evolutionary computation
This paper describes a novel knowledge discovery and data mining framework dealing with nonlinear interactions among domain attributes. Our network-based model provides an effective and efficient reasoning procedure to perform prediction and decision making. Unlike many existing paradigms based on linear models, the attribute relationship in our framework is represented by nonlinear nonnegative multiregressions based on the Choquet integral. This kind of multiregression is able to model a rich set of nonlinear interactions directly. Our framework involves two layers. The outer layer is a network structure consisting of network elements as its components, while the inner layer is concerned with a particular network element modeled by Choquet integrals. We develop a fast double optimization algorithm (FDOA) for learning the multiregression coefficients of a single network element. Using this local learning component and multiregression-residual-cost evolutionary programming (MRCEP), we propose a global learning algorithm, called MRCEP-FDOA, for discovering the network structures and their elements from databases. We have conducted a series of experiments to assess the effectiveness of our algorithm and investigate the performance under different parameter combinations, as well as sizes of the training data sets. The empirical results demonstrate that our framework can successfully discover the target network structure and the regression coefficients.
10417746
An Adaptive Inductive Logic Programming System Using Genetic Programming
Recently, there has been increasing interest in systems that induce firstorder logic programs. In this formalism, domain knowledge represented in the forms of first-order relations can be used in the induced programs. The task of inducing a logic program can be formulated as a search problem (Mitchell 1982) in a hypotheses space of logic programs. Various approaches (Quinlan 1990; Muggleton and Feng 1990) differ mainly in the search strategy and the heuristics used to guide the search. The search Abstract