17445118
User Group Profile Modeling Based on User Transactional Data for Personalized Systems
In this paper, we propose a framework named UMT (User-profile Modeling based on Transactional data) for modeling user group profiles based on the transactional data. UMT is a generic framework for application systems that keep the historical transactions of their users. In UMT, user group profiles consist of three types: basic information attributes, synthetic attributes and probability distribution attributes. User profiles are constructed by clustering user transaction data and integrating cluster attributes with domain information extracted from application systems and other external data sources. The characteristic of UMT makes it suitable for personalization of transaction-based commercial application systems. A case study is presented to illustrate how to use UMT to create a personalized tourism system capable of using domain information in intelligent ways and of reacting to external events.
1856845
Typing evolving ambients
This paper presents a type system that deals with the type evolution problem of the ambient calculus. It breaks the law that the open capability must introduce to the opening process the exact type information of the opened ambient. It can also serve as a general type architecture supporting type evolution. A typical usage of the type system is illustrated in the immobile server example.
6895989
Enhanced Algorithm for Mining Frequently Visited Page Groups
5083193
A Comparative Study on Feature Selection in Text Categorization
This paper is a comparative study of feature selection methods in statistical learning of text categorization The focus is on aggres sive dimensionality reduction Five meth ods were evaluated including term selection based on document frequency DF informa tion gain IG mutual information MI a test CHI and term strength TS We found IG and CHI most e ective in our ex periments Using IG thresholding with a k nearest neighbor classi er on the Reuters cor pus removal of up to removal of unique terms actually yielded an improved classi cation accuracy measured by average preci sion DF thresholding performed similarly Indeed we found strong correlations between the DF IG and CHI values of a term This suggests that DF thresholding the simplest method with the lowest cost in computation can be reliably used instead of IG or CHI when the computation of these measures are too expensive TS compares favorably with the other methods with up to vocabulary reduction but is not competitive at higher vo cabulary reduction levels In contrast MI had relatively poor performance due to its bias towards favoring rare terms and its sen sitivity to probability estimation errors
5091874
Integrating Web Services into Ontology-Based Web Portal
With the explosive emerged Web services, the Web becomes a world of information and applications. So portals whose goal is presenting a structured view onto the Web should provide users with a single point access to multiple types of not only information but also services. Recently, many systems have been developed using ontology-driven approaches to automatically generate and manage Web portals. However, all of them lack the integration of Web services. In this paper, we describe SPortS, an OWL-DL(Web Ontology Language-Description Logics) based portal generation system that integrates semantic Web services into generated portals at the presentation level, the data level and the function level. We present the portal generation process, the query decomposition and the service recommendation algorithms through which semantic Web services are integrated tightly into the generated portal.
12472387
CLID: A general approach to validate security policies in a dynamic network
Many researchers have considered security policy management, including how to configure policies manually and even how to automatically generate security policies based on security requirements. Both can be error prone, especially when properties of the network topology change, because security requirements are usually not bound to any particular route path. Our DETER lab emulation results show that conflicts could be caused by these factors. Therefore, a systematic way to validate the correctness of the security policies is essential. This paper presents an approach, CLID (conflict and looping identification and detection), to verify whether a set of security policies (e.g. IPSec/VPN tunnels) satisfy the given security requirements, without causing any conflicts. This approach utilizes the definition of a security policy lo include network routing data as well as traffic selector information, thus it works for general network topologies. We also analyze and justify the correctness of the approach. The paper concludes with our simulation results and addresses future work.
1409762
A scalability analysis of classifiers in text categorization
Real-world applications of text categorization often require a system to deal with tens of thousands of categories defined over a large taxonomy. This paper addresses the problem with respect to a set of popular algorithms in text categorization, including Support Vector Machines, k-nearest neighbor, ridge regression, linear least square fit and logistic regression. By providing a formal analysis of the computational complexity of each classification method, followed by an investigation on the usage of different classifiers in a hierarchical setting of categorization, we show how the scalability of a method depends on the topology of the hierarchy and the category distributions. In addition, we are able to obtain tight bounds for the complexities by using the power law to approximate category distributions over a hierarchy. Experiments with kNN and SVM classifiers on the OHSUMED corpus are reported on, as concrete examples.
7974265
A Probabilistic Model for Online Document Clustering with Application to Novelty Detection
In this paper we propose a probabilistic model for online document clustering. We use non-parametric Dirichlet process prior to model the growing number of clusters, and use a prior of general English language model as the base distribution to handle the generation of novel clusters. Furthermore, cluster uncertainty is modeled with a Bayesian Dirichlet-multinomial distribution. We use empirical Bayes method to estimate hyperparameters based on a historical dataset. Our probabilistic model is applied to the novelty detection task in Topic Detection and Tracking (TDT) and compared with existing approaches in the literature.