In this paper, we propose a Document Self Organizer (DoSO), an extension of the classic Self Organizing Map (SOM) model, in order to deal more efficiently with a document clustering task. Starting from a document representation model, based on important ?concepts? exploiting Wikipedia knowledge, that we have previously developed in order to overcome some of the shortcomings of the Bag-of-Words (BOW) model, we demonstrate how SOM?s performance can be boosted by using the most important concepts of the document collection to explicitly initialize the neurons. We also show how a hierarchical approach can be utilized in the SOM model and how this can lead to a more comprehensive final clustering result with hierarchical descriptive labels attached to neurons and clusters. Experiments show that the proposed model (DoSO) yields promising results both in terms of extrinsic and SOM evaluation measures.

Delegation is a mechanism that allows one agent to act on another?s privilege. It is important that the privileges should be delegated to a person who is trustworthy. In this paper, we propose a multi-level delegation model with trust management in access control systems. We organize the delegation tasks into three levels, Low, Medium, and High, according to the sensitivity of the information contained in the delegation tasks. It motivates us that the more sensitive the delegated task is, the more trustworthy the delegatee should be. In order to assess how trustworthy a delegatee is, we devise trust evaluation techniques to describe a delegatee?s trust history and also predict the future trend of trust. In our proposed delegation model, a delegatee with a higher trust level could be assigned with a higher level delegation task. Extensive experiments show that our proposed multi-level delegation model is effective in accurately predicting trust and avoiding sensitive information disclosure.

In recent years, researchers have been developing algorithms for the automatic mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. We are detecting semantic heterogeneities that occur due to conflicts among the set of Generalized Concept Inclusions, Property Subsumption Criteria, and Constraint Satisfaction Mechanism in local heterogeneous ontologies, which become obstacles for the generation of semantically consistent global merged ontology. We present several algorithms to detect such semantic inconsistencies based on subsumption analysis of concepts and properties in local ontologies from the list of initial mappings. We provide ontological patterns for resolving these inconsistencies automatically. This results global merged ontology free from ?circulatory error in class/property hierarchy?, ?common class between disjoint classes/properties?, ?redundancy of subclass/subproperty of relations? and other types of ?semantic inconsistency? errors. Experiments on the real ontologies show that our algorithms save time and cost of traversing local ontologies, improve system?s performance by producing only consistent accurate mappings, and reduce the users? dependability for ensuring the satisfiability of merged ontology.

RSS news articles that are either partially or completely duplicated in content are easily found on the Internet these days, which require Web users to sort through the articles to identify non-redundant information. This manual-filtering process is time-consuming and tedious. In this paper, we present a new filtering and clustering approach, called FICUS, which starts with identifying and eliminating redundant RSS news articles using a fuzzy set information retrieval approach and then clusters the remaining non-redundant RSS news articles according to their degrees of resemblance. FICUS uses a tree hierarchy to organize clusters of RSS news articles. The contents of the respective clusters are captured by the representative keywords from RSS news articles in the clusters so that searching and retrieval of similar RSS news articles is fast and efficient. FICUS is simple, since it uses the pre-defined word-correlation factors to determine related (words in) RSS news articles and filter redundant ones, and is supported by well-known and yet simple mathematical models, such as the standard deviation, vector space model, and probability theory, to generate clusters of non-redundant RSS news articles. Experiments performed on (test sets of) RSS news articles on various topics, which were downloaded from different online sources, verify the accuracy of FICUS on eliminating redundant RSS news articles, clustering similar RSS news articles together, and segregating different RSS news articles in terms of their contents. In addition, further empirical studies show that FICUS outperforms well-known approaches adopted for clustering RSS news articles.

The performance of database operations can be enhanced with an efficient storage structure design using attribute partitioning and/or tuple clustering. Previous research deals mostly with attribute partitioning. We address here the combined problem of attribute partitioning and tuple clustering. We propose a novel approach for this mixed fragmentation problem by applying a genetic algorithm iteratively to attribute partitioning and tuple clustering sub-problems. We compared our results to attribute-only partitioning and random search solution, resulting in a database access cost reduction of upto 70% and 67% respectively. We analyzed the effect of varying genetic parameters on the optimal solution through experimentation.