An algorithm to discover the k-clique cover in networks

In social network analysis, a k-clique is a relaxed clique, i.e., a k-clique is a quasi-complete sub-graph. A k-clique in a graph is a sub-graph where the distance between any two vertices is no greater than k. The visualization of a small number of vertices can be easily performed in a graph. However, when the number of vertices and edges increases the visualization becomes incomprehensible. In this paper, we propose a new graph mining approach based on k-cliques. The concept of relaxed clique is extended to the whole graph, to achieve a general view, by covering the network with k-cliques. The sequence of k-clique covers is presented, combining small world concepts with community structure components. Computational results and examples are presented

Clique communities in social networks

Given the large amount of data provided by the Web 2.0, there is a pressing need to obtain new metrics to better understand the network structure; how their communities are organized and the way they evolve over time. Complex network and graph mining metrics are essentially based on low complexity computational procedures like the diameter of the graph, clustering coefficient and the degree distribution of the nodes. The connected communities in the social networks have, essentially, been studied in two contexts: global metrics like the clustering coefficient and the node groups, such as the graph partitions and clique communities

Abstraction, aggregation and recursion for generating accurate and simple classifiers

An important goal of inductive learning is to generate accurate and compact classifiers from data. In a typical inductive learning scenario, instances in a data set are simply represented as ordered tuples of attribute values. In our research, we explore three methodologies to improve the accuracy and compactness of the classifiers: abstraction, aggregation, and recursion;Firstly, abstraction is aimed at the design and analysis of algorithms that generate and deal with taxonomies for the construction of compact and robust classifiers. In many applications of the data-driven knowledge discovery process, taxonomies have been shown to be useful in constructing compact, robust, and comprehensible classifiers. However, in many application domains, human-designed taxonomies are unavailable. We introduce algorithms for automated construction of taxonomies inductively from both structured (such as UCI Repository) and unstructured (such as text and biological sequences) data. We introduce AVT-Learner, an algorithm for automated construction of attribute value taxonomies (AVT) from data, and Word Taxonomy Learner (WTL), an algorithm for automated construction of word taxonomy from text and sequence data. We describe experiments on the UCI data sets and compare the performance of AVT-NBL (an AVT-guided Naive Bayes Learner) with that of the standard Naive Bayes Learner (NBL). Our results show that the AVTs generated by AVT-Learner are compeitive with human-generated AVTs (in cases where such AVTs are available). AVT-NBL using AVTs generated by AVT-Learner achieves classification accuracies that are comparable to or higher than those obtained by NBL; and the resulting classifiers are significantly more compact than those generated by NBL. Similarly, our experimental results of WTL and WTNBL on protein localization sequences and Reuters newswire text categorization data sets show that the proposed algorithms can generate Naive Bayes classifiers that are more compact and often more accurate than those produced by standard Naive Bayes learner for the Multinomial Model;Secondly, we apply aggregation to construct features as a multiset of values for the intrusion detection task. For this task, we propose a bag of system calls representation for system call traces and describe misuse and anomaly detection results on the University of New Mexico (UNM) and MIT Lincoln Lab (MIT LL) system call sequences with the proposed representation. With the feature representation as input, we compare the performance of several machine learning techniques for misuse detection and show experimental results on anomaly detection. The results show that standard machine learning and clustering techniques using the simple bag of system calls representation based on the system call traces generated by the operating system\u27s kernel is effective and often performs better than approaches that use foreign contiguous sequences in detecting intrusive behaviors of compromised processes;Finally, we construct a set of classifiers by recursive application of the Naive Bayes learning algorithms. Naive Bayes (NB) classifier relies on the assumption that the instances in each class can be described by a single generative model. This assumption can be restrictive in many real world classification tasks. We describe recursive Naive Bayes learner (RNBL), which relaxes this assumption by constructing a tree of Naive Bayes classifiers for sequence classification, where each individual NB classifier in the tree is based on an event model (one model for each class at each node in the tree). In our experiments on protein sequences, Reuters newswire documents and UC-Irvine benchmark data sets, we observe that RNBL substantially outperforms NB classifier. Furthermore, our experiments on the protein sequences and the text documents show that RNBL outperforms C4.5 decision tree learner (using tests on sequence composition statistics as the splitting criterion) and yields accuracies that are comparable to those of support vector machines (SVM) using similar information

Quality of Service (QoS) in SOA Systems. A Systematic Review

In the last recent years a new technology called Web Services has emerged. The main characteristic of a web service is that it is a piece of software that the user can utilize but doesn’t own, that is, the user doesn’t install the software but uses it through the internet and standard protocols. With this new technology, a new architecture paradigm called SOA (Service Oriented Architecture) has appeared. This architecture is based on combining several web services, each one responsible to develop a concrete task, in order to obtain full‐operational software. The web services that compose a SOA System might be able to perform a task in a certain time, might be unavailable in some cases, might have security policies, etc. All this attributes, named Quality attributes, are essential in order to choose the appropriate web service for a SOA System. The objective of this Master Thesis is focused on two different but related subjects: (1) The development of a review regarding to the Quality Attributes for web services in a systematic manner and the development of a tool for monitoring SOA Systems capable to be used in several frameworks such as for Self‐Adaptive SOA Systems and for Web Service Discovery Systems

Legal Ontology for Nexus: Water, Energy and Food in EU Regulations

Objectives of the thesis are – (a) to identify the problems in water-energy-food nexus from ICT and Law point of view and to propose theoretically a legal knowledge framework for water-energy-food nexus in order to reduce those problems technologically, (b) to construct and implement legal ontology for nexus extracted from EU water, energy and food Regulations in OWL 2 language, which is a part of the grater work of implementing legal knowledge framework for water-energy-food nexus pro-posed through the compilation of objective (a). Considering these objectives, this thesis presents total five chapters. Chapter 1 is dedicated to fulfill the requirement of objective (a) and the rest chapters are devoted for objective (b). More particularly chapter four presents technical descriptions of the legal ontology for nexus, while chapter two and three articulate methodological aspect of it. Chapter five evaluates legal ontology for nexus. Additionally, besides the list of references, annex 1 delivers all asserted restrictions used in this ontology and annex 2 provides the links of all modules and documentations of legal ontology for nexus.Erasmus Mundus Joint Doctorate programme in “Law, Science and Technology