A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances

Modelo basado en agentes para las etapas de recopilación e integración de datos en el proceso de KDD

La transformación de grandes cantidades de datos en información útil y conocimiento es una inminente necesidad para la industria y la sociedad en general. Buscando cubrir esta necesidad surge el proceso de descubrimiento de conocimiento en bases de datos (Knowledge Discovery in Databases, KDD), el cual está compuesto por varias etapas. Un conjunto de estas etapas es conocido como preparación de datos y en la actualidad representa la mayor parte del esfuerzo destinado en las organizaciones al proceso de KDD. En este trabajo se presenta un análisis para dos etapas de la preparación de datos (Recopilación e Integración de Datos), y se define un modelo sobre una arquitectura distribuida, escalable, basada en Sistemas Multi-Agentes que soporta el lanzamiento de agentes que logran llevar a cabo las actividades de estas etapas en bases de datos distribuidas. Este modelo finalmente se implemento sobre un caso de estudio práctico con datos reales y simulados logrando resultados que demuestran la pertinencia del mismo. / Abstract. The transformation of large amounts of data into useful information and knowledge is an imminent need for industry and society in general. Looking to fulfill this need, the process of Knowledge Discovery in databases (KDD) arises, which consists of several stages. A set of these stages is known as data preparation and currently represents the bulk of effort in organizations during the KDD process. This document presents an analysis for two stages of Data Preparation (Data Collection and Integration), and defines a model with a distributed architecture, scalable, based on Multi-Agent Systems that supports the release of agents that fail to bring out the activities of these stages in distributed databases. This model was finally implemented on a case study with real and simulated data achieving results that demonstrate its relevance.Maestrí

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated