Mo Adda

Network fault detection with Wiener filter-based agent

Over the last few decades, network domains have become more and more advanced in terms of their size, complexity and level of heterogeneity. Existing centralized-based network management approaches suffer from problems such as insufficient scalability, availability and flexibility, as networks become more distributed. Mobile agents (MA), upgraded with intelligence, can present a reasonable new technology that will help to achieve distributed management. These agents migrate from one node to another, accessing an appropriate subset of Management Information Base (MIB) variables from each node analysing them locally and retaining the results of this analysis during their subsequent migration. One of the network fault management tasks is fault detection, and in this paper our purpose was to carry out a statistical method based on Wiener filter to capture the abnormal changes in the behaviour of the MIB variables. Our algorithm was implemented on data obtained from two different scenarios in the laboratory, with four different fault case studies. The purpose of this is to provide the manager node with a high level of information, such as a set of conclusions or recommendations, rather than large volumes of data relating to each management task. The filtering process is carried out concurrently by each agent responsible for a particular domain and device, proving to be more scalable and efficient.

Analysis of mobile agents in network fault management

Network domains have become more and more advanced in terms of their size, complexity and the level of heterogeneity. Comprehensive fault management is the most significant challenge in network management. Fault management can help increase the availability of the network by quickly identifying the faults and then, proactively, start the recovery process. Current centralized configured network management systems suffer from problems such as insufficient scalability, availability and flexibility as networks become more distributed. Mobile agents (MAs), with integral intelligence, can present a reasonable new technology that will help to achieve distributed management, several researchers have embraced these approaches. In this paper, we introduce a general analytical model for network management client/server (CS) and MA paradigms. We express how to build up an analytical framework, which can be used to quantitatively assess the performances of the MA and CS paradigms under different scenarios. We present some numerical and experimental results that demonstrate the applicability of our proposed framework, which will be based on a combination of MA and CS schemes called Adaptive Intelligent Mobile Agent.

PMCRI: A Parallel Modular Classification Rule Induction Framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.

P-Prism: a computationally efficient approach to scaling up classification rule induction

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Hierarchical classification for dealing with the Class imbalance problem

The aim of classification in machine learning is to utilize knowledge gained from applying learning algorithms on a given data so as determine what class an unlabelled data having same pattern belongs to. However, algorithms do not learn properly when a massive difference in size between data classes exist. This classification problem exists in many real world application domains and has been a popular area of focus by machine learning and data mining researchers. The class imbalance problem is further made complex with the presence of associative data difficult factors. The duo have proven to greatly deteriorate classification performance. This paper introduces a two-phased data level approach for binary classes which entails the temporary re-labelling of classes. The proposed approach takes advantage of the local neighbourhood of the minority instances to identify and treat difficult examples belonging to both classes. Its outcome was satisfactory when compared against various data-level methods using datasets extracted from KEEL and UCI datasets repository.

J-PMCRI: A Methodology for Inducing Pre-pruned Modular Classification Rules

Inducing rules from very large datasets is one of the most challenging areas in data mining. Several approaches exist to scaling up classification rule induction to large datasets, namely data reduction and the parallelisation of classification rule induction algorithms. In the area of parallelisation of classification rule induction algorithms most of the work has been concentrated on the Top Down Induction of Decision Trees (TDIDT), also known as the ‘divide and conquer’ approach. However powerful alternative algorithms exist that induce modular rules. Most of these alternative algorithms follow the ‘separate and conquer’ approach of inducing rules, but very little work has been done to make the ‘separate and conquer’ approach scale better on large training data. This paper examines the potential of the recently developed blackboard based J-PMCRI methodology for parallelising modular classification rule induction algorithms that follow the ‘separate and conquer’ approach. A concrete implementation of the methodology is evaluated empirically on very large datasets.

Parallel Rule Induction with Information Theoretic Pre-Pruning

In a world where data is captured on a large scale the major challenge for data mining algorithms is to be able to scale up to large datasets. There are two main approaches to inducing classification rules, one is the divide and conquer approach, also known as the top down induction of decision trees; the other approach is called the separate and conquer approach. A considerable amount of work has been done on scaling up the divide and conquer approach. However, very little work has been conducted on scaling up the separate and conquer approach.In this work we describe a parallel framework that allows the parallelisation of a certain family of separate and conquer algorithms, the Prism family. Parallelisation helps the Prism family of algorithms to harvest additional computer resources in a network of computers in order to make the induction of classification rules scale better on large datasets. Our framework also incorporates a pre-pruning facility for parallel Prism algorithms.

Parallel Induction of Modular Classification Rules

The Distributed Rule Induction (DRI) project at the University of Portsmouth is concerned with distributed data mining algorithms for automatically generating rules of all kinds. In this paper we present a system architecture and its implementation for inducing modular classification rules in parallel in a local area network using a distributed blackboard system. We present initial results of a prototype implementation based on the Prism algorithm.

Quality of Service: Dynamic Authentication Bandwidth Management for the Wireless Environment

With the popularity of distributed applications such as BitTorrent and Peer 2 Peer (P2P) networks, coupled with the increase in mobility of end user devices, there is a requirement for dynamic bandwidth management. This paper proposes a dynamic wireless bandwidth management system that allocates bandwidth dynamically to users as they authenticate with a wireless access point (AP). As users log into the system and out on an adhoc basis the bandwidth is dynamically redistributed with each event.

Personality Filter in Mobile Networks with Communication Constraints

A new psychological model for efficient data transmission in mobile networks under communication constrains is proposed. It accounts for user personality characteristics to determine the feasible path for packet transmission from source to target and to predict the availability of the communication resources based on dynamically determined level of node generosity. Two case studies are presented with initial performance better than chance. The model is designed for the developed framework of evolving fuzzy modeling in mobile Ad hoc networks via lightweight online unsupervised learning.