An ontology enhanced parallel SVM for scalable spam filter training

This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart

RDF Querying

Reactive Web systems, Web services, and Web-based publish/ subscribe systems communicate events as XML messages, and in many cases require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. Emphasizing language design and formal semantics, we describe the rule-based query language XChangeEQ for detecting composite events. XChangeEQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as model and fixpoint theories; while this is an established approach for rule languages, it has not been applied for event queries before

Scalability considerations for multivariate graph visualization

Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations

Model-Based Mitigation of Availability Risks

The assessment and mitigation of risks related to the availability of the IT infrastructure is becoming increasingly important in modern organizations. Unfortunately, present standards for Risk Assessment and Mitigation show limitations when evaluating and mitigating availability risks. This is due to the fact that they do not fully consider the dependencies between the constituents of an IT infrastructure that are paramount in large enterprises. These dependencies make the technical problem of assessing availability issues very challenging. In this paper we define a method and a tool for carrying out a Risk Mitigation activity which allows to assess the global impact of a set of risks and to choose the best set of countermeasures to cope with them. To this end, the presence of a tool is necessary due to the high complexity of the assessment problem. Our approach can be integrated in present Risk Management methodologies (e.g. COBIT) to provide a more precise Risk Mitigation activity. We substantiate the viability of this approach by showing that most of the input required by the tool is available as part of a standard business continuity plan, and/or by performing a common tool-assisted Risk Management