Fast adaptive real-time classification for data streams with concept drift

An important application of Big Data Analytics is the real-time analysis of streaming data. Streaming data imposes unique challenges to data mining algorithms, such as concept drifts, the need to analyse the data on the fly due to unbounded data streams and scalable algorithms due to potentially high throughput of data. Real-time classification algorithms that are adaptive to concept drifts and fast exist, however, most approaches are not naturally parallel and are thus limited in their scalability. This paper presents work on the Micro-Cluster Nearest Neighbour (MC-NN) classifier. MC-NN is based on an adaptive statistical data summary based on Micro-Clusters. MC-NN is very fast and adaptive to concept drift whilst maintaining the parallel properties of the base KNN classifier. Also MC-NN is competitive compared with existing data stream classifiers in terms of accuracy and speed.

Pocket data mining - big data on small devices

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Computationally efficient rule-based classification for continuous streaming data

Advances in hardware and software technologies allow to capture streaming data. The area of Data Stream Mining (DSM) is concerned with the analysis of these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (concept drift) in the stream in real-time in order to reflect the most recent concept in the data as accurately as possible. A recent addition to the data stream classifier toolbox is eRules which induces and updates a set of expressive rules that can easily be interpreted by humans. However, like most rule-based data stream classifiers, eRules exhibits a poor computational performance when confronted with continuous attributes. In this work, we propose an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up the real-time rule induction process while maintaining a similar level of accuracy compared with the original eRules classifier. We termed this new version of eRules with our approach G-eRules.

Reflections on the concept of the central executive

Notes that the central executive lies at the heart of A. D. Baddeley's theory of working memory, but that what it does has been characterized in several different ways. This chapter reviews and brings together various interpretations of central executive functioning. The authors describe an experiment involving a novel cognitive task to point out the restrictive nature of the term 'executive capacity.' The chapter concludes by commenting briefly on the implications of a working memory model in which the executive does not play a major part.

In situ observation of lithium hydride hydrolysis by DRIFT spectroscopy

Polycrystalline LiH was studied in situ using diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy to investigate the effect water vapour has on the rate of production of the corrosion products, particularly LiOH. The reaction rate of the formation of surface LiOH was monitored by measurement of the hydroxyl (OH) band at 3676 cm(-1). The initial hydrolysis rate of LiH exposed to water vapour at 50% relative humidity was found to be almost two times faster than LiH exposed to water vapour at 2% relative humidity. The hydrolysis rate was shown to be initially very rapid followed by a much slower, almost linear rate. The change in hydrolysis rate was attributed to the formation of a coherent layer of LiOH on the LiH surface. Exposure to lower levels of water vapour appeared to result in the formation of a more coherent corrosion product, resulting in effective passivation of the surface to further attack from water. Crown Copyright (c) 2007 Published by Elsevier B.V. All rights reserved.

Microblock ionomers: a new concept in high temperature, swelling-resistant membranes for PEM fuel cells

A novel series of polyaromatic ionomers with similar equivalent weights but very different sulphonic acid distributions along the ionomer backbone has been designed and prepared. By synthetically organising the sequence-distribution so that it consists of fully defined ionic segments (containing singlets, doublets or quadruplets of sulphonic acid groups) alternating strictly with equally well-defined nonionic spacer segments, a new class of polymers which may be described as microblock ionomers has been developed. These materials exhibit very different properties and morphologies from analogous randomly substituted systems. Progressively extending the nonionic spacer length in the repeat unit (maintaining a constant equivalent weight by increasing the degree of sulphonation. of the ionic segment) leads to an increasing degree of nanophase separation between hydrophilic and hydrophobic domains in these materials. Membranes cast from ionomers with the more highly phase-separated morphologies show significantly higher onset temperatures for uncontrolled swelling in water. This new type of ionomer design has enabled the fabrication of swelling-resistant hydrocarbon membranes, suitable for fuel cell operation, with very much higher ion exchange capacities (>2 meq g(-1)) than those previously reported in the literature. When tested in a fuel cell at high temperature (120 degrees C) and low relative humidity (35% RH), the best microblock membrane matched the performance of Nafion 112. Moreover, comparative low load cycle testing of membrane -electrode assemblies suggests that the durability of the new membranes under conditions of high temperature and low relative humidity is superior to that of conventional perfluorinated materials.

Measuring toughness and the cohesive stress-displacement relationship by the essential work of fracture concept

The complete fracture behaviour of ductile double edge notched tension (DENT) specimen is analysed with an approximate model, which is then used to discuss the essential work of fracture (EWF) concept. The model results are compared with the experimental results for an aluminium alloy 6082-O. The restrictions on the ligament size for valid application of the EWF method are discussed with the aid of the model. The model is used to suggest an improved method of obtaining the cohesive stress-displacement relationship for the fracture process zone (FPZ).