Mining complex data in highly streaming environments

Data is growing at a rapid rate because of advanced hardware and software technologies and platforms such as e-health systems, sensor networks, and social media. One of the challenging problems is storing, processing and transferring this big data in an efficient and effective way. One solution to tackle these challenges is to construct synopsis by means of data summarization techniques. Motivated by the fact that without summarization, processing, analyzing and communicating this vast amount of data is inefficient, this thesis introduces new summarization frameworks with the main goals of reducing communication costs and accelerating data mining processes in different application scenarios. Specifically, we study the following big data summarizaion techniques:(i) dimensionality reduction;(ii)clustering,and(iii)histogram, considering their importance and wide use in various areas and domains. In our work, we propose three different frameworks using these summarization techniques to cover three different aspects of big data:"Volume","Velocity"and"Variety" in centralized and decentralized platforms. We use dimensionality reduction techniques for summarizing large 2D-arrays, clustering and histograms for processing multiple data streams. With respect to the importance and rapid growth of emerging e-health applications such as tele-radiology and tele-medicine that require fast, low cost, and often lossless access to massive amounts of medical images and data over band limited channels,our first framework attempts to summarize streams of large volume medical images (e.g. X-rays) for the purpose of compression. Significant amounts of correlation and redundancy exist across different medical images. These can be extracted and used as a data summary to achieve better compression, and consequently less storage and less communication overheads on the network. We propose a novel memory-assisted compression framework as a learning-based universal coding, which can be used to complement any existing algorithm to further eliminate redundancies/similarities across images. This approach is motivated by the fact that, often in medical applications, massive amounts of correlated images from the same family are available as training data for learning the dependencies and deriving appropriate reference or synopses models. The models can then be used for compression of any new image from the same family. In particular, dimensionality reduction techniques such as Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF) are applied on a set of images from training data to form the required reference models. The proposed memory-assisted compression allows each image to be processed independently of other images, and hence allows individual image access and transmission. In the second part of our work,we investigate the problem of summarizing distributed multidimensional data streams using clustering. We devise a distributed clustering framework, DistClusTree, that extends the centralized ClusTree approach. The main difficulty in distributed clustering is balancing communication costs and clustering quality. We tackle this in DistClusTree through combining spatial index summaries and online tracking for efficient local and global incremental clustering. We demonstrate through extensive experiments the efficacy of the framework in terms of communication costs and approximate clustering quality. In the last part, we use a multidimensional index structure to merge distributed summaries in the form of a centralized histogram as another widely used summarization technique with the application in approximate range query answering. In this thesis, we propose the index-based Distributed Mergeable Summaries (iDMS) framework based on kd-trees that addresses these challenges with data generative models of Gaussian mixture models (GMMs) and a Generative Adversarial Network (GAN). iDMS maintains a global approximate kd-tree at a central site via GMMs or GANs upon new arrivals of streaming data at local sites. Experimental results validate the effectiveness and efficiency of iDMS against baseline distributed settings in terms of approximation error and communication costs

Enhanced electrical conductivity of ultrafine-grained 8Y(2)O(3) stabilized ZrO2 produced by two-step sintering technique

Two-step sintering of nanocrystalline zirconia powder stabilized with 8 mol% yttria resulted in remarkable enhancement of the electrical conductivity and significant suppression of the accelerated grain growth. The electrical conductivity of the nearly full-dense structures obtained with an average grain size of 2.15 mu m at 900 degrees C was similar to 107 mS/cm. Two-step sintering resulted in reduction of the grain size to less than 300 nm and increase of the electrical conductivity to 209 mS/cm (i.e. >95% increase). These changes accompanied with a fracture toughness increase of more that 95% (from 1.61 to 3.16 MPa m(1/2)), too. (C) 2010 Elsevier B.V. All rights reserved

Synthesis and Characterization of Al–SiC Nanocomposites Produced by Mechanical Milling and Sintering

Aluminum powder and various volume fractions of SiC particles with an average diameter of 50 nm were milled by a high-energy planetary ball mill to produce nanocrystalline Al-SiC nanocomposite powders. Double pressing/sintering process was used to consolidate powders to cylindrical specimens. It was shown that a double cycle of cold pressing and sintering can be utilized to obtain high density Al-SiC nanocomposite parts without using a hot-working step. High resolution scanning electron microscopy (HRSEM), X-ray diffraction (XRD) and laser particle size analyzer (PSA) were used to study the morphological and microstructural evolution of nanocomposite powders and bulk samples. The role of volume fraction of SiC nanoparticles in grain size of both as-milled and as-consolidated aluminum matrix was investigated. It was found that the presence of the higher SiC particles eventuate to slowly decrease in grain size of aluminum matrix powders. However, this trend is strongly noticeable in grain size of consolidated samples. The pinning effects on grain stability by SiC nanoparticles were quantitatively analyzed. It was found that Gladman's model is in close agreement with the experimentally determined grain size of Al-SiC nanocomposites

Effect of high energy ball milling on compressibility and sintering behavior of alumina nanoparticles

The effect of high-energy ball milling on the textural evolution of alumina nanopowders (compaction response, sinter-ability, grain growth and the degree of agglomeration) during post sintering process is studied. The applied pressure required for the breakage of the agglomerates (Py) during milling was estimated and the key elements of compressibility (i.e. critical pressure (Pcr) and compressibility (b)) were calculated. Based on the results, the fracture point of the agglomerates decreased from 150 to 75 MPa with prolonged milling time from 3 to 60 min. Furthermore, the powders were formed by different shaping methods such as cold isostatic press (CIP) and uniaxial press (UP) to better illustrate the influence of green compact uniformity and powder deagglomeration on the densification behavior of nanopowders