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Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Kernel function implicitly maps data from its original space to a higher dimensional feature space. Kernel based machine learning algorithms are typically applied to data that is not linearly separable in its original space. Although kernel methods are among the most elegant part of machine learning, it is challenging for users to define or select a proper kernel function with optimized parameter...
In many application areas, data that is being generated and processed goes beyond the petabyte scale. Analyzing such an increasing massive volume of data faces computational, as well as, statistical challenges. In order to solve these challenges, distributed and parallel processing frameworks have been used for implementing scalable data analysis algorithms. Nevertheless, processing the whole big...
The recent rise in multimedia technology has made it easier to perform a number of tasks. One of these tasks is monitoring where cheap cameras are producing large amount ofvideo data. This video data is then processed for object classification to extract useful information. However, the videodata obtained by these cheap cameras is often of low qualityand results in blur video content. Moreover, various...
Big data analysis requires adequate infrastructure and programming paradigms capable of processing large amount of data. Hadoop, the most known open-source implementation of the MapReduce paradigm, is widely employed in big data analysis frameworks. However, in many recent application scenarios data are natively distributed over different geographic regions in data centers which are inter-connected...
Big Data Systems are becoming increasingly complex and generally have very high operational costs. Cloud computing offers attractive solutions for managing large scale systems. However, one of the major bottlenecks in VM performance is virtualized I/O. Since Big Data applications and middleware rely heavily on high performance interconnects such as InfiniBand, the performance of virtualized InfiniBand...
Rule-based systems process event streams and trigger actions according to pre-defined rule-sets. Over the last three decades, such systems have been widely used in businesses, governments and organisations. However, with today's need to process larger event streams such as events produced in Internet of Things (IoT), current rule-based systems face serious challenges in terms of speed, scalability...
Academic publication archives often draw from numerous, heterogeneous sources, whose records can follow differing naming conventions. As such, ambiguity issues concerning authorship of scientific papers often arise, such as authors sharing similar names, the use of first names versus initials, or alternate name spellings for the same author. These ambiguities have plagued research on scientific collaboration...
This paper studies feasibility and scalable computing processes for visualizing big high dimensional data in a 3 dimensional space by using dimension reduction techniques. More specifically, we propose an unsupervised approach to compute a measure that is called visualizability in a 3 dimensional space for a high dimensional data. This measure of visualizability is computed based on the comparison...
Online social networks (OSNs) have attracted millions of users worldwide over the last decade. In response to a series of urgent issues faced by existing OSNs, such as information overload, single-point failure, and the privacy issue, this paper introduces a self-organized decentralized OSN (SDOSN) over a social overlay resembling real-life social graph. The social overlay considers social relationship...
In this feasibility study, we demonstrate the use of a factorgraph-based probabilistic graphical model approach to process longitudinal data derived from a population's electronic health records (EHR). Processing of EHR allows for forecasting patient-specific health complications and inference of population-level statistics on several epidemiological factors. As a case-study, we provide preliminary...
In recent years, with the development of high-throughput technologies and the increase of omics data, molecular diagnostics and the analysis of patients' exact causative genes become possible. However, massive data also challenges traditional methods. GWAS(Genome-Wide Association Study) traditional methods are usually used for causative gene discovery of single-gene disorders. But because they needs...
A huge amount of data is constantly being produced in the world. Data coming from the IoT, from scientific simulations, or from any other field of the eScience, are accumulated over historical data sets and set up the seed for future Big Data processing, with the final goal to generate added value and discover knowledge. In such computing processes, data are the main resource, however, organizing...
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