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Analytical Review of Data Visualization Methods in Application to Big Data  [PDF]
Evgeniy Yur’evich Gorodov,Vasiliy Vasil’evich Gubarev
Journal of Electrical and Computer Engineering , 2013, DOI: 10.1155/2013/969458
Abstract: This paper describes the term Big Data in aspects of data representation and visualization. There are some specific problems in Big Data visualization, so there are definitions for these problems and a set of approaches to avoid them. Also, we make a review of existing methods for data visualization in application to Big Data and taking into account the described problems. Summarizing the result, we have provided a classification of visualization methods in application to Big Data. 1. Introduction The customers need to process secondary data, which is not directly connected to the customers business which has lead to the phenomenon called Big Data. Bellow we will provide the definition of the Big Data term. Big Data, as mentioned by Gubarev Vasiliy Vasil’evich—is a phenomenon, which have no clear borders, and can be presented in unlimited or even infinite data accumulation. And even more, the accumulated data can be presented in various data formats, most of them are not structural data flows. Usually, under the term of Big Data we understand a large data set, with volume growing exponentially. This data set can be too large, too “raw”, or too unstructured for classical data processing methods, used in relational data bases theory. Still, the main concern in that question is not the data volume, but the field of application of that data [1]. It is used to provide the following Big Data properties in different analytical literature sources: large volume of data (Volume), multiformat data presentation (Variety), and high data processing speed (Velocity). It is thought that if the exact data satisfies only two of three described properties, it can be related to the Big Data class [2, 3]. Therefore, nowadays, there are the following Big Data classes: “Volume-Velocity” class, “Volume-Variety” class, “Velocity-Variety” class, and “Volume-Velocity-Variety” class. The Big Data processing is not a trivial task at all, and it requires special methods and approaches. Graphical thinking is a very simple and natural type of data processing for a human being, so, it can be said, that image data representation is an effective method, which allows for easing data understanding and provides enough support for decision making. But, in case of Big Data, most of classical data representation methods become less effective or even not applicable for concrete tasks. Analysis of applicability for one of the concrete classes of Big Data is a topical problem of subject area as there are no such case studies held before. Therefore, there is a purpose for this paper:
Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization  [PDF]
Xiaoquan Su, Weihua Pan, Baoxing Song, Jian Xu, Kang Ning
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0089323
Abstract: The metagenomic method directly sequences and analyses genome information from microbial communities. The main computational tasks for metagenomic analyses include taxonomical and functional structure analysis for all genomes in a microbial community (also referred to as a metagenomic sample). With the advancement of Next Generation Sequencing (NGS) techniques, the number of metagenomic samples and the data size for each sample are increasing rapidly. Current metagenomic analysis is both data- and computation- intensive, especially when there are many species in a metagenomic sample, and each has a large number of sequences. As such, metagenomic analyses require extensive computational power. The increasing analytical requirements further augment the challenges for computation analysis. In this work, we have proposed Parallel-META 2.0, a metagenomic analysis software package, to cope with such needs for efficient and fast analyses of taxonomical and functional structures for microbial communities. Parallel-META 2.0 is an extended and improved version of Parallel-META 1.0, which enhances the taxonomical analysis using multiple databases, improves computation efficiency by optimized parallel computing, and supports interactive visualization of results in multiple views. Furthermore, it enables functional analysis for metagenomic samples including short-reads assembly, gene prediction and functional annotation. Therefore, it could provide accurate taxonomical and functional analyses of the metagenomic samples in high-throughput manner and on large scale.
Data sonification and sound visualization  [PDF]
Hans G. Kaper,Sever Tipei,Elizabeth Wiebel
Computer Science , 2000,
Abstract: This article describes a collaborative project between researchers in the Mathematics and Computer Science Division at Argonne National Laboratory and the Computer Music Project of the University of Illinois at Urbana-Champaign. The project focuses on the use of sound for the exploration and analysis of complex data sets in scientific computing. The article addresses digital sound synthesis in the context of DIASS (Digital Instrument for Additive Sound Synthesis) and sound visualization in a virtual-reality environment by means of M4CAVE. It describes the procedures and preliminary results of some experiments in scientific sonification and sound visualization.
Visualization of Collaborative Data  [PDF]
Guobiao Mei,Christian R. Shelton
Computer Science , 2012,
Abstract: Collaborative data consist of ratings relating two distinct sets of objects: users and items. Much of the work with such data focuses on filtering: predicting unknown ratings for pairs of users and items. In this paper we focus on the problem of visualizing the information. Given all of the ratings, our task is to embed all of the users and items as points in the same Euclidean space. We would like to place users near items that they have rated (or would rate) high, and far away from those they would give a low rating. We pose this problem as a real-valued non-linear Bayesian network and employ Markov chain Monte Carlo and expectation maximization to find an embedding. We present a metric by which to judge the quality of a visualization and compare our results to local linear embedding and Eigentaste on three real-world datasets.
The importance of visualization in cartographic communication
Ikonovi? Vesna,?ivkovi? Dragica,?or?evi? Aleksandar
Glasnik Srpskog Geografskog Dru?tva , 2011, DOI: 10.2298/gsgd1104159i
Abstract: Visualization is a field of computer graphics which explores the analytical and communication possibilities of visual presentation. Visualization explores the possibilities of using images, similar to three-dimensional world, as models so that analysis and communication can be improved. Visualization depends on new computer techniques of data analysis and presentation, as well as on the accuracy, exactness and form of said data. Visualization is a scientific tool, but its application demands art, imagination and intuition. Visualization demands the use of the latest and the best computer technology.
inPHAP: Interactive visualization of genotype and phased haplotype data  [PDF]
Günter J?ger,Alexander Peltzer,Kay Nieselt
Computer Science , 2014,
Abstract: Background: To understand individual genomes it is necessary to look at the variations that lead to changes in phenotype and possibly to disease. However, genotype information alone is often not sufficient and additional knowledge regarding the phase of the variation is needed to make correct interpretations. Interactive visualizations, that allow the user to explore the data in various ways, can be of great assistance in the process of making well informed decisions. But, currently there is a lack for visualizations that are able to deal with phased haplotype data. Results: We present inPHAP, an interactive visualization tool for genotype and phased haplotype data. inPHAP features a variety of interaction possibilities such as zooming, sorting, filtering and aggregation of rows in order to explore patterns hidden in large genetic data sets. As a proof of concept, we apply inPHAP to the phased haplotype data set of Phase 1 of the 1000 Genomes Project. Thereby, inPHAP's ability to show genetic variations on the population as well as on the individuals level is demonstrated for several disease related loci. Conclusions: As of today, inPHAP is the only visual analytical tool that allows the user to explore unphased and phased haplotype data interactively. Due to its highly scalable design, inPHAP can be applied to large datasets with up to 100 GB of data, enabling users to visualize even large scale input data. inPHAP closes the gap between common visualization tools for unphased genotype data and introduces several new features, such as the visualization of phased data.
Visualization of microarray gene expression data  [cached]
Tangirala Venkateswara Prasad,Syed Ismail Ahson
Bioinformation , 2006,
Abstract: Microarray gene expression data is used in various biological and medical investigations. Processing of gene expression data requires algorithms in data mining, process automation and knowledge discovery. Available data mining algorithms exploits various visualization techniques. Here, we describe the merits and demerits of various visualization parameters used in gene expression analysis.
Data visualization in political and social sciences  [PDF]
Andrei Zinovyev
Computer Science , 2010,
Abstract: The basic objective of data visualization is to provide an efficient graphical display for summarizing and reasoning about quantitative information. During the last decades, political science has accumulated a large corpus of various kinds of data such as comprehensive factbooks and atlases, characterizing all or most of existing states by multiple and objectively assessed numerical indicators within certain time lapse. As a consequence, there exists a continuous trend for political science to gradually become a more quantitative scientific field and to use quantitative information in the analysis and reasoning. It is believed that any objective analysis in political science must be multidimensional and combine various sources of quantitative information; however, human capabilities for perception of large massifs of numerical information are limited. Hence, methods and approaches for visualization of quantitative and qualitative data (and, especially multivariate data) is an extremely important topic. Data visualization approaches can be classified into several groups, starting from creating informative charts and diagrams (statistical graphics and infographics) and ending with advanced statistical methods for visualizing multidimensional tables containing both quantitative and qualitative information. In this article we provide a short review of existing methods of data visualization methods with applications in political and social science.
Data Visualization for University Research Papers  [PDF]
Alpa K. Oza
International Journal of Soft Computing & Engineering , 2013,
Abstract: Quite many publications are being published either in form of Theses, essays or Research papers at various levels of scientists, research scholars or Ph.D students. This is a big jargon. They are required to be segregated under various Topics. Topic modeling is a set of tool that provides a solution. Topic modeling discovers a hidden thematic structure in collection of documents. Topic models are high level statistical tools. A user must scrutinize numerical distribution to understand and explore their results. Latent Dirichlet Allocation LDA has been used to generate automatically topics of text corpora and also to subdivide the corpus words among those topics. Topic models also fall in the same line of functioning. This model (topic model) has proven remarkably powerful for information retrieval tasks. Information visualization technologies when used in conjunction with data mining and text analyses tools can be of great value for various types of tasks. For this reason various visualizations have been designed. Quite laborious work has been done and still being labored at various levels of scholars. Here our aim is to present a brief description to the topical method of visualization under data mining.
A Rendering Method for Visualization of Medical Data  [cached]
Fei He,Xia Li
Modern Applied Science , 2010, DOI: 10.5539/mas.v4n12p126
Abstract: This article describes the common visualization of medical images and effective optimization method proposed from different perspectives. Ultimately, the authors propose their own best method and use test data to prove the validity of the method.
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