A new analytics model for large scale multidimensional data visualization

© Springer International Publishing Switzerland 2015. With The Rise Of Big Data, The Challenge For Modern Multidimen-Sional Data Analysis And Visualization Is How It Grows Very Quickly In Size And Complexity. In This Paper, We First Present A Classification Method Called The 5ws Dimensions Which Classifies Multidimensional Data Into The 5ws Definitions. The 5ws Dimensions Can Be Applied To Multiple Datasets Such As Text Datasets, Audio Datasets And Video Datasets. Second, We Establish A Pair-Density Model To Analyze The Data Patterns To Compare The Multidimensional Data On The 5ws Patterns. Third, We Created Two Additional Parallel Axes By Using Pair-Density For Visualization. The Attributes Has Been Shrunk To Reduce Data Over-Crowding In Pair-Density Parallel Coordinates. This Has Achieved More Than 80% Clutter Reduction Without The Loss Of Information. The Experiment Shows That Our Model Can Be Efficiently Used For Big Data Analysis And Visualization

Extending Hybrid CSP with Probability and Stochasticity

Probabilistic and stochastic behavior are omnipresent in computer controlled systems, in particular, so-called safety-critical hybrid systems, because of fundamental properties of nature, uncertain environments, or simplifications to overcome complexity. Tightly intertwining discrete, continuous and stochastic dynamics complicates modelling, analysis and verification of stochastic hybrid systems (SHSs). In the literature, this issue has been extensively investigated, but unfortunately it still remains challenging as no promising general solutions are available yet. In this paper, we give our effort by proposing a general compositional approach for modelling and verification of SHSs. First, we extend Hybrid CSP (HCSP), a very expressive and process algebra-like formal modeling language for hybrid systems, by introducing probability and stochasticity to model SHSs, which is called stochastic HCSP (SHCSP). To this end, ordinary differential equations (ODEs) are generalized by stochastic differential equations (SDEs) and non-deterministic choice is replaced by probabilistic choice. Then, we extend Hybrid Hoare Logic (HHL) to specify and reason about SHCSP processes. We demonstrate our approach by an example from real-world.Comment: The conference version of this paper is accepted by SETTA 201

RELT - Visualizing trees on mobile devices

The small screens on increasingly used mobile devices challenge the traditional visualization methods designed for desktops. This paper presents a method called "Radial Edgeless Tree" (RELT) for visualizing trees in a 2-dimensional space. It combines the existing connection tree drawing with the space-filling approach to achieve the efficient display of trees in a small geometrical area, such as the screen that are commonly used in mobile devices. We recursively calculate a set of non-overlapped polygonal nodes that are adjacent in the hierarchical manner. Thus, the display space is fully used for displaying nodes, while the hierarchical relationships among the nodes are presented by the adjacency (or boundary-sharing) of the nodes. It is different from the other traditional connection approaches that use a node-link diagram to present the parent-child relationships which waste the display space. The hierarchy spreads from north-west to south-east in a top-down manner which naturally follows the traditional way of human perception of hierarchies. We discuss the characteristics, advantages and limitations of this new technique and suggestions for future research. © Springer-Verlag Berlin Heidelberg 2007

Two axes re-ordering methods in parallel coordinates plots

© 2015 Elsevier Ltd. Visualization and interaction of multidimensional data are challenges in visual data analytics, which requires optimized solutions to integrate the display, exploration and analytical reasoning of data into one visual pipeline for human-centered data analysis and interpretation. Even though it is considered to be one of the most popular techniques for visualization and analysis of multidimensional data, parallel coordinate visualization is also suffered from the visual clutter problem as well as the computational complexity problem, same as other visualization methods in which visual clutter occurs where the volume of data needs to be visualized to be increasing. One straightforward way to address these problems is to change the ordering of axis to reach the minimal number of visual clutters. However, the optimization of the ordering of axes is actually a NP-complete problem. In this paper, two axes re-ordering methods are proposed in parallel coordinates visualization: (1) a contribution-based method and (2) a similarity-based method.The contribution-based re-ordering method is mainly based on the singular value decomposition (SVD) algorithm. It can not only provide users with the mathmetical theory for the selection of the first remarkable axis, but also help with visualizing detailed structure of the data according to the contribution of each data dimension. This approach reduces the computational complexity greatly in comparison with other re-ordering methods. A similarity-based re-ordering method is based on the combination of nonlinear correlation coefficient (NCC) and SVD algorithms. By using this approach, axes are re-ordered in line with the degree of similarities among them. It is much more rational, exact and systemic than other re-ordering methods, including those based on Pearson's correlation coefficient (PCC). Meanwhile, the paper also proposes a measurement of contribution rate of each dimension to reveal the property hidden in the dataset. At last, the rationale and effectiveness of these approaches are demonstrated through case studies. For example, the patterns of Smurf and Neptune attacks hidden in KDD 1999 dataset are visualized in parallel coordinates using contribution-based re-ordering method; NCC re-ordering method can enlarge the mean crossing angles and reduce the amount of polylines between the neighboring axes

An integrated visual framework for the human-Web interface

© 2002 IEEE. The design of Web sites has been largely ad hoc, with little concern about the effectiveness of navigation and maintenance. This paper presents a general framework with a human-Web interface that supports Web design through visual programming and reverse Web engineering through visualization. The paper describes the framework in the context of a Web tool, known as HWIT which has been developed for a pilot study

TreemapBar: Visualizing additional dimensions of data in bar chart

Bar chart is a very common and simple graph that ismainly used to visualize simple x, y plots of data for numerical comparisons by partitioning the categorical data values into bars and typically limited to operate on highly aggregated dataset. In today's growing complexity of business data with multi dimensional attributes using bar chart itself is not sufficient to deal with the representation of such business dataset and it also not utilizes the screen space efficiently. Nevertheless, bar chart is still useful because of its shape create strong visual attention to users at first glance than other visualization techniques. In this article, we present a treemap bar chart + tablelens interaction technique that combines the treemap and bar chart visualizations with a tablelens based zooming technique that allows users to view the detail of a particular bar when the density of bars increases. In our approach, the capability of the original bar chart and treemaps for representing complex business data is enhanced and the utilization of display space is also optimized. © 2009 IEEE

Learning preferences for large scale multi-label problems

Despite that the majority of machine learning approaches aim to solve binary classification problems, several real-world applications require specialized algorithms able to handle many different classes, as in the case of single-label multi-class and multi-label classification problems. The Label Ranking framework is a generalization of the above mentioned settings, which aims to map instances from the input space to a total order over the set of possible labels. However, generally these algorithms are more complex than binary ones, and their application on large-scale datasets could be untractable. The main contribution of this work is the proposal of a novel general online preference-based label ranking framework. The proposed framework is able to solve binary, multi-class, multi-label and ranking problems. A comparison with other baselines has been performed, showing effectiveness and efficiency in a real-world large-scale multi-label task

Visual clustering of spam emails for DDoS analysis

Networking attacks embedded in spam emails are increasingly becoming numerous and sophisticated in nature. Hence this has given a growing need for spam email analysis to identify these attacks. The use of these intrusion detection systems has given rise to other two issues, 1) the presentation and understanding of large amounts of spam emails, 2) the user-assisted input and quantified adjustment during the analysis process. In this paper we introduce a new analytical model that uses two coefficient vectors: 'density' and 'weight'for the analysis of spam email viruses and attacks. We then use a visual clustering method to classify and display the spam emails. The visualization allows users to interactively select and scale down the scope of views for better understanding of different types of the spam email attacks. The experiment shows that this new model with the clustering visualization can be effectively used for network security analysis. © 2011 IEEE

An improved wavelet analysis method for detecting DDoS attacks

Wavelet Analysis method is considered as one of the most efficient methods for detecting DDoS attacks. However, during the peak data communication hours with a large amount of data transactions, this method is required to collect too many samples that will greatly increase the computational complexity. Therefore, the real-time response time as well as the accuracy of attack detection becomes very low. To address the above problem, we propose a new DDoS detection method called Modified Wavelet Analysis method which is based on the existing Isomap algorithm and wavelet analysis. In the paper, we present our new model and algorithm for detecting DDoS attacks and demonstrate the reasons of why we enlarge the Hurst's value of the self-similarity in our new approach. Finally we present an experimental evaluation to demonstrate that the proposed method is more efficient than the other traditional methods based on wavelet analysis. © 2010 IEEE

Continuous monitoring of residual torque of loose bolt in a bolted joint

6th Asia Pacific Workshop on Structural Health Monitoring, APWSHM, Hobart, Tasmania, Australia, 7-9 December 20162016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordPublishe