Data Visualization Approach Research Articles

Clone-World: A visual analytic system for large scale software clones

With the era of big data approaching, the number of software systems, their dependencies, as well as the complexity of the individual system is becoming larger and more intricate. Understanding these evolving software systems is thus a primary challenge for cost-effective software management and maintenance. In this paper we perform a case study with evolving code clones. The programmers often need to manually analyze the co-evolution of clone fragments to decide about refactoring, tracking, and bug removal. However, manual analysis is time consuming, and nearly infeasible for a large number of clones, e.g., with millions of similarity pairs, where clones are evolving over hundreds of software revisions.We propose an interactive visual analytics system, Clone-World, which leverages big data visualization approach to manage code clones in large software systems. Clone-World, gives an intuitive yet powerful solution to the clone analytic problems. Clone-World combines multiple information-linked zoomable views, where users can explore and analyze clones through interactive exploration in real time. User studies and experts’ reviews suggest that Clone-World may assist developers in many real-life software development and maintenance scenarios. We believe that Clone-World will ease the management and maintenance of clones, and inspire future innovation to adapt visual analytics to manage big software systems.

Development and Assessment of a Mobile Health Application for Monitoring the Linkage Among Treatment Factors of Type 1 Diabetes Mellitus.

Background: In the daily routine of type 1 diabetes mellitus (T1DM), the patients deal with many data and consider many variables to perform actions, decisions, and regimen adjustments. There is a need to apply filtering techniques to extract relevant information and provide appropriate data visualization methods to assist in clinical tasks and decision making. Objective: To present Soins DM, a mobile health tool, for monitoring the linkage among treatment factors of T1DM with an interactive data visualization approach. Methods: First, we performed a literature review, a commercial search, and ideation. Next, we created a prototype and an online survey for its feedback, with participation of 76 individuals. Afterward, the mobile app and its website version were built. Eventually, we conducted a pilot experiment with 4 patients, an online experiment for satisfaction assessment with 97 patients, and an online assessment by 9 health professionals. Results: Prototyping and feedback facilitated the design refinement. Soins DM enables the recording of data from routines of glycemia, insulin applications, meals, and physical exercises. From these logs, the app builds two different ways of interactive data visualization, a timeline and an integrated chart, providing personalized feedback on bad glycemia with its possible causes. The assessments revealed overall satisfaction with the app's characteristics. Conclusions: Soins DM is a novel application with interactive visualization and personalized feedback for easy identification of the linkage among treatment factors of T1DM. The test scenario with patients and health professionals indicates Soins DM as a useful and reliable tool.

NCAR’s Real-Time Convection-Allowing Ensemble Project

AbstractBeginning 7 April 2015, scientists at the U.S. National Center for Atmospheric Research (NCAR) began producing daily, real-time, experimental, 10-member ensemble forecasts with 3-km horizontal grid spacing across the entire conterminous United States. Graphical forecast products were posted in real time to the Internet, where they attracted a large following from both forecasters and researchers across government, academia, and the private sector. Although these forecasts were initially planned to terminate after one year, the project was extended through 30 December 2017 because of the enthusiastic community response. This article details the motivation for the NCAR ensemble project and describes the project’s impacts throughout the meteorological community. Classroom and operational use of the NCAR ensemble are discussed in addition to the diverse application of NCAR ensemble output for research purposes. Furthermore, some performance statistics are provided, and the NCAR ensemble website and data visualization approach are described. We hope the NCAR ensemble’s success will motivate additional experimental forecast demonstrations that transcend current operational capabilities, as forward-looking forecast systems are needed to accelerate operational development and provide students, young scientists, and forecasters with glimpses of what future modeling systems may look like. Additionally, the NCAR ensemble dataset is publicly available and can be used for meaningful research endeavors concerning many meteorological topics.

A Data Mining and Data Visualization Approach to Examine the Interrelationships Between Life Satisfaction, Secularization and Religiosity.

Previous studies have suggested a positive relationship between secularization and happiness, as well as a geographical, cultural and development pattern, primarily based on data gathered in Europe and the USA. To gain a more holistic view on the interrelationships between secularization, religiosity and subjective perception of well-being, this study utilized the Wave 6 archival data set (2010-2014) of World Values Survey, which contains 74,042 observations from 60 countries. In this study, the rating of satisfaction with life was treated as the dependent variable. Four secular value indices and 12 variables related to religiosity were extracted from the data set for predictive analysis. Data mining tools, such as the partition tree and bootstrap forest approaches, suggested that only secular values were influential. Specifically, secular values had a negative relationship with satisfaction. In addition, hierarchical clustering based on secularization and satisfaction did not suggest a meaningful pattern. For example, the dendrogram showed that South Korea, Lebanon, Estonia and Algeria were grouped together. This implies that secularization and satisfaction could vary from country to country, regardless of their geographical location, culture and development status. Specifically, countries that are similar in terms of satisfaction and secularization are not necessarily culturally similar or geographically clustered. This discourages the idea that these factors (e.g., location, development status, culture) play a role in mediating the relationship between secularization and religiosity. By directly contradicting previous work showing a pattern based on Europe and the USA, this finding challenges the existing understanding of the relationship between these factors. By expanding the scope of study to the whole world, the current analysis suggested that the existing view regarding the positive relationship between secularization and well-being might be oversimplified.

Ship speed power performance under relative wind profiles in relation to sensor fault detection

Statistical data analysis and visualization approaches to identify ship speed power performance under relative wind (i.e. apparent wind) profiles are considered in this study. Ship performance and navigation data of a selected vessel are analyzed, where various data anomalies, i.e. sensor related erroneous data conditions, are identified. Those erroneous data conditions are investigated and several approaches to isolate such situations are also presented by considering appropriate data visualization methods. Then, the cleaned data are used to derive various relationships among ship performance and navigation parameters that have been visualized in this study, appropriately. The results show that the wind profiles along ship routes can be used to evaluate vessel performance and navigation conditions by assuming the respective sea states relate to their wind conditions. Hence, the results are useful to derive appropriate mathematical models that represent ship performance and navigation conditions. Such mathematical models can be used for weather routing type applications (i.e. voyage planning), where the respective weather forecast can be used to derive optimal ship routes to improve vessel performance and reduce fuel consumption. This study presents not only an overview of statistical data analysis of ship performance and navigation data but also the respective challenges in data anomalies (i.e. erroneous data intervals and sensor faults) due to onboard sensors and data handling systems. Furthermore, the respective solutions to such challenges in data quality have also been presented by considering data visualization approaches.

An improvement on feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods

Feature extraction by time series modeling based on statistical pattern recognition is a powerful approach to structural health monitoring. Determination of an adequate order and identification of an appropriate model play prominent roles in extracting sensitive features to damage from time series representations. Early damage detection under statistical decision-making via high-dimensional features is another significant issue. The main objectives of this article are to improve a residual-based feature extraction method by time series modeling and propose a multivariate data visualization approach to early damage detection. A simple graphical tool based on Box-Jenkins methodology is presented to identify the most compatible time series model with vibration time-domain measurements. Furthermore, k-Means and Gaussian mixture model clustering techniques are utilized to examine the performance of the residuals of the identified model in damage detection. A numerical concrete beam and an experimental benchmark model are applied to verify the improved and proposed methods along with comparative analyses. Results will show that these approaches are successful in obtaining a sufficient order superior to a state-of-the-art order determination technique, generating uncorrelated residuals, extracting sensitive features to damage, and accurately detecting early damage by high-dimensional data.

Innovative data reduction and visualization strategy for hyperspectral imaging datasets using t-SNE approach

Abstract Visible hyperspectral imaging (HSI) is a fast and non-invasive imaging method that has been adapted by the field of conservation science to study painted surfaces. By collecting reflectance spectra from a 2D surface, the resulting 3D hyperspectral data cube contains millions of recorded spectra. While processing such large amounts of spectra poses an analytical and computational challenge, it also opens new opportunities to apply powerful methods of multivariate analysis for data evaluation. With the intent of expanding current data treatment of hyperspectral datasets, an innovative approach for data reduction and visualization is presented in this article. It uses a statistical embedding method known as t-distributed stochastic neighbor embedding (t-SNE) to provide a non-linear representation of spectral features in a lower 2D space. The efficiency of the proposed method for painted surfaces from cultural heritage is established through the study of laboratory prepared paint mock-ups, and medieval French illuminated manuscript.

Grade distribution digests: A novel tool to enhance teaching and student learning in laboratory practicals.

Assessment is a central component of course curriculums and is used to certify student learning, but it can also be used as a tool to improve teaching and learning. Many laboratory courses are structured such that there is only a grade for a particular laboratory, which limits the insights that can be gained in student learning. We developed a laboratory program that incorporates assessments designed to probe student understanding of different components of the individual modules making up the program. The challenge was to analyze and present grades from these assessment tasks in a format that was readily interpretable by academics. We show that a simplified synthesis of grade distributions (grade distribution digests) provides sufficient information to make decisions about changes in course components. The main feature of the digests is its data visualization approach, where student grades for individual laboratory practicals, individual assessment tasks or individual assessment items are graphically presented as an overall average grade, an average top quartile grade and an average bottom quartile grade, and relative averages across all assessments. This ability to visualize student grades in variety of contexts enables academics with many other demands on their time (e.g. research and administration) to more efficiently identify ways to improve teaching delivery and learning outcomes. Examples are presented of the use of such data to identify and improve deficiencies in both student skills and teaching practice, resulting in improved learning outcomes. © 2017 by The International Union of Biochemistry and Molecular Biology, 46(2):130-140, 2018.

Layer analysis of CO2 sources in the US economic supply chains: an input output LCA study

Purpose Carbon footprint assessment requires a holistic approach, where all possible lifecycle stages of products from raw material extraction to the end of life are considered. The purpose of this paper is to develop an analytical sustainability assessment framework to assess the carbon footprint of US economic supply chains from two perspectives: supply chain layers (tiers) and carbon footprint sources. Design/methodology/approach The methodology consists of two phases. In the first phase, the data were collected from EORA input output and environmental impact assessment database. In the second phase, 48 input-output-based lifecycle assessment models were developed (seven CO2 sources and total CO2 impact, and six supply chain tiers). In the third phase, the results are analyzed by using data visualization, data analytics, and statistical approaches in order to identify the heavy carbon emitter industries and their percentage shares in the supply chains by each layer and the CO2 source. Findings Vast majority of carbon footprint was found to be attributed to the power generation, petroleum refineries, used and secondhand goods, natural gas distribution, scrap, and truck transportation. These industries dominated the entire supply chain structure and found to be the top drivers in all six layers. Practical implications This study decomposes the sources of the total carbon footprint of US economic supply chains into six layers and assesses the percentage contribution of each sector in each layer. Thus, it paves the way for quantifying the carbon footprint of each layer in today’s complex supply chain structure and highlights the importance of handling CO2 source in each layer separately while maintaining a holistic focus on the overall carbon footprint impacts in the big picture. In practice, one size fits all type of policy making may not be as effective as it could be expected. Originality/value This paper provides a two-dimensional viewpoint for tracing/analyzing carbon footprint across a national economy. In the first dimension, the national economic system is divided into six layers. In the second dimension, carbon footprint analysis is performed considering specific CO2 sources, including energy production, solvent, cement and minerals, agricultural burning, natural decay, and waste. Thus, this paper contributes to the state-of-art sustainability assessment by providing a comprehensive overview of CO2 sources in the US economic supply chains.

Rapid fingerprinting of lignin by ambient ionization high resolution mass spectrometry and simplified data mining

Ambient ionization techniques are typically used to analyze samples in their native states with minimal or no sample pretreatment prior to mass spectrometric (MS) analysis. Desorption electrospray ionization (DESI) and direct analysis in real time (DART) were systematically investigated in this work for direct solid analysis of depolymerized lignin samples, with the goal of rapidly fingerprinting these samples, to efficiently characterize the subunits of this renewable energy source. High resolution MS was required for enhanced selectivity in this study due to the inherent structural complexity of lignin. DESI provided results across a broader mass range (up to m/z 700) than DART and also ionized saturated compounds of low oxygen-to-carbon (O/C) ratios and low double bond equivalents (DBE). While DART detected the same core lignin monomeric and dimeric compounds as seen with DESI and electrospray ionization (ESI), results were restricted to a narrower mass range to m/z 500, due to thermal degradation and losses of methoxy groups. In contrast to DESI and ESI, the DART spectra were nearly void of saturated components. On a structural level, the core lignin compounds were visually fingerprinted and ionization method performances critically assessed by employing simplified Kendrick-based data mining approaches. A novel simplified data visualization approach was developed in this work based on modified Kendrick mass defect (KMD) filtering for lignin subunits and plotting the mass defect values against the degree of unsaturation. Direct visualization of monomeric, dimeric and trimeric lignin species was simplified by the KMD separation plots, easily allowing the core lignin compounds to be visually identified and compared. Modified KMD bases, namely methoxy and phenol bases, which represent monomer-specific lignin constituents, were successfully used to classify and group the complex mixture of lignin species. Further separation of methoxy-related lignin species was successfully achieved by employing the more specific phenolic KMD base.

Small data first: pictures from Instagram as an ethnographic source

ABSTRACTResearch projects that employ big data and data visualization approach for analysis of users’ posts on Instagram deal with materials devoid of contextual information about the circumstances of taking photos and posting, but instead bear traces of users’ activities in the form of meta-data such as location, time and date. However, in order to be interpreted, patterns revealed in big data require reference to the characteristics of places and social practices. More traditional studies of vernacular photography and its uses, as well as studies of social networking and mobile technologies within digital anthropology, are able to provide rich layers of meaning to users’ content from Instagram that can also be considered in the perspective of visual anthropology. In order to check what features and parameters can be relevant for interpretation of data from Instagram, a ‘small data’ approach has been used for manual collection, annotation and analysis of a corpus of photographs that have to do with a Ukrainian town.

Material Codes: Ephemeral Traces

New forms of digital imaging such as GPS and satellite mapping have had significant impact on our collective senses of orientation in space and time. Similarly, digital software such as that used for Jacquard weaving allow for new forms of collage, superimposing, combining, repetition, and fragmenting of imagery, which further disrupt a stable perspective. This article links concepts of global data circulation and collection with contemporary data visualization and materialization approaches in the field of digital humanities.Kelly Thompson’s multifaceted project Material Codes: Ephemeral Traces, established in 2014, translates various forms of data through digitally assisted Jacquard weaving. With data visualization, the artist’s subjective process of beautification can make an audience feel emotionally engaged by the rendering of phenomena that may otherwise be beyond the scale of human senses. What is beyond reach becomes tangible. In Thompson’s research-creation work, the intangible concept of digital data is both visualized and materialized into the literally tangible, as tactile cloth. This conversation between Janis Jefferies, (London, UK) and Kelly Thompson (Montreal, Canada) over a period of 10 months proposes questions around code, data, image, weaving, and research engagement.

Identification of Reconstructed Speech

Both voice conversion and hidden Markov model-- (HMM) based speech synthesis can be used to produce artificial voices of a target speaker. They have shown great negative impacts on speaker verification (SV) systems. In order to enhance the security of SV systems, the techniques to detect converted/synthesized speech should be taken into consideration. During voice conversion and HMM-based synthesis, speech reconstruction is applied to transform a set of acoustic parameters to reconstructed speech. Hence, the identification of reconstructed speech can be used to distinguish converted/synthesized speech from human speech. Several related works on such identification have been reported. The equal error rates (EERs) lower than 5% of detecting reconstructed speech have been achieved. However, through the cross-database evaluations on different speech databases, we find that the EERs of several testing cases are higher than 10%. The robustness of detection algorithms to different speech databases needs to be improved. In this article, we propose an algorithm to identify the reconstructed speech. Three different speech databases and two different reconstruction methods are considered in our work, which has not been addressed in the reported works. The high-dimensional data visualization approach is used to analyze the effect of speech reconstruction on Mel-frequency cepstral coefficients (MFCC) of speech signals. The Gaussian mixture model supervectors of MFCC are used as acoustic features. Furthermore, a set of commonly used classification algorithms are applied to identify reconstructed speech. According to the comparison among different classification methods, linear discriminant analysis-ensemble classifiers are chosen in our algorithm. Extensive experimental results show that the EERs lower than 1% can be achieved by the proposed algorithm in most cases, outperforming the reported state-of-the-art identification techniques.

Data Visualization to Explore Improving Decision-Making within Hajj Services

This paper proposes improving Hajj services gaining advantage from the development of the exploratory data visualization technology. We will illustrate the latest view on data visualization, its difference from the normal explanatory figures and how it is needed as a current effective exploration decision making solution from the massive amount of data (big data) gathered from all agencies serving Hajj. Using the data visualization approach to gain and share insight by presenting some real exploratory global experience examples. We apply the technique to visualize some real data from last year Hajj season representing the results of a study on what pilgrims mostly do in their camping in Mina (a 4-days Hajj living area) during the peak Hajj days and finding out what are the prevailing habits among these pilgrims based on their nationalities averaged for last three years. We also give a simplified theoretical example to illustrate the concept linking between the different nationalities of pilgrims and their residence in Makkah beside the grand mosque (Al-Masjid Al-Haram). We show another exploratory data visualization example relating a virtual map of Mina to improve civil defence system services providing the various civil defence stations with its specialized most probable needed equipments and number of employees.

On Generative Topographic Mapping and Graph Theory combined approach for unsupervised non-linear data visualization and fault identification

Abstract Process monitoring of chemical plants relies on two steps: discriminating anomalies (fault detection) and characterizing them (fault identification). This work proposes a combined Generative Topographic Mapping (GTM) and Graph Theory (GT) approach. GTM highlights system features, reducing variable dimensionality and providing a strategy for calculating similarity between samples. GT then clusters them using networks, discriminating normal and anomalous entries. Because of biased normal and anomalous labeling, however, the methodology proposed is unsupervised, meaning that labels are inexistent. Three case studies were considered: a simulation data set, Tennessee Eastman process and an industrial data set. Principal Component Analysis (PCA), dynamic PCA and kernel PCA indexes ( Q and T 2 ) alongside GTM and GT independent monitoring methodologies were used for comparison, considering supervised and unsupervised approaches. For the industrial scenario, soft sensors were used for assessing discrimination performance. The proposed method, while unsupervised, discriminated normal states similarly to supervised strategies, justifying its development.

Study of vibration characteristics for orthotropic circular cylindrical shells using wave propagation approach and multivariate analysis

A study of free vibration of orthotropic circular cylindrical shells is presented. The vibration control equations of shells are based on Flugge classical thin shell theory. Wave approach is used in the analysis, in which the boundary conditions of shells can be simplified according to the associated beam. The free vibration frequencies of shells can be obtained from a frequency polynomial equation of order 6. The parametric analysis of the free vibration of orthotropic cylindrical shells is investigated using a statistical method. The effects of geometrical parameters and material characteristics upon frequencies are investigated here. Multivariate analysis (MVA) can be a useful tool for this parametric study. Some statistical characteristics, including correlation analysis and ANOVA are applied. ANOVA has been conducted to predict the statistical significance of the various factors. Calculations are performed in the Minitab statistical software. The results show that the L/R, h/R and m have larger effects on the lowest frequency. The importance of input parameters is ranked according to their contributions to the total variance. A knowledge and data visualization approach, Self-organizing mapping (SOM) is also adopted here for mining some intrinsic characteristics of shells.

PRM223 - Systematic Review Toolbox

Background Systematic reviews facilitate the rigorous identification and synthesis of evidence in healthcare. However, they can be time consuming, logistically challenging and labour intensive to undertake. Such challenges have led to the development of various software tools to support the systematic review process. It has remained difficult, however, for researchers to easily discover what tools are currently available to support their reviews. In response, a free, online resource of tools to support systematic reviews has been developed. Concept Systematic Review (SR) Toolbox [1] is a community-driven, searchable, web-based catalogue of tools to support the systematic review process. The resource aims to help reviewers find appropriate tools based on the stage(s) of the systematic review process. Users can perform a simple keyword search (i.e. Quick Search) to locate tools, or a more detailed search (i.e. Advanced Search) allowing users to select various criteria to find specific types of tools. Users are also able to submit new tools they have found, used or developed, to the SR Toolbox. The resource was developed using PHP, SQL and JavaScript and uses Twitter [2] to manage its community. Results The SR Toolbox stores information on over 100 software tools (June 2016) to support the systematic review process. These include freely-available tools based on data visualisation, text mining and machine learning approaches. The SR toolbox also catalogues a number of more substantial commercial and not-for-profit software packages that manage substantial portions of the systematic review process. Paper-based tools, such as guidelines, checklists and reporting standards, are also included. Recommendations The SR Toolbox is a useful way to keep up to date with new software tools which have potential to make the SR process more efficient. We recommend researchers consider using the SR Toolbox as a resource for finding tools, and encourage software developers to catalogue any new tools. [1] http://systematicreviewtools.com/ [2] http://twitter.com/SRToolbox

Detecting Abnormalities Behavior in Inventory Management Transactions Using Data Visualization Approaches

Detecting Abnormalities Behavior in Inventory Management Transactions Using Data Visualization Approaches

Walking and health: an enactive affective system

ABSTRACTThis essay presents a transdisciplinary project whose objective is to create a sensorised insole built with latex (Hevea brasiliensis) to measure physiological variations of the human body, as well as to graphically represent affective reciprocal exchanges of the human behaviour with the environment. The purpose is to improve our enactive system through the use of various physiological sensors (e.g. pressure, galvanic skin response and temperature sensors), which can be processed and visualised by revealing the affective exchanges of the body while walking in the city, pinned on a map as data visualisation. Inspired by the enaction theories, the output of sensors and devices application that models the perception as a laboratory phenomenon, as proposed by Dr Ted Krueger, the investigation directed by Dr Domingues is conceived for the expanded sensorium and for the reengineering of life. The artwork is inserted in the domain of aesthetic investigation of art as experiences related to topics on art and technoscience for innovation in health. The created prototype was tested in the city at different locations and the results demonstrated its use as a kind of personal assistant for vital signals by configuring an enactive system added of affective qualities which possibilities to reveal the ‘pathos’, or the affective narratives experienced in the emergent reciprocity with the city. The insole biomaterial, created by Dr Suelia Rodrigues (BioEngeLab), has been successfully used to measure the foot pressure and to serve as a health assistant, especially for people with diabetes. The signal-processing approaches, explored in Biomedical engineering by Dr Cristiano Miosso, were empowered by data visualisation approaches developed by Dr Ricardo Torres (University of Campinas). Finally, we consider this prototype a creative technology for mhealth, which is a disruptive innovation, by providing other forms of existence.

The Personal Health Technology Design Space

Interest is increasing in personal health technologies that utilize mobile platforms for improved health and well-being. However, although a wide variety of these systems exist, each is designed quite differently and materializes many different and more or less explicit design assumptions. To enable designers to make informed and well-articulated design decision, the authors propose a design space for personal health technologies. This space consists of 10 dimensions related to the design of data sampling strategies, visualization and feedback approaches, treatment models, and regulatory constraints.