Data Visualization Components Research Articles

FIMED: Flexible management of biomedical data

Background and objectives: In the last decade, clinical trial management systems have become an essential support tool for data management and analysis in clinical research. However, these clinical tools have design limitations, since they are currently not able to cover the needs of adaptation to the continuous changes in the practice of the trials due to the heterogeneous and dynamic nature of the clinical research data. These systems are usually proprietary solutions provided by vendors for specific tasks. In this work, we propose FIMED, a software solution for the flexible management of clinical data from multiple trials, moving towards personalized medicine, which can contribute positively by improving clinical researchers quality and ease in clinical trials.Methods: This tool allows a dynamic and incremental design of patients’ profiles in the context of clinical trials, providing a flexible user interface that hides the complexity of using databases. Clinical researchers will be able to define personalized data schemas according to their needs and clinical study specifications. Thus, FIMED allows the incorporation of separate clinical data analysis from multiple trials.Results: The efficiency of the software has been demonstrated by a real-world use case for a clinical assay in Melanoma disease, which has been indeed anonymized to provide a user demonstration. FIMED currently provides three data analysis and visualization components, guaranteeing a clinical exploration for gene expression data: heatmap visualization, clusterheatmap visualization, as well as gene regulatory network inference and visualization. An instance of this tool is freely available on the web at https://khaos.uma.es/fimed. It can be accessed with a demo user account, “researcher”, using the password “demo”.Conclusion: This paper shows FIMED as a flexible and user-friendly way of managing multidimensional clinical research data. Hence, without loss of generality, FIMED is flexible enough to be used in the context of any other disease where clinical data and assays are involved.

FESRCT

We present a framework for the event semantic retrieval of cultural tourism. Nowadays, information and communication technologies are ubiquitous and pervasive and have greatly promoted the development of cultural tourism. Cultural tourism should utilize these technologies to improve a sense of participation and experience for cultural tourists by sorting out domain-specific cultural knowledge from tourism attractions systematically, building bridges between tourism resources and cultural connotation naturally and presenting the cultural changes behind tourism resources vividly. To the end, we present a complete framework that is suitable to event retrieval of cultural tourism, helping cultural tourists learn culture in all directions and in depth quickly and easily before their cultural tours, and local government create tourism cards through the dissemination of culture connotation as well. Our main inspiration is that story-telling would be an effective form of acceptance by cultural tourists to spread the culture behind tourism resources. Concretely, our framework includes data acquisition and preprocessing, data organization and processing, and data visualization components. The data acquisition and preprocessing component is responsible for collecting historical event texts and fusing text knowledge. The data organization and processing component enables an intuitive view on properties and relations of the event elements in terms of defined event ontology for cultural tourism and supports event semantic retrieval. The data visualization component provides a knowledge navigation through dynamic display and an interactive interface with modification function. We have conducted event semantic retrieval of Minnan culture and verified the feasibility and effectiveness of the framework.

RDP5: a computer program for analyzing recombination in, and removing signals of recombination from, nucleotide sequence datasets.

For the past 20 years, the recombination detection program (RDP) project has focused on the development of a fast, flexible, and easy to use Windows-based recombination analysis tool. Whereas previous versions of this tool have relied on considerable user-mediated verification of detected recombination events, the latest iteration, RDP5, is automated enough that it can be integrated within analysis pipelines and run without any user input. The main innovation enabling this degree of automation is the implementation of statistical tests to identify recombination signals that could be attributable to evolutionary processes other than recombination. The additional analysis time required for these tests has been offset by algorithmic improvements throughout the program such that, relative to RDP4, RDP5 will still run up to five times faster and be capable of analyzing alignments containing twice as many sequences (up to 5000) that are five times longer (up to 50 million sites). For users wanting to remove signals of recombination from their datasets before using them for downstream phylogenetics-based molecular evolution analyses, RDP5 can disassemble detected recombinant sequences into their constituent parts and output a variety of different recombination-free datasets in an array of different alignment formats. For users that are interested in exploring the recombination history of their datasets, all the manual verification, data management and data visualization components of RDP5 have been extensively updated to minimize the amount of time needed by users to individually verify and refine the program’s interpretation of each of the individual recombination events that it detects.

Status of the neutron time-of-flight single-crystal diffraction data-processing software STARGazer.

The STARGazer data-processing software is used for neutron time-of-flight (TOF) single-crystal diffraction data collected using the IBARAKI Biological Crystal Diffractometer (iBIX) at the Japan Proton Accelerator Research Complex (J-PARC). This software creates hkl intensity data from three-dimensional (x, y, TOF) diffraction data. STARGazer is composed of a data-processing component and a data-visualization component. The former is used to calculate the hkl intensity data. The latter displays the three-dimensional diffraction data with searched or predicted peak positions and is used to determine and confirm integration regions. STARGazer has been developed to make it easier to use and to obtain more accurate intensity data. For example, a profile-fitting method for peak integration was developed and the data statistics were improved. STARGazer and its manual, containing installation and data-processing components, have been prepared and provided to iBIX users. This article describes the status of the STARGazer data-processing software and its data-processing algorithms.

Architecture of the Parallel Big Data Processing System for Security Monitoring of Internet of Things Networks

Internet-of-Things networks are applied in many areas of people life now. A cornerstone in a issue of a possibility of further distribution and use of these networks is the aspect of security support. However, the features of these networks complicate the use of traditional means and systems of computer protection in them. One of such features is the need to analyze very large volumes of data, heterogeneous by the nature, in real time and with the minimum computing expenses. Taking into account the features of computational capabilities of Internet-of-Things networks the architecture of the system for parallel big data processing based on the data processing technology named as Complex Event Processing and the parallel computing platform Hadoop is offered. The issues directly connected to the architecture of the system and with implementation of its principal components are considered. These components are: data collection component, data storage component, data normalization and analysis component, and data visualization component. An interconnection between components is provided by means of the Hadoop Distributed File System that is a basis for creation of the distributed data storage. The data collection component organizes the distributed data acquisition and their storage in the data storage component. The data normalization and analysis component transforms data to a uniform format and processes them by means of correlation rules. The data visualization component presents data in a graphical form more suitable for further perception by the operator. The results of the experimental evaluation of the system performance confirming a conclusion about its high performance are discussed.

Demo: Shield UI JavaScript Chart - A Flexible HTML5 Data Visualization Component

Shield UI’s advanced framework for creating rich charts and graphs is the first of a line of data visualization components, giving web developers the power for embedding rich graphics in their web projects with minimum effort. Built with HTML, CSS3 and packaged as a jQuery plugin, the library has full support for legacy and modern desktop web browsers, as well as the latest mobile devices.

New data visualization of the LHC Era Monitoring (Lemon) system

In the last few years, new requirements have been received for visualization of monitoring data: advanced graphics, flexibility in configuration and decoupling of the presentation layer from the monitoring repository. Lemonweb is the data visualization component of the LHC Era Monitoring (Lemon) system. Lemonweb consists of two subcomponents: a data collector and a web visualization interface. The data collector is a daemon, implemented in Python, responsible for data gathering from the central monitoring repository and storing into time series data structures. Data is stored on disk in Round Robin Database (RRD) files: one file per monitored entity, with set of entity related metrics. Entities may be grouped into a hierarchical structure, called “clusters” and supporting mathematical operations over entities and clusters (e.g. cluster A + cluster B /clusters C – entity XY). Using the configuration information, a cluster definition is evaluated in the collector engine and, at runtime, a sequence of data selects is built, to optimize access to the central monitoring repository. In this article, an overview of the design and architecture as well as highlights of some implemented features will be presented.

DECHO—a framework for the digital exploration of cultural heritage objects

We present a framework for the digital exploration of cultural heritage objects. Today computing and information technology is pervasive and ubiquitous and has boosted at unprecedented levels, information diffusion and productivity. Such technology is today a ripe context for succinctly gathering knowledge by combining in innovative ways powerful visualization tactics, rapid access to a significant amount of relevant information, domain-specific knowledge, and rich and pervasive tools to sort, group, and slice the information and knowledge in different ways. To this end, we present a complete framework that is easy to use, does not require expensive custom equipment, and has been designed for helping archaeology researchers and educators reconstruct and analyze the historical context of cultural heritage objects. Our main inspiration is that archaeology today would benefit significantly from having spur-of-the-moment access to information from a variety of heterogeneous data sources and being able to have multiple participants visually observe factual and visual data in an intuitive and natural setting. While we present a framework geared towards archaeology, in the long term we envision reusing it in a variety of fields. Our framework includes data acquisition, data management, and data visualization components. The data acquisition component enables the fast, easy, and accurate addition of 3D object models and factual data, including narrations. The data management component includes a novel semantic database system that provides an intuitive view of the available contents in terms of an ontology, supports the addition of narrations, integrates data stored by other databases, and supports object retrieval, browsing, and knowledge navigation. The data visualization component provides visual feedback, which is a crucial part of an exploratory endeavor. It provides the ability to alter the appearance of archaeological objects, complete fragments of 3D object models, and several compelling forms of digital inspection and information visualization. All algorithms exploit knowledge from the database and from the obtained 3D models. Visuals can be applied on top of the physical object or on a 3D model shown in a traditional display, controllable via a Web page interface.

Object-oriented data analysis framework for neutron scattering experiments

Materials and Life Science Facility (MLF) of Japan Proton Accelerator Research Complex (J-PARC) is one of the facilities that provided the highest intensity pulsed neutron and muon beams. The MLF computing environment design group organizes the computing environments of MLF and instruments. It is important that the computing environment is provided by the facility side, because meta-data formats, the analysis functions and also data analysis strategy should be shared among many instruments in MLF. The C++ class library, named Manyo-lib, is a framework software for developing data reduction and analysis softwares. The framework is composed of the class library for data reduction and analysis operators, network distributed data processing modules and data containers. The class library is wrapped by the Python interface created by SWIG. All classes of the framework can be called from Python language, and Manyo-lib will be cooperated with the data acquisition and data-visualization components through the MLF-platform, a user interface unified in MLF, which is working on Python language. Raw data in the event-data format obtained by data acquisition systems will be converted into histogram format data on Manyo-lib in high performance, and data reductions and analysis are performed with user-application software developed based on Manyo-lib. We enforce standardization of data containers with Manyo-lib, and many additional fundamental data containers in Manyo-lib have been designed and developed. Experimental and analysis data in the data containers can be converted into NeXus file. Manyo-lib is the standard framework for developing analysis software in MLF, and prototypes of data-analysis softwares for each instrument are being developed by the instrument teams.

BioWidgets: data interaction components for genomics.

The presentation of genomics data in a perspicuous visual format is critical for its rapid interpretation and validation. Relatively few public database developers have the resources to implement sophisticated front-end user interfaces themselves. Accordingly, these developers would benefit from a reusable toolkit of user interface and data visualization components. We have designed the bioWidget toolkit as a set of JavaBean components. It includes a wide array of user interface components and defines an architecture for assembling applications. The toolkit is founded on established software engineering design patterns and principles, including componentry, Model-View-Controller, factored models and schema neutrality. As a proof of concept, we have used the bioWidget toolkit to create three extendible applications: AnnotView, BlastView and AlignView.