Svetlana Chuprina

Adaptive Scientific Visualization System for Desktop Computers and Mobile Devices

Abstract In this paper, the development of a client-server scientific visualization system named SciVi is described. This system is designed to render the results of scientific experiments in a human-observable form. The means for flexible integration of visualization system with software solving scientific problems (solver) is proposed. The multi-platform portability is achieved that allows SciVi to be able to work on desktop computers as well as on different types of mobile devices with no source code modification. It is showing how to improve the user abilities of interaction with the graphical scene and to reach an optimal load of computer system at the same time. The main feature of our approach is that the system developed is not only adaptable but also adaptive to the performance of servers and clients and to connection speed. The Dublin Core metadata standard is used to describe different kinds of recourses including server and client settings, 3D-models and shaders.

Development of ontology-based multiplatform adaptive scientific visualization system

Abstract In this paper, we propose methods and tools for multi-platform adaptive visualization systems’ development that meets the specific visualization requirements of the computational experiments in the different fields of science. The proposed approach was implemented within the client-server rendering system SciVi (Scientific Visualizer) presented in this paper. This system provides multi-platform portability and automated integration with different solvers based on ontology engineering methods. SciVi is used in Perm State University to help scientists and researchers acquire the necessary multidisciplinary skills and to solve real scientific problems by means of adaptive visualization tools.

Steps towards Bridging the HPC and Computational Science Talent Gap Based on Ontology Engineering Methods

Abstract The paper describes an ontology-based methods and framework for design of learning courses covering the HPC and Big Data areas and how to include these into Computational Science training within the remit of existing courses of Master Programme entitled “Applied Mathematics and Computer Science” (Faculty of Mechanics and Mathematics, Perm State University, Russia). It helped bringing together the university and IT-companies around a real industry projects in the field of Big Data with active participation of masters students. In this paper, the visual tools and ontology-based methods for computer-supported collaborative learning environment will be also presented.

Ontology Based Data Access Methods to Teach Students to Transform Traditional Information Systems and Simplify Decision Making Process

We describe a service-based approach that provides a natural language interface to legacy information systems, built on top of relational database management systems. The long term goal is to make data management and analysis accessible to a wider range of users for a diverse range of purposes and to simplify the decision making process. We present an ontology-driven web-service, named Reply, that transforms traditional information systems into intelligent systems, endowed with a natural language interface, so that they can be queried by any novice user much like modern day search engines. The principal mechanism of our approach is turning a natural language query into a SQL-query for structured data sources by using Ontology-Based Data Access methods. We also outline how the proposed approach allows semantic searching of large structured, unstructured, or semi-structured data within the database or outside sources, thus helping bridge the talent gap in the case of Big Data Analytics used by researchers and postgraduate students.

Using Scientific Visualization Tools to Bridge the Talent Gap

Abstract In this paper the use of adaptive scientific visualization tools in education, including in the area of high performance computing education is proposed in order to help students understand in depth the nature of particular scientific problems and to help them to learn parallel computing approaches to solving these problems. The proposed approach may help to bridge the talent gap in natural and computational sciences, since high quality visualization can help to uncover hidden regularities in the data with which the researchers and students work and can lead to new level of understanding how the data can be partitioned and processed in parallel. A multiplatform client-server scientific visualization system is presented that can be easily integrated with third-party solvers from any field of science. This system can be used as a visual aid and a collaboration tool in high performance computing education.

Using Ontology Engineering Methods to Improve Computer Science and Data Science Skills.

Abstract This paper focuses on issues of ontology construction process, Computing Classification System and Data Science domain ontology all used to help not only IT-students but any IT-specialists from industry and academia also to tackle the problems addressing the Big Data and Data Science skills gap. We discuss some methodological aspects of ontology design process and enriching of existing free accessible ontologies and show how suggested methods and software tools help IT-specialists including master students to implement their research work and participate in real world projects. The role of visual data exploration tools for certain issues under discussion and some use cases are discussed.

Development of Multiplatform Adaptive Rendering Tools to Visualize Scientific Experiments

Abstract In this paper, we propose methods and tools for multiplatform adaptive visualization system development adequate to the specific visualization goals of the experiments in the different fields of science. Approach proposed was implemented and we present a client-server rendering system SciVi (Scientific Visualizer) which provides multiplatform portability and automated integration with different solvers based on ontology engineering methods. SciVi is developed in Perm State University to help scientists and researchers acquire the necessary multidisciplinary skills and to solve real scientific problems.

Calibration and Monitoring of IoT Devices by Means of Embedded Scientific Visualization Tools

In the paper we propose ontology based scientific visualization tools to calibrate and monitor various IoT devices in a uniform way. We suggest using ontologies to describe associated controllers, chips, sensors and related data filters, visual objects and graphical scenes to provide self-service solutions for IoT developers and device makers. High-level interface of these solutions enables composing data flow diagrams defining both the behavior of the IoT devices and rendering features. According to the data flow diagrams and the set of ontologies the firmware for IoT devices is automatically generated incorporating both the data visualization and device behavior code. After the firmware loading, it’s possible to connect to these devices using desktop computer or smartphone/tablet, get the visualization client code over HTTP, monitor the data and calibrate the devices taking into account monitoring results. To monitor the distributed IoT networks a new visualization model based on circle graph is presented. We demonstrate the implementation of suggested approach within ontology based scientific visualization system SciVi. It was tested in a real world project of an interactive Permian Antiquities Museum exhibition creating.

High-Level Toolset For Comprehensive Visual Data Analysis and Model Validation

Abstract The paper is devoted to the new method of high-level scientific visualization, comprehensive visual analysis and model validation tools development using new version of client-server scientific visualization system SciVi as an example. The distinctive features of the methods implemented are ontology-based automated adaptation to third-party data sources from various application domains and to specifics of the visualization problems as well as multiplatform portability of the software solution. High-level tools for semantic filtering of the rendered data are presented. These tools improve visual analytics capabilities of SciVi enabling to validate solvers’ or/and data sources’ models in more comprehensive form and to reduce uncertainties due to the explicit representation of hidden features of data.

A Way How to Impart Data Science Skills to Computer Science Students Exemplified by OBDA-Systems Development

Abstract Nowadays to explore and examine data from a variety of angles to tackle Big Data problems, to devise data-driven solutions to the most pressing challenges, it is necessary to build multidisciplinary students’ skills set for innovative methods not only for master’s in Data Science programs but in traditional Computer Science programs also. In the paper, we describe how teaching methods and tools, which are used to train students to develop Ontology-Based Data Access systems with natural language interface to relational databases helps Master’s Degree students in Computer Science to collaborate with Data Scientists in real-world interdisciplinary projects and to prepare them for a data science career. We use ontology engineering in a combination with Natural Language Processing methods based on lexico-syntactic patterns, in particular, to extract needful data from structured, semi-structured and unstructured datasets in a uniform way to analyze real-world Russian social networks related to new building area.