VisIVO Workflow-Oriented Science Gateway for Astrophysical Visualization

SummaryState‐of‐the‐art science gateways can be connected to several distributed computing infrastructures (DCIs) and are able to run jobs and workflows simultaneously in all those DCIs. Flexibility of accessing diverse data storages from these workflows and assisting end users to manage these storages are however the missing features in current gateway implementations, in which these problems often prove to be a barrier of exploiting the power of distributed computing by user communities having no or little IT competence. This paper addresses these issues by integrating a data bridging service called Data Avenue into WS‐PGRADE/gUSE portal framework. Data Avenue offers tools for the end users to easily manage their data residing on various storage resources, and also, jobs become capable of accessing different storages regardless of the particular distributed computing infrastructure where the job is currently being run. Copyright © 2014 John Wiley & Sons, Ltd.

Virtual high-throughput screening (vHTS) is an invaluable method in modern drug discovery. It permits screening large datasets or databases of chemical structures for those structures binding possibly to a drug target. Virtual screening is typically performed by docking code, which often runs sequentially. Processing of huge vHTS datasets can be parallelized by chunking the data because individual docking runs are independent of each other. The goal of this work is to find an optimal splitting maximizing the speedup while considering overhead and available cores on Distributed Computing Infrastructures (DCIs). We have conducted thorough performance studies accounting not only for the runtime of the docking itself, but also for structure preparation. Performance studies were conducted via the workflow-enabled science gateway MoSGrid (Molecular Simulation Grid). As input we used benchmark datasets for protein kinases. Our performance studies show that docking workflows can be made to scale almost linearly up to 500 concurrent processes distributed even over large DCIs, thus accelerating vHTS campaigns significantly.

In the past decade, a lot of research work has been conducted to support collaborative visualization among remote users over the networks, allowing them to visualize and manipulate shared data for problem solving. There are many applications of collaborative visualization, such as oceanography, meteorology and medical science. To facilitate user interaction, a critical system requirement for collaborative visualization is to ensure that remote users will perceive a synchronized view of the shared data. Failing this requirement, the user's ability in performing the desirable collaborative tasks will be affected. In this paper, we propose a synchronization method to support collaborative visualization. It considers how interaction with dynamic objects is perceived by application participants under the existence of network latency, and remedies the motion trajectory of the dynamic objects. It also handles the false positive and false negative collision detection problems. The new method is particularly well designed for handling content changes due to unpredictable user interventions or object collisions. We demonstrate the effectiveness of our method through a number of experiments.

The rapidly expanding application of visualization technology has the potential to fundamentally change the way companies conduct business in the oil and gas industry. This technology is already having a significant impact on efficiency, accuracy, completeness and integration in exploration and development. The last three years have seen the growing acceptance of large collaborative and semiimmersive visualization systems in the upstream portion of the oil and gas industry. As this technology matures, and as it is applied broadly to both the upstream and downstream business, it will produce tremendous value through the reduction of cycle times and risk. Large display systems, including large flat screens, curved screen Visionaria and fully immersive CAVEs, hold the potential to revolutionize the way business is conducted. Experience using these large systems suggests that the cost of the visualization system is recovered after only a few months of operation. Once useful applications are available, the utilization of the system rapidly expands to fill all of the available time. Without head tracking (tracking the viewer's position and updating the display appropriately), these large systems facilitate the integration of data, and the integration and collaboration between members of interdisciplinary teams. Systems that incorporate head tracking hold the potential to produce significantly greater value by allowing display and interaction to occur in a truly three-dimensional fashion. An example of this type of system and application is an immersive drilling planner. It integrates all of the geophysical and geological information in the 3-D tracked visual display, and incorporates the algorithms and input data necessary to generate engineered drillable well paths. Introduction Visualization technology has been used for years in oil and gas exploration and development. Visualization is simply the graphical presentation of data in an intuitive fashion to reveal information. A bar chart, line graph, seismic section or reservoir map are all visualizations of data. Over the last five to ten years, the technology of visualization has advanced more and more rapidly, with improving resolution, interactivity and display size. Visualization on smaller desktop display systems has tended to focus on discipline or "domain" specific applications (e.g. 3-D visualization applications used for 3-D seismic interpretation). These applications have demonstrated value through improving the accuracy, completeness and, to some extent, the efficiency of interpretation and reservoir modeling. However, These domain specific applications provide only limited integration between disciplines and little opportunity for collaboration between domain experts in front of the small screen. These limitations arise from the combination of the domain specific nature of the software itself (seismic interpretation software, log analysis software, geologic modeling software, etc.), and the limitations of the desktop display as a viewing medium. A desktop display is typically limited to 1280×1024 pixels (picture elements) on a 21-inch monitor. Although resolutions can be higher on more expensive systems, and displays can be a few inches larger, desktop displays are fundamentally limited in the amount of data that can be displayed, and in the number of people who can view the data simultaneously.

Solid-state NMR (ssNMR) can provide structural information at the most detailed level and, at the same time, is applicable in highly heterogeneous and complex molecular environments. In the last few years, ssNMR has made significant progress in uncovering structure and dynamics of proteins in their native cellular environments [1-4]. Additionally, ssNMR has proven to be useful in studying large biomolecular complexes as well as membrane proteins at the atomic level [5]. In such studies, innovative labeling schemes have become a powerful approach to tackle spectral crowding. In fact, selecting the appropriate isotope-labeling schemes and a careful choice of the ssNMR experiments to be conducted are critical for applications of ssNMR in complex biomolecular systems. Previously, we have introduced a software tool called FANDAS (Fast Analysis of multidimensional NMR DAta Sets) that supports such investigations from the early stages of sample preparation to the final data analysis [6]. Here, we present a new version of FANDAS, called FANDAS 2.0, with improved user interface and extended labeling scheme options allowing the user to rapidly predict and analyze ssNMR data sets for a given protein-based application. It provides flexible options for advanced users to customize the program for tailored applications. In addition, the list of ssNMR experiments that can be predicted now includes proton (1H) detected pulse sequences. FANDAS 2.0, written in Python, is freely available through a user-friendly web interface at http://milou.science.uu.nl/services/FANDAS .

Dynamic Monitoring is one of the most basic and the most important parts in geographical conditions monitoring, which provides basic data for geographical conditions monitoring and assists local governments with land survey and database updating quickly, accurately and completely. Based on the uniform principle, it can extract various types of figure spot for land using, by the HCI-based methods with the prototype system, through the registration and contrastive analysis of the remote sensing images, the land survey database of last annual, temporary polygons, etc. Binding the characteristics of Big Data environments, the complexity, isomerism and distributives of spatial information system as well as the diversity and personalization of user needs, determine Dynamic Monitoring should have the characteristics of universality and synergy. It also can improve the collaborative visualization efficiency of data service, by establishing a distributed collaborative system and a universal computing environment of spatial information.

A "Collaboratory" is a lightweight application component allowing project teams to collaborate and share data and insight while distributed over a network, using intuitive visual navigation techniques. The innovation can be summarised as the integration of network technology used in collaborative games with 3D data navigation techniques and based on a low-level, fine-grained, customisable "atomic" visualization component infrastructure. This paper is also a blueprint to inform researchers and software engineers about the possibility to extend existing visualisation applications to support collaboration, e.g. making it easier to "collaboratize" visualization applications based on Microsoft's DirectX class library.

A divide-and-conquer local search heuristic for data visualization

Video analysis has attracted the attention of many researchers because of the growing need for multimedia information retrieval for computer vision applications. Content based retrieval and object-based video retrieval are challenging because of the poor feature representation of the objects and the low number of multiclass video datasets available. Even though there have been significant advances in generic object detection & information retrieval tasks, performance of object based indexing & retrieval falls due to the low number of representative video dataset and lack of information on the frequency of objects. In order to facilitate the progress of multimedia information retrieval in the field of computer vision related application research, we present the multiclass object detection datasets. This dataset consists of 732 videos from YouTube with 5 object classes and each video is about 2 minutes long and total size of the dataset being approximately 8.5 GB. In this article, we will provide the baseline evaluation results of two of the latest object detection algorithms used to evaluate the newly created multidimensional object detection dataset. The paper presents the performance metrics of object detection methods at various time frames. Accuracy remains consistently high, ranging from 93.98% at the 5th frame to 84.17% at the 30th frame, suggesting a generally strong ability to make correct predictions. The dataset is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://drive.google.com/file/d/1BHVsB38vbu9LUY03XlFWY2o_gxEiIKY7/view?usp=sharing</uri>.

This paper details the architecture of a test-bed under development for secure sharing, capture, distributed processing, and archiving of surveillance data called the Networked Sensor Tapestry (NeST). The test-bed consists of core software modules including a centralized server, client interface library, a layered XML messaging scheme. Mobile hardware clients are interfaced to the NeST using a Tiny-OS based microcontroller with sensor data collected over a 1-wire data bus. Maintaining subject privacy in video and other sensor monitoring scenarios can be imperative for the successful deployment of surveillance networks. Subject privacy is integrated into the architecture and can (if desired) operate as a buffer to the server core, denying access to identity specific information to any or all modules or operators. We introduce 3 fundamental privacy concepts: The <i>privacy buffer</i>: is a core component of the NeST server and utilizes programmable plug-in <i>privacy filters</i> operating on incoming sensor data to prevent access to or transform data to remove personally identifiable information. These privacy filters are developed and specified using a <i>privacy grammar</i> that can connect multiple low-level data filters and features to create arbitrary data-dependent privacy definitions. The utility of the architecture is demonstrated with a connection to a variety of hardware/software clients including PDA based client hardware, remote sensor interface devices, software modules for sensor data inferencing, data visualization, sensor control and data archival applications.

Subjects dealing with graph theory and combinatorial optimization belong among the basic subjects of informatics study. Their aim is above all to develop and deepen students’ capacity for logical and algorithmic thinking.Mathematical proofs that are inseparable part of these subjects do not belong to popular topics among students due to the difficulty to understand this field of mathematics. Within educational process we are searching for various tools to help students deal with this area of mathematics. Suitable visualization could become a very convenient instrument used to enhance understanding of theorems and their proofs. Using visualization and deep analysis of the topic together with discussion on mutual relationships between solutions to problems allows the teacher to enhance student’s logical thinking and support their understanding to more complex proofs. In the paper we provide a justification for the importance of the mathematical proofs for students of informatics at first. Secondly, we introduce a research study examining an impact of visual applications on understanding of mathematical proofs; we implemented presentations instructed within the Discreet mathematics course, which are used to visualize proofs of mathematical theorems in the area of the graph theory, and we analysed students’ approaches to proofs visualization and proofing in general.

The main goal of graphical modelling is to communicate information clearly and effectively through graphical means. Little research has been undertaken in the domain of construction scheduling. It can be noted that there is no standard graphics protocol; therefore, it is up to each individual planner to set his or her own standard. This paper develops a new chronographical conceptual framework that describes all the elements required to perform construction operations, their processes, their logical constraints and their association and organisational models. The protocol studies the suitable visual parameters and their associated values in order to define a standard graphical presentation using shapes, sketches, codes, text, textures and colours. This protocol aims to overcome the current difficulties with graphical visualisation of the considerable amount of data needed for effective planning and to increase the effectiveness of visual research based on human visual habits. The validation process was performed using case studies that evaluated visual data and assessed the necessary mental effort required to find information on the schedule. The graphical convention of textures and colours has already been validated. The results have clearly demonstrated that this convention helps to simplify the process of searching for information on the schedule.

High quality ground truth data sets are crucial for the development of image recognition systems. However, the task of annotating large image data sets manually takes a lot of time and effort. In order to lower the burden for the development of application-specific image recognition systems, we developed an advanced user interface. This interface is especially designed for non-expert users with little-to-no knowledge of computer vision techniques. The interface presents images clustered by similarity and allows for an efficient and simple annotation of large data sets. The integration of overview+detail concepts allows the precise navigation inside large data sets. The interface can be used without prior instructions on the underlying concepts, like self-organizing maps, image features or visualization techniques.