INTERFACING INTERACTIVE DATA ANALYSIS TOOLS WITH THE GRID: THE PPDG CS-11 ACTIVITY

The traditional data analysis tools support strong computational capabilities and numerous standard visualization techniques. However, they provide little visual interactions due to the fact that the tools maintain a wide applicability to diverse data domains, and thus any inherent meanings associated with the data domains are hardly allowed. To cover these limitations, we propose to augment Mat lab, one of the widely used data analysis tools and computational languages, by imposing the capabilities of handling semantic objects so that diverse essential interaction capabilities could be allowed such as brushing-and-linking, details-on-demand, and dynamic interactive updating on visualization. In our demonstration, we will show our audience how to import semantic data, how visual interactions are occurred, and how these functionalities are convenient using the movie similarity graph data set.

Integrated application of geo-analysis models is critical for geo-process research. Due to the continuity of the real world, the geo-analysis model cannot be applied immediately over the entire space. To date, the method of regrading space as a sequence of computing units (i.e. grid) has been widely used in geographic study. However, the model's variances in division algorithms result in distinct grid data structures. At first, researchers must install and setup the various software to generate the structure-specific grid data required by the models. This method of localized processing is inconvenient and inefficient. Second, in order to integrate the models that use different structural grid data, researchers need to design a specific conversion method based on the integration scenario. Due to difference of researcher’s development habits, it is difficult to reuse the conversion method in another runtime environment. The open and cross-platform character of web services enables users to generate data without the assistance of software programs. It has the potential to revolutionize the present time-consuming process of grid generation and conversion, hence increasing efficiency.Based on the standardized model encapsulation technology proposed by OpenGMS group, this paper presents a grid-service method tailored to the specific requirements of open geographic model integration applications, and the research work is carried out in the following three areas:The basic strategy of grid servitization. The heterogeneity of the grid generation method is a major factor that prevents it from being invoked via a unified way by web services. To reduce the heterogeneous of the grid generation method, this study proposes a standardized description method based on the Model Description Language (MDL). Method for constructing a grid data generating service. A unified representation approach for grid data is proposed in order to standardize the description of heterogeneous grid data; an encapsulation method for grid generating algorithms is proposed; and grid-service is realized by merging the main idea of grid servitization. Method for constructing a grid data conversion service . A box-type grid indexing approach is provided to facilitate the retrieval of grid cells with a large data volume; two conversion types, topologically similar and topologically inaccessible grid data conversion, are summarized, along with the related conversion procedures. On this foundation, a grid conversion engine is built using the grid service-based strategy as a theoretical guide and integrated with the grid conversion strategy. Based on the grid service approach proposed in this paper, researchers can generate and converse grid data without tedious steps for downloading and installing programs. Thus, there are more time spend on geography problem solving, hence increasing efficiency.

The Steinbuch Centre for Computing (SCC) is a new organizational unit of the Karlsruhe Institute of Technology (KIT). Founded in February 2008 as a merger of the previous Institute for Scientific Computing of Forschungszentrum Karlsruhe and the Computing Centre of the Technical University Karlsruhe, SCC provides a broad spectrum of IT services for 8.000 employees and 18.000 students and carries out research and development in key areas of information technology under the same roof. SCC is also known to host the German WLCG [1] Tier-1 centre GridKa.In order to accompany the merging of the two existing computing centres located at a distance of about 10 km and to provide common first class services for science, SCC has selected the IT service management according to the industrial quasi-standard "IT Infrastructure Library (ITIL)" [3] as a strategic element. The paper discusses the implementation of a few ITIL key components from the perspective of a Scientific Computing Centre using examples of Grid services at GridKa.

IDA: An interactive data analysis system for clinical and experimental research

Users of Web and grid services often must temporarily delegate some or all of their rights to a software entity to perform actions on their behalf. The problem with the typical grid services approach (X. 509 proxy certificates) is that commercial Web services tooling fails to recognize these certificates or process them properly. The security assertion markup language (SAML) is a standardized XML-based framework for exchanging authentication, authorization and attribute information. SAML has broadening commercial support but lacks delegation capabilities. To address this shortcoming, we exploit SAML's inherent extensibility to create a delegation framework for Web and grid services that supports both direct and indirect delegation. We develop a set of verification rules for delegation tokens that rely on WS-Security X.509 signatures, but do not force any trust relationship between the delegatee and the target service. We have implemented the framework on two common Web service hosting environments: Java/Tomcat and .NET. By leveraging existing Web services standards, we make it easier for Grid practitioners to build and consume Web and grid services without resorting to grid-specific protocols.

<p>Strong winds associated with extratropical cyclones are one of the most dangerous natural hazards in Europe. These high winds are mostly connected with five mesoscale dynamical features, namely the warm (conveyor belt) jet (WJ), the cold (conveyor belt) jet (CJ), (post) cold-frontal convective gusts (CFC), strong cold sector winds (CS) and – at least in some storms – the sting jet (SJ). While all these have strong winds in common, the timing, location and some further characteristics tend to differ and hence likely also the forecast errors occurring in association with them.</p><p>Here we present a novel objective identification approach for the features listed above, based on a probabilistic random forest using each feature’s most important characteristics in wind, rainfall, pressure and temperature evolution. However, as CJ and SJ turn out to be difficult to distinguish in surface observations alone, we decided to consider the two features together. This identification can then be used to generate a climatology for Central Europe, to analyse forecast errors specific to individual features, and to ultimately improve forecasts of high wind events through feature-dependent statistical post-processing. To achieve this, we strive to identify the features in irregularly spaced surface observations and in gridded analyses and forecasts in a consistent way, thus making it independent of spatial dependencies and gradients.</p><p>To train the probabilistic random forest, we subjectively identify the four storm features in twelve winter storm cases between 2015 and 2020 in both hourly surface observations and high-resolution reanalyses of the German COSMO model over Europe, using an interactive data analysis and visualisation tool. Results show that mean sea-level pressure (tendency), potential temperature, precipitation amount and wind direction are most important for the distinction between the features. From the random forest we get occurrence probabilities for each feature at every station, which can be converted into areal information using Kriging.</p><p>The results show a satisfactory identification for all features, especially for WJ and CFC. We encounter, however, some difficulties to clearly distinguish the CJ and CS, which are dynamically similar. A climatology is currently being compiled for both surface observations and the reanalyses over a period of around 20 years using the trained probabilistic random forests and further for high-resolution COSMO ensemble forecasts, for which we want to analyse forecast errors and develop feature-dependent postprocessing procedures.</p>

Market penetration of residential heat pump water heaters (HPWHs) is accelerating due to advancements in highperformance designs, purchase incentives, and regulatory energy efficiency requirements in the United States. Electric utilities can aggregate and leverage these customer-owned flexible loads to provide grid services, such as demand response. However, replacing electric resistance water heaters (ERWHs) with HPWHs introduces new challenges to grid service dispatch. The objective of this paper is to highlight conformance issues and grid service asset performance deficiencies that arise due to inherent HPWH characteristics. The results of this study show that the reduced energy consumption and slower response times of HPWHs, compared against ERWHs, limit their use in grid services, as do non-conformant responses to protocol messaging. To improve grid service planning, utilities need to account for restricted energy capacities and slower response times, and HPWH manufacturers need to ensure conformant behavior and accurate capacity reporting. The latter could be achieved by developing an ANSI/CTA-2045-B implementation guide for HPWHs. By addressing these concerns, utilities can better plan for flexible load-based grid service programs and manufacturers can provide higher quality products to appliance owners.

Cloud Computing has been widely used by Customers to outsource IT services and business applications managed by a third-party called Cloud Provider in order to reduce their equipment management costs. The academic sector have also taken notice and are investigating how Cloud technology can provide results more quickly and at lower cost in order to deploy Grid services which benefit already from distributed environment like Grid Computing. Indeed, Grid Computing environment supports the large-scale distribution of all resources but faces problem of resource’s increasing demands and resource’s management caused by the growing of scientific computing applications. In this case, taking full advantage of Cloud technologies like enabling the use of Cloud-provided resources and exploiting infrastructure Cloud management system can simplify Grid service’s resource deployment and allocation. However, it is necessary to have an adaptable monitoring solution to operate, analyze and adjust Cloud’s resources to the specifications and requirements of Grid services including ”Middleware” which allows them to run. For this purpose, the work presented in this paper consists on the proposal of an integration ”Grid-Cloud” model to deploy Grid services on Cloud IaaS-resource provided. And secondly, complete this model by developing a Framework based on new monitoring approach with ”Complex Event Processing (CEP)”.

In this paper, we propose a new SQL based incremental distributed algorithm for predicting the next location of a mobile user in a mobile web environments. Parallel and Distributed data mining algorithm is applied on moving logs stored in geographically distributed data grid to generate the mobility pattern, which provides various location based services to the mobile users. One of the existing works for deriving mobility pattern is re-executing the algorithm from scratch results in excessive computation. In our work, we have designed new incremental algorithm by maintaining infrequent mobility patterns, which avoids unnecessary scan of full database. We built data grid system on a cluster of workstation using open source Globus Toolkit (GT) and Message Passing Interface extended with Grid Services (MPICH-G2). The experiments were conducted on original data sets with incremental addition of data and the computation time was recorded for each data sets. We analyzed our results with various sizes of data sets and it shows the time taken to generate mobility pattern by incremental mining algorithm is less than re-computing approach.

The Karlsruhe Institute of Technology (KIT) is the merger of Forschungszentrum Karlsruhe and the Technical University Karlsruhe. The Steinbuch Centre for Computing (SCC) was one of the first new organizational units of KIT, combining the former Institute for Scientific Computing of Forschungszentrum Karlsruhe and the Computing Centre of the University. IT service management according to the worldwide de-facto-standard "IT Infrastructure Library (ITIL)" [1] was chosen by SCC as a strategic element to support the merging of the two existing computing centres located at a distance of about 10 km.The availability and reliability of IT services directly influence the customer satisfaction as well as the reputation of the service provider, and unscheduled loss of availability due to hardware or software failures may even result in severe consequences like data loss. Fault tolerant and error correcting design features are reducing the risk of IT component failures and help to improve the delivered availability. The ITIL process controlling the respective design is called Availability Management [1]. This paper discusses Availability Management regarding grid services delivered to WLCG and provides a few elementary guidelines for availability measurements and calculations of services consisting of arbitrary numbers of components.

Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.

Supporting secure programming in web applications through interactive static analysis

The desire to reduce model-dependency has driven measurement-based modal identification methods, as an alternative to analyzing linearized system models. One such measurement-based method is Dynamic Mode Decomposition (DMD), which has recently appeared in literature. Using this relatively fast DMD algorithm, this paper presents an interactive modal-identification tool for spatio-temporal analysis of measurement data. The tool can automatically scan through measurements, and display the values of oscillation frequency and damping ratio, as well as reconstruct signals. The use of this tool, its options, and visualization capabilities are illustrated using simulated measurements from an interconnected power grid.

Creating many replicas in the processing of data-intensive jobs in data grid is an efficient strategy. Replica replacement is the crucial step to this strategy. Economic model, popularity model and hybrid model etc. have been proposed to solve this issue of replica replacement with analysis and prediction based on each data file, however, these models neglect association relationships among different data files. To find out these association relationships hidden in data-intensive jobs, Apriori algorithm in data mining field is adopted to analyze behaviors of each data-intensive job. An associated replica replacement algorithm based on Apriori approach in data grid is proposed in this paper. This algorithm has two major steps: 1) associated behavior analysis and classification of data files in each node; 2) generation and application of replica replacement rules. Our proposed algorithm is simulated in Optorsim to be compared with LFU algorithm. The experiment shows that there is a relative advantage compared with LFU in mean job times of all jobs, number of remote file access and effective network usage perspectives.

Performance engineering of parallel and distributed applications is a complex task that iterates through various phases, ranging from modeling and prediction, to performance measurement, experiment management, data collection, and bottleneck analysis. There is no evidence so far that all of these phases should/can be integrated into a single monolithic tool. Moreover, the emergence of computational Grids as a common single wide‐area platform for high‐performance computing raises the idea to provide tools as interacting Grid services that share resources, support interoperability among different users and tools, and, most importantly, provide omnipresent services over the Grid. We have developed the ASKALON tool set to support performance‐oriented development of parallel and distributed (Grid) applications. ASKALON comprises four tools, coherently integrated into a service‐oriented architecture. SCALEA is a performance instrumentation, measurement, and analysis tool of parallel and distributed applications. ZENTURIO is a general purpose experiment management tool with advanced support for multi‐experiment performance analysis and parameter studies. AKSUM provides semi‐automatic high‐level performance bottleneck detection through a special‐purpose performance property specification language. The PerformanceProphet enables the user to model and predict the performance of parallel applications at the early stages of development. In this paper we describe the overall architecture of the ASKALON tool set and outline the basic functionality of the four constituent tools. The structure of each tool is based on the composition and sharing of remote Grid services, thus enabling tool interoperability. In addition, a data repository allows the tools to share the common application performance and output data that have been derived by the individual tools. A service repository is used to store common portable Grid service implementations. A general‐purpose Factory service is employed to create service instances on arbitrary remote Grid sites. Discovering and dynamically binding to existing remote services is achieved through registry services. The ASKALON visualization diagrams support both online and post‐mortem visualization of performance and output data. We demonstrate the usefulness and effectiveness of ASKALON by applying the tools to real‐world applications. Copyright © 2005 John Wiley & Sons, Ltd.