The current geoscience applications are confronted by two major challenges—integration with numerous diverse sensor devices and use in real-time use case scenarios. Whilst the challenge of service integration is addressed by the concept of Cyber-Physical systems, which aims to incorporate sensor data in application workflows, the usage of High Performance Computers helps minimize the execution time to fulfill the real time scenarios requirements. However, the existing programming models do not allow scientific workflows to take advantage of both technologies simultaneously. This paper contribution offers an approach to encapsulation of workflow-based applications into services, which are flexible enough to run on heterogeneous, distributed infrastructures spanning over both industrial sensor services and parallel computing systems. The approach is demonstrated on a computational fluid dynamics simulation study of aerodynamic processes in complex underground mine ventilation networks.

The amount of computation power in the world keeps increasing as well as the computation needs by the industry and society. That increases also the total energy consumption on ICT, which reached the level of billions of dollars spent every year, as well as an equivalent emission print of millions of tons of CO\(_2\) per year. That economical and ecological costs motivate us to search for more efficient computation. In addition, one more need for an efficient computer is the target of exascale computing and higher levels after that. We consider that it is needed a shift from considering only computation time when optimizing code, to also consider more efficient use of energy. To achieve energy-efficient computing, we consider that the first step considers recording the energy consumption of the algorithms used, and then using those results to select a more efficient energy algorithm among those available, which may require to increase the parallelization level and/or computation time, while still fulfill with the application requirements. Notice that cooling systems in the HPC may require to consume the same amount of energy as that consumed in the computing nodes, which means that the reduction of energy consumption due to efficient energy programming will also be doubled.

Quality assurance for embedded systems is challenging to the heterogeneity, connectivity and constructivity of environments. In this paper, we present the quality assurance design and implementation driven by model-based testing (MBT) for component-based applications in embedded environments. The work is conducted within the EU H2020 project PHANTOM, and the quality assurance in PHANTOM consists of two stages, i.e., early validation and test execution, for both functional and non-functional verification and validation. Early validation stage is carried out in parallel with application development without executing applications to eliminate early design defects, while test execution stages involves functional and non-functional testing, which start in parallel with the application development and end with the execution of test cases against systems under test. All activities are applied to three industry use cases upon hardware agnostic platforms brought by PHANTOM technology, and the results show that combining early validation and test execution enables early and thorough defect detections all along development lifecycle and improve the efficiency and effectiveness of quality assurance.

The latest advances in the High Performance Computing hardware have significantly raised the level of available compute performance. At the same time, the growing hardware capabilities of modern supercomputing architectures have caused an increasing complexity of the parallel application development. Despite numerous efforts to improve and simplify parallel programming, there is still a lot of manual debugging and tuning work required. This process is supported by special software tools, facilitating debugging, performance analysis, and optimization and thus making a major contribution to the development of robust and efficient parallel software. This book introduces a selection of the tools, which were presented and discussed at the 6th International Parallel Tools Workshop, held in Stuttgart, Germany, 25-26 September 2012.

Resource allocation is today an integral part of cloud infrastructures management to efficiently exploit resources. Cloud infrastructures centers generally use custom built heuristics to define the resource allocations. It is an immediate requirement for the management tools of these centers to have a fast yet reasonably accurate simulation and evaluation platform to define the resource allocation for cloud applications. This work proposes a framework allowing users to easily specify mappings for cloud applications described in the AMALTHEA format used in the context of the DreamCloud European project and to assess the quality for these mappings. The two quality metrics provided by the framework are execution time and energy consumption.

High Performance Computing infrastructures are getting increasingly heterogeneous, which offers opportunities to the applications in terms of performance improvement. However, the "hardware-concise" heterogeneous resource allocation requires a deep knowledge of the scheduled applications' characteristics. The challenge is getting more difficult if the scheduler has to balance the optimization policies between the application-and the infrastructure-specific policies, e.g. the overall energy consumption, resource utilization, etc. We introduce a heuristic-based approach to adaptive scheduling, enabled by in-depths monitoring technologies. Our solution is complementary to the native schedulers like Torque or Maui and is open per design and thus can be seamlessly integrated into the core scheduling algorithms.

Modern data analytics applications impose demandin g requirements on storage and processing facilities and are thus e volving to a new challenging class of High Performance Computing (HPC) application s. However, typical Big Data frameworks like Hadoop, Spark, etc. are nor mally not supported onthe fly on traditional HPC infrastructures. The majo r reason for this is that they are mainly based on Java technologies that do not a llow the very complex and expensive HPC hardware to be efficiently utilized; n onetheless, the sustainable performance and efficiency of the infrastructure is a very important usage factor of HPC that cannot be disrespected in the Big Data ap plication setups. In this chapter we analyse the applicability of the well-es tablished HPC technologies and tools like the Message-Passing Interface (MPI) to the implementation of data analytics application and demonstrate advantag es of this approach over the

This paper presents a technology of automated knowledge extraction from unstructured text corpora by leveraging computer linguistic tools and cross-fertilizing them with the semantic ontologies techniques. In our approach, the quality of information (e.g., in form of OWL ontologies) that is derived by semantic analysis techniques from large domain- specific text corpora can be considerably improved by incorporating linguistic analysis tools that help gain a deeper insight into the grammatical structure of the analysed texts and thus allow the reasoning engines to cover a much wider set of rules and patterns, also positively impacting the performance. The novelty of our approach lies in a possibility of its application to the domains that require a very high quality of the knowledge extraction and analysis, such as reasoning for legacy data collections. We propose a system architecture for the implementation of our approach and illustrate its use on a practical use case for legislative and regulatory information analysis. Keywords-Knowledge Representation; Legal Systems; Ontology; OWL; Big Data; Reasoning; DreamCloud Project.

Legal domain is an important source of information and includes diverse law texts, court decisions, etc., which is dominated by documents collected in natural language. There is a great demand for the automatic analysis of the legal information in everyday work of lawyers and other people dealing with laws, for which the ontology-based knowledge representation would be of a great advantage. Unfortunately, the current semantic and ontology based technologies cannot be easily applied for the analysis of legal texts due to a certain complexity of those. We present a strategy that allows different categories of tools, such as those for ontology creation, syntactical analysis of texts collected in natural language, and others to interoperate in order to achieve a common goal – creation of domain-specific ontologies and performing complex reasoning over them. A model that can be applied for the legal system knowledge representation is proposed and its implementation for the German Civil Law System in form of an ontology is discussed. The model allows the creation of a common ontology spanning over the knowledge contained in diverse laws, thus paving the way towards a wide adoption of semantic technologies by the experts in the law domain. Keywords-Knowledge Representation; Law Texts; Ontology; RDF; Big Data; Rule-Set.

Modern computing technologies are increasingly getting data-centric, addressing a variety of challenges in storing, accessing, processing, and streaming massive amounts of structured and unstructured data effectively. An important analytical task in a number of scientific and technological domains is to retrieve information from all these data, aiming to get a deeper insight into the content represented by the data in order to obtain some useful, often not explicitly stated knowledge and facts, related to a particular domain of interest. The major issue is the size, structural complexity, and frequency of the analyzed data’ updates (i.e., the ‘big data’ aspect), which makes the use of traditional analysis techniques, tools, and infrastructures ineffective. We introduce an innovative approach to parallelise data-centric applications based on the Message-Passing Interface. In contrast to other known parallelisation technologies, our approach enables a very high-utilization rate and thus low costs of using productional high-performance computing and Cloud computing infrastructures. The advantages of the technique are demonstrated on a challenging Semantic Web application that is performing web-scale reasoning.

The Large Knowledge Collider (LarKC) is a prominent development platform for the Semantic Web reasoning applications. Guided by the preliminary goal to facilitate the incomplete reasoning, LarKC has evolved in a unique platform, which can be used for the development of robust, flexible, and efficient semantic web applications, also leveraging the modern grid and cloud resources. As a reaction on the numerous requests coming from the tremendously increasing user community of LarKC, we set up a demonstration package for LarKC that is intended to present the main subsystems, development tools and graphical user interfaces of LarKC. The demo aims for both early adopters and experienced users and serves the purpose of promoting Semantic Web Reasoning and LarKC technologies to the potentially new user communities.

We present a scalable, open source realization of MPI-2 standard for Java, seamlessly integrated in Open MPI, as a reaction to novel challenges of supercomputing in the web application domain. A number of Java software solutions developed for the Web, such as coming from Information Retrieval, Semantic Web, and other domains, have begun to face performance and scalability challenges, for which MPI has proved to be an efficient solution in the “traditional” high performance computing languages, such as C and Fortran. We demonstrate that the native Java language design prevents MPI implementations to scale massively on productional supercomputing systems, and present a solution of overcoming the scalability issues by integrating in the native C realization of Open MPI. We also point out the design features of Open MPI that enable the proliferation of MPI into Java applications. Finally, we present some successful pilot scenarios implemented with MPI in Java and discuss future work in terms of promising Java applications of Open MPI, such as Random Indexing of large semantically annotated text sets.

With billions of triples in the Linked Open Data cloud, which continues to grow exponentially, challenging tasks start to emerge related to the exploitation and reasoning of Web data. A considerable amount of work has been done in the area of using Information Retrieval (IR) methods to address these problems. However, although applied models work on the Web scale, they downgrade the semantics contained in an RDF graph by observing each physical resource as a ’bag of words (URIs/literals)’. Distributional statistic methods can address this problem by capturing the structure of the graph more efficiently. However, these methods are computationally expensive. In this paper, we describe the parallelization algorithm of one such method (Random Indexing) based on the Message-Passing Interface technology. Our evaluation results show super linear improvement

The current IT technologies have a strong need for scaling up the high-performance analysis to large-scale datasets. Tremendously increased over the last few years volume and complexity of data gathered in both public (such as on the web) and enterprise (e.g. digitalized internal document base) domains have posed new challenges to providers of high performance computing (HPC) infrastructures, which is recognised in the community as Big Data problem. On contrast to the typical HPC applications, the Big Data ones are not oriented on reaching the peak performance of the infrastructure and thus offer more opportunities for the "capacity" infrastructure model rather than for the "capability" one, making the use of Cloud infrastructures preferable over the HPC. However, considering the more and more vanishing difference between these two infrastructure types, i.e. Cloud and HPC, it makes a lot of sense to investigate the abilities of traditional HPC infrastructure to execute Big Data applications as well, despite their relatively poor efficiency as compared with the traditional, very optimized HPC ones. This paper discusses the main state-of-the-art parallelisation techniques utilised in both Cloud and HPC domains and evaluates them on an exemplary text processing application on a testbed HPC cluster.

A good deal of digital data produced in academia, commerce and industry is made up of a raw, unstructured text, such as Word documents, Excel tables, emails, web pages, etc., which are also often represented in a natural language. An important analytical task in a number of scientific and technological domains is to retrieve information from text data, aiming to get a deeper insight into the content represented by the data in order to obtain some useful, often not explicitly stated knowledge and facts, related to a particular domain of interest. The major challenge is the size, structural complexity, and frequency of the analysed text sets' updates (i.e., the 'big data' aspect), which makes the use of traditional analysis techniques and tools impossible. We introduce an innovative approach to analyse unstructured text data. This allows for improving traditional data mining techniques by adopting algorithms from ontological domain modelling, natural language processing, and machine learning. The technique is inherently designed with parallelism in mind, which allows for high performance on large-scale Cloud computing infrastructures.

Big Data is a modern phenomenon that promises to bring unprecedented economical benefits. Hadoop-like MapReduce implementations has gained a well deserved popularity by providing an open-source data management solution running on commodity PC clusters and with a potential of Big Data scale. Nevertheless, there are many critical problems, for which solutions based on HPCs, FPGA-enabled nodes and providing real-time guaranties may offer a cost-efficient solution for data processing. JUNIPER is a European research projects that is carried out by an international consortium aiming at developing a Big Data analysis platform. In this article, we present an integral part of JUNIPER - a modeling approach, which helps abstracting the data processing stages and wrap the communication between them. This approach is also applied to specify the timing constraints. We illustrate our approach on a real-life application of credit card transaction processing developed by petaFuel.

The modern Semantic Web scenarios require reasoning algorithms to be flexible, modular, and highly-configurable. A solid approach, followed in the design of the most currently existing reasoners, is not sufficient when dealing with today's challenges of data analysis across multiple sources of heterogeneous data or when the data amount grows to the “Big Data” sizes. The “reasoning as a workflow” concept has attracted a lot of attention in the design of new-generation Semantic Web applications, offering a lot of opportunities to improve both flexibility and scalability of the reasoning process. Considering a single workflow component as a service offers a lot of opportunities for a reasoning algorithm to target a much wider range of potentially enabled Semantic Web use cases by taking benefits of a service-oriented and component-based implementation. We introduce a technique for developing service-oriented Semantic Reasoning applications based on the workflow concept. We also present the Large Knowledge Collider - a software platform for developing workflow-based Semantic Web applications, taking advantages of on-demand high performance computing and cloud infrastructures.

The volume of exposed on the Web is increasing at a robust pace. Reasoning is a wide-spread knowledge discovery and information retrieval technique, in particular extensively used for developing Web applications. However, most of the reasoning algorithms are dealing with significant challenges when being scaled up to the problem sizes addressed by the modern Semantic Web, breaking the barrier of billions of RDF statements (triples). Unfortunately, reasoning applications are not optimized to be applied to emerging Internet-scale sets, known as a data problem. In this paper, we introduce a service-oriented approach to facilitate the development of reasoning applications that can scale to big demands. The approach is based on an incomplete reasoning engine LarKC (the Large Knowledge Collider) as well as parallelization techniques elaborated for big applications in the frame of the JUNIPER EU project. We discuss the use of the service-oriented approach to develop two exemplarily resource discovery applications - query expansion and subsetting, based on the random indexing technique.