Creating curricula for educational institutions that meet the demands of a fast-moving labour market is a complex process that can take up to several years. Especially in a field like information technology (IT), new technologies require ongoing adaptation of the corresponding curricula. A particular challenge is to put theoretical concepts, such as those taught by universities, in curricula in such a way that they correspond to the technologies that are currently required on the labour market. While this is a general problem, we elaborated it in the context of cloud computing by addressing the following questions: Is it adequately dealt with in the IT curricula of Austrian universities according to the requirements of the IT labour market? And further, how can curriculum alignments be (semi-)automated to help to better meet current IT job market needs? To answer them, the texts of job descriptions and IT study plans of Austrian universities are first analysed and later compared with similarity metrics. After a quantitative analysis, genetic algorithms are applied to improve the coverage of the curricula.

Rapid technological progress in computer sciences finds solutions and at the same time creates ever more complex requirements. Due to an evolving complexity todays programming languages provide powerful frameworks which offer standard solutions for recurring tasks to assist the programmer and to avoid the re-invention of the wheel with so-called out-of-the-box-features. In this paper, we propose a way of comparing different programming paradigms on a theoretical, technical and practical level. Furthermore, the paper presents the results of an initial comparison of two representative programming approaches, both in the closed SAP environment.

The popularity of cloud based Infrastructure-as-a- Service (IaaS) solutions is becoming increasingly popular. However, since IaaS providers and customers interact in a flexible and scalable environment, security remains a serious concern. To handle such security issues, defining a set of security parameters in the service level agreements (SLA) between both, IaaS provider and customer, is of utmost importance. In this paper, the European Network and Information Security Agency (ENISA) guidelines are evaluated to extract a set of security parameters for IaaS. Furthermore, the level of applicability and implementation of this set is used to assess popular industrial and open-source IaaS cloud platforms, respectively VMware and OpenStack. Both platforms provide private clouds, used as backend infrastructures in Industry 4.0 application scenarios. The results serve as initial work to identify a security baseline and research needs for creating secure cloud environments for Industry 4.0.

The first part of this chapter reviews the design, implementation, and customer experience with the OLDES SW tele-care platform developed within the EU project Older people’s e-services at home. The OLDES solution has been successfully tested at two different locations: in Italy with the participation of a group of 100 seniors (including 10 senior citizens suffering from heart disease), and in the Czech Republic, with the involvement of a group of 10 diabetic patients. The suggested OLDES approach proved to be an effective solution for municipalities, hospitals, and their contact centres for providing health and social services. The project partners therefore decided to develop a second generation of the system called SPES (Support to Patients through E-Service Solutions), which started in April 2011. The SPES project aims at transferring the original approach and results achieved in implementing the OLDES focusing on new target problem domains: dementia, mobility-challenged persons, respiratory problems, and social exclusion.

In this paper we present a novel dataspace-based support platform for the international breath gas analysis community, which consists of over 30 institutions world wide with rapidly expanding research studies in various application domains. The work discussed in this paper is mainly consolidated of two independently targeted frameworks: a data life cycle management framework for e-Science applications and a code execution framework handling multiple problem solving environments. The first is based on our previous work, in particular the e-Science Life Cycle Ontology, which traces semantics about procedures a researcher is conducting during the execution of a scientific study. Within the ABA-platform these two frameworks are integrated as complementary components aiming at enabling reproducibility of breath gas studies. We present the architecture of the ABA-platform, discuss its security concept and provide a performance evaluation of the first vertical prototype implementation. Our approach to provide a platform that supports both, the preservation of scientific studies as well as their reproducibility with integrated execution services represents a novel solution for e-Science applications, where data management and code execution services can be complementary utilized.

Providing the appropriate access means for data mining services in Grid Environment is principal for com- bination of Grid and data mining. The transition from cen- tralized data mining process as they are in traditional tools to Grid-compliant and Grid-based data mining services that can coordinate with each other is important to extract useful and potential knowledge/patterns from distributed data resources. But, data mining process has not been integrated with the current Grid architecture, and there are no related specifications or standards available within the Grid com- munity for handling it. The work discusses the interface specification WS-DAI-DM and related issues of providing the consistent web service interfaces to mine meaningful and hidden information/patterns from distributed data re- sources in Grid environments. WS-DAI-DM can facilitate using data mining tools and developing data mining applica- tions. The proposed specification idea is partially imple- mented in the feasibility study and we give an application scenario about how users can access data mining services in Grid environments conveniently via the proposed mecha- nism.

The major aim of this survey is to identify the strengths and weaknesses of a representative set of Data-Mining and Integration (DMI) query languages. We describe a set of properties of DMI-related languages that we use for a systematic evaluation of these languages. In addition, we introduce a scoring system that we use to quantify our opinion on how well a DMI-related language supports a property. The languages surveyed in this paper include: DMQL, Mine SQL, MSQL, M2MQL, dmFSQL, OLEDB for DM, MINE RULE, and Oracle Data Mining. This survey may help researchers to propose a DMI language that is beyond the state-of-the-art, or it may help practitioners to select an existing language that fits well a purpose.

The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system's architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the system's ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

The exchange format in Software-oriented Architectures is typically XML based and for relational data the WebRowSet format is most prominent. Our WebRowSet implementation is based on XML indexing. It features on-demand parsing keeping only a part of the complete XML document in main memory and allows forward and backward navigation. This paper evaluates our implementation against other available ones, SUN reference implementation and OGSA-DAI implementation focused on data sets integrated into grids, with respect to time and memory needed in order to process large WebRowSet files.

Data mining deals with the extraction of hidden knowledge from large amounts of data. Nowadays, coarse-grained data mining modules are used. This traditional black box approach focuses on specific algorithm improvements and is not flexible enough to be used for more general optimization and beneficial component reuse. The work presented in this paper elaborates on decomposing data mining tasks as data mining execution process plans which are composed of finer-grained data mining operators. The cost of an operator can be analyzed and provides means for more holistic optimizations. This process-based data mining concept is evaluated via an OGSA-DAI based implementations for association rule mining which show the feasibility of our approach as well as the re-usability of some of the data mining operators.

The concept of mediation has recently been recognized as an important component of service-oriented architectures. The overall process of mediation is commonly split into design-time, preparing the ground to enable semantic mediation and run-time, actually doing it. Typically, a mediator is seen as a coarse grained black box component hiding its internals. Our proposal defines the runtime phase of mediation as a dynamic process of a set of well defined components. The benefits of such a decomposition include component reuse and assembling of different combinations, development process improvement and conformance to the service paradigm. Our approach is evaluated in the context of the European @neurIST project.

In recent years the focus of grid computing shifted towards more data intensive applications, increasingly needing access to various public and private databases. Relocating the code for Data Preprocessing (DPP) closer towards the data source is the overall task of the D³Gframework. This paper presents the data service side architecture to gather Data Statistics (DS) on-the-fly, use them in remote DPP methods on query results and gather exact continuous DS for whole tables inside a database. The performance results are showing low running costs for the continuous DS and the feasibility of the service side DPP functionality.

Data mining deals with finding hidden knowledge patterns in often huge data sets. The work presented in this paper elaborates on defining data mining tasks in terms of fine-grained composable operators instead of coarse-grained black box algorithms. Data mining tasks in the knowledge discovery process typically need one relational table as input and data preprocessing and integration beforehand. The possible combination of different kind of operators (relational, data mining and data preprocessing operators) represents a novel holistic view on the knowledge discovery process. Initially, as described in this paper, for the low-level execution phase but yielding the potential for rich optimization similar to relational query optimization. We argue that such macro-optimization embracing the overall KDD process leads to improved performance instead of focusing on just a small part of it via micro-optimization.

Although computational and data resources are available in geographically distributed locations in China's railway freight transportation information system, for further data analysis a centralized data warehouse is used. This approach leads to various issues: a centralized solution is not sufficient to meet the ever increasing demand for more and up-to-date datasets, accommodating additional applications is limited by the available computing power and therefore useful knowledge might not be exploited. Distributed computational resources and large accumulated freight transportation data are not being used comprehensively and effectively. This paper proposes to use Grid technology in China's railway freight transportation information system to solve upcoming application requirements by sharing resources and collaborative operations. We first describe the present situation of China's railway freight transportation information system and discuss its requirements and problems in detail. After that, we present related concepts of railway freight transportation information Grid and discuss its implementation. Finally, we use a typical application scenario to illustrate how it works. © 2009 by MIPRO.

Earth and life sciences are at the forefront to successfully include computational simulations and modeling. Medical applications are often mentioned as the killer applicationsfor the Grid. The complex methodology and models of Traditional Chinese Medicine offer different approaches to diagnose and treat a persons health condition than typical Western medicine. A possibility to make this often hidden knowledge explicit and available to a broader audience will result in mutual synergies for Western and Chinese medicine as well as improved patient care. This paper proposes the design and implementation of a method to accurately estimate blood glucose values using a novel non-invasive method based on electro-transformation measures in human body meridians. The framework used for this scientific computing collaboration, namely the China-Austria Data Grid (CADGrid) framework, provides an Intelligence Base offering commonly used models and algorithms as Web/Grid-Services. The controlled execution of the Non-Invasive Blood Glucose Measurement Service and the management of scientific data that arise from model execution can be seen as the first application on top of the CADGrid.

A focus of Grid computing are data intensive applications. Additionally, database management systems (DBMS) are gaining on importance in many scientific disciplines for publication of research results. The employment of Service-oriented-Architecture (SoA) raises the question of how DBMSs and their built-in technologies can be best utilized in such environments. A common way is to pull out all required data for a certain task from a source and process it service side far away from the original source. This approach is characterized by a passive usage of the DBMS as a pure data provider which implies significant overheads. The research effort described in this paper allows an active usage of a DBMS by relocating distributed query processing functionality inside it. Our novel solution utilizes the existing database technology, puts just the interface code at the service level while the data processing code resides at the database level and uses a push mechanism for the result data. The advantages are less overheads and data movement as well as increased data locality. Our proof of concept implementation is evaluated by comparing our distributed query processing prototype working inside popular relational DBMS (Oracle 10g and PostGreSQL 8.3) with a traditional installation of the OGSA-DQP middleware developed for distributed query processing on the Grid.

Chinese and Western medicine s have a different understanding and approach to life, health, and illness -joining their complementary work and support them by an advanced information technology could result in an improved health system. The Non-Invasive Blood Glucose Measurement(NIGM) Service is a grid based implementation of a novel on-invasive method for measuring human blood glucose values exploiting Chinese meridian theory. In this paper, we describe the implementation of the NIGM service in detail, present an initial performance evaluation and discuss an extension towards other non-invasive long term diabetic relevant measurement. Additionally, the adaption of the ontology-based Medical records Annotation Tool (MedAT) framework towards usage in NIGM trails is elaborated.