Організація побудови просторів даних туристичної сфери

У статті описано методи отримання, інтеграції та завантаження даних у сховищах даних туристичної сфери. Побудовано модель простору даних туристичної сфери.Аn order, methods and facilities of getting, concordance, integration of information, creation of operative depositories of information and load of information, is in-process worked out in a central depository. The dataspase model is build. The maim problems of tourism sphere are described

Mapping and Describing Geospatial Data to Generalize Complex Models: The Case of LittoSIM-GEN

For some scientific questions, empirical data are essential to develop reliable simulation models. These data usually come from different sources with diverse and heterogeneous formats. The design of complex data-driven models is often shaped by the structure of the data available in research projects. Hence, applying such models to other case studies requires either to get similar data or to transform new data to fit the model inputs. It is the case of agent-based models (ABMs) that use advanced data structures such as Geographic Information Systems data. We faced this problem in the LittoSIM-GEN project when generalizing our participatory flooding model (LittoSIM) to new territories. From this experience, we provide a mapping approach to structure, describe, and automatize the integration of geospatial data into ABMs

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

In multidisciplinary engineering (MDE) projects, for example, automation systems or manufacturing systems, stakeholders from various disciplines, for example, electrics, mechanics and software, have to collaborate. In industry practice, engineers apply individual and highly specialized tools with strong limitation regarding defect detection in early engineering phases. Experts typically execute reviews with limited tool support which make engineering projects defective and risky. Semantic Web Technologies (SWTs) can help to bridge the gap between heterogeneous sources as foundation for efficient and effective defect detection. Main questions focus on (a) how to bridge gaps between loosely coupled tools and incompatible data models and (b) how SWTs can help to support efficient and effective defect detection in context of engineering process improvement. This chapter describes success-critical requirements for defect detection in MDE and shows how SWTs can provide the foundation for early and efficient defect detection with an adapted review approach. The proposed defect detection framework (DDF) suggests different levels of SWT contributions as a roadmap for engineering process improvement. Two selected industry-related real-life cases show different levels of SWT involvement. Although SWTs have been successfully applied in real-life use cases, SWT applications can be risky if applied without good understanding of success factors and limitations

A Goal and Ontology Based Approach for Generating ETL Process Specifications

Data warehouse (DW) systems development involves several tasks such as defining requirements, designing DW schemas, and specifying data transformation operations. Indeed, the success of DW systems is very much dependent on the proper design of the extracting, transforming, and loading (ETL) processes. However, the common design-related problems in the ETL processes such as defining user requirements and data transformation specifications are far from being resolved. These problems are due to data heterogeneity in data sources, ambiguity of user requirements, and the complexity of data transformation activities. Current approaches have limitations on the reconciliation of DW requirement semantics towards designing the ETL processes. As a result, this has prolonged the process of the ETL processes specifications generation. The semantic framework of DW systems established from this study is used to develop the requirement analysis method for designing the ETL processes (RAMEPs) from the different perspectives of organization, decision-maker, and developer by using goal and ontology approaches. The correctness of RAMEPs approach was validated by using modified and newly developed compliant tools. The RAMEPs was evaluated in three real case studies, i.e., Student Affairs System, Gas Utility System, and Graduate Entrepreneur System. These case studies were used to illustrate how the RAMEPs approach can be implemented for designing and generating the ETL processes specifications. Moreover, the RAMEPs approach was reviewed by the DW experts for assessing the strengths and weaknesses of this method, and the new approach is accepted. The RAMEPs method proves that the ETL processes specifications can be derived from the early phases of DW systems development by using the goal-ontology approach

Reducing variability in the cost of energy of ocean energy arrays

Variability in the predicted cost of energy of an ocean energy converter array is more substantial than for other forms of energy generation, due to the combined stochastic action of weather conditions and failures. If the variability is great enough, then this may influence future financial decisions. This paper provides the unique contribution of quantifying variability in the predicted cost of energy and introduces a framework for investigating reduction of variability through investment in components. Following review of existing methodologies for parametric analysis of ocean energy array design, the development of the DTOcean software tool is presented. DTOcean can quantify variability by simulating the design, deployment and operation of arrays with higher complexity than previous models, designing sub-systems at component level. A case study of a theoretical floating wave energy converter array is used to demonstrate that the variability in levelised cost of energy (LCOE) can be greatest for the smallest arrays and that investment in improved component reliability can reduce both the variability and most likely value of LCOE. A hypothetical study of improved electrical cables and connectors shows reductions in LCOE up to 2.51% and reductions in the variability of LCOE of over 50%; these minima occur for different combinations of components.The research leading to this publication is part of the DTOceanPlus project which has received funding from the EuropeanUnion's Horizon 2020 research and innovation programme under grant agreement No 785921. Funding was also received from the European Community's Seventh Framework Programme for the DTOcean Project (grant agreement No. 608597). The contribution of Sandia National Laboratories was funded by the U.S. Department of Energy's Water Power Technologies Office. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. The image of the RM3 device, in Fig. 7, was reproduced with the permission of Sandia National Laboratorie

Reconciling Equational Heterogeneity within a Data Federation

Mappings in most federated databases are conceptualized and implemented as black-box transformations between source schemas and a federated schema. This approach does not allow specific mappings to be declared once and reused in other situations. We present an alternative approach, in which data-level mappings are represented independent of source and federated schemas as a network between “contexts”. This compendious representation expedites the data federation process via mapping reuse and automated mapping composition from simpler mappings. We illustrate the benefits of mapping reuse and composition by using an example that incorporates equational mappings and the application of symbolic equation solving techniques

A conceptual method for data integration in business analytics

Viele Unternehmen funktionieren derzeit in einem schnellen, dynamischen und vor allem unbeständigen Umfeld und wettbewerbsintensiven Markt. Daraus folgt, dass schnelle und faktenbasierende Entscheidungen ein wichtiger Erfolgsfaktor sein können. Basis für solche Entscheidungen sind oft Informationen aus Business Intelligence und Business Analytics. Eine der Herausforderungen bei der Schaffung von hochqualitativer Information für Geschäftsentscheidungen ist die Konsolidierung der Daten, die häufig aus mehrfachen heterogenen Systemen innerhalb eines Unternehmens oder in ein oder mehreren Standorten verteilt sind. ETL-Prozesse (Extraction, Transforming and Loading) sind häufig im Einsatz, um heterogene Daten aus einem oder mehreren Datenquellen in einem Zielsystem zusammenzuführen mit dem Ziel Data Marts oder Date Warehouse zu erstellen. Aufgrund mangelnder allgemeiner Methoden oder Ansätze, um systematisch solche ETL-Prozesse zu bewältigen, und Aufgrund der hohen Komplexität der Integration von Daten aus multiplen Quellen in einer allgemeinen, vereinheitlichten Darstellung, ist es sowohl für Fachleute als auch für die wenige erfahrene Anwender schwierig, Daten erfolgreich zu konsolidieren. Derzeit wird der analytische Prozess oft ohne vordefiniertes Rahmenwerk durchgeführt und basiert eher auf informelles Wissen als auf eine wissenschaftliche Methodik. Das größte Problem mit kommerzieller Software, die den Datenintegrationsprozess inklusive Visualisierung, Wiederverwendung von analytischen Sequenzen und automatischer Übersetzung der visuellen Beschreibung in einem ausführbaren Code unterstützt, ist, dass Metadaten für die Datenintegration generell nur syntaktisches Wissen darstellt. Semantische Informationen über die Datenstruktur sind typsicherweise nur in rudimentärer Form vorhanden und das obwohl sie eine signifikante Rolle bei der Definition des analytischen Modells und der Evaluierung des Ergebnisse spielen. Vor diesem Hintergrund hat Grossmann das “Conceptual Approach for Data Integration for Business Analytics” formuliert. Es zielt darauf hin, die Komplexität der analytischen Prozesse zu reduzieren und Fachkräfte in ihrer Arbeit zu unterstützen, um somit auch den Prozess für weniger erfahrene Anwender in unterschiedlichen Domänen zugänglich zu machen. Das Konzept ist detailliertes Wissen über Daten in Business Analytics, speziell Information über Semantik, zu berücksichtigen. Der Fokus liegt auf die Einbeziehung der strukturierten Beschreibung der Transformationsprozesse im Business Analytics, wo Informationen über Abhängigkeiten und Nebeneffekte von Algorithmen auch inkludiert sind. Darüber hinaus bezieht dieser Ansatz das Meta-Modell Konzept mit ein: es präsentiert ein Rahmenwerk mit Modellierungskonzepte für Datenintegration für Business Analytics. Basierend auf Grossmans Ansatz ist das Ziel dieser Masterarbeit die Entwicklung eines Meta-Model Prototyps, der die Datenintegration für Business Analytics unterstütz. Der Fokus liegt auf dem intellektuellen Prozess der Umwandlung einer theoretischen Methode in einem konzeptuellen Model, das auf ein Rahmenwerk von Modellierungsmethoden angewendet werden kann und welches zu den spezifischen Konzepten für eine bestimmte angewandte Meta-Model Plattform passt. Das Ergebnis ist ein Prototyp, der auf einer generischen konzeptuellen Methode basiert, welche unabhängig von der Ausführbarkeit einer Plattform ist. Darüber hinaus gibt es keine vordefinierte Granularitätsebene und die Modellobjekte sind für die unterschiedlichen Phasen der Datenintegration Prozess wiederverwendbar. Der Prototyp wurde auf der Open Model Plattform eingesetzt. Die Open Model Plattform ist eine Initiative der Universität Wien mit dem Ziel die Verwendung von Modellierungsmethoden zu erweitern und diese durch das Rahmenwerk, welches alle mögliche Modellierungsaktivitäten beinhaltet, für Geschäftsdomäne zur Verfügung zu stellen und nützlich zu machen, um die Zugänglichkeit bei dein Anwendern zu steigern.Today many organizations are operating in dynamic and rapid changing environment and highly competitive markets. Consequently fast and accurate fact-based decisions can be an important success factor. The basis for such decisions is usually business information as a result of business intelligence and business analytics in the corporate associations. One of the challenges of creating high-quality information for business decision is to consolidate the collected data that is spread in multiple heterogeneous systems throughout the organization in one or many different locations. Typically ETL-processes (Extraction, Transforming and Loading) are used to merge heterogeneous data from one or more data sources into a target system to form data repositories, data marts, or data warehouses. Due to the lack of a common methods or approaches to systematically manage such ETL processes and the high complexity of the task of integrating data from multiple sources to one common and unified view, it is difficult for both professionals and less experienced users to successfully consolidate data. Currently the analysis process is often performed without any predefined framework and is rather based on informal basis than a scientific methodology. Hence, for commercial tools that are supporting the data integration process including visualization of the integration, the reuse of analyses sequences and the automatic translation of the visual description to executable code, the major problem is that metadata used for data integration in general is only employed for representation of syntactic knowledge. Semantic information about the data structure is typically only available in a rudimentary form though it plays a significant role in defining the analysis model and the evaluation of the results. With this background Grossmann developed a “Conceptual Approach for Data Integration for Business Analytics”. It aims to support professionals by making business analytics easier and consequently more applicable to less experienced user in different domains. The idea is to incorporate detailed knowledge about the data in business analytics, especially information about semantics. It focuses on the inclusion of a more structured description of the transformation process in business analytics in which information about dependencies and side effects of the algorithms are included. Furthermore the approach incorporates the concept of meta-modelling; it presents a framework including the modelling concepts for data integration for business analytics. The idea of the thesis at hand is to develop a meta-model prototype that supports Data Integration for Business Analytics based on Grossman’s approach. The paper focuses on the intellectual process of transforming the theoretical method into a conceptual model which can be applied to the framework of a modelling methods and which fits to the specific concepts of a meta-model platform used. The result is a prototype based on a generic conceptual method which is execution platform independent, there are no pre-defined granularity levels and the objects of the model are re-usable for the different phases of the data integration process. The prototype is deployed on the Open Model Platform, an initiative started at the University of Vienna that aims to extend the usage of modelling methods and models and to make it more accessible to users by offering a framework including all kinds of modelling activities useful for business applications

Doctor of Philosophy

dissertationBiomedical data are a rich source of information and knowledge. Not only are they useful for direct patient care, but they may also offer answers to important population-based questions. Creating an environment where advanced analytics can be performed against biomedical data is nontrivial, however. Biomedical data are currently scattered across multiple systems with heterogeneous data, and integrating these data is a bigger task than humans can realistically do by hand; therefore, automatic biomedical data integration is highly desirable but has never been fully achieved. This dissertation introduces new algorithms that were devised to support automatic and semiautomatic integration of heterogeneous biomedical data. The new algorithms incorporate both data mining and biomedical informatics techniques to create "concept bags" that are used to compute similarity between data elements in the same way that "word bags" are compared in data mining. Concept bags are composed of controlled medical vocabulary concept codes that are extracted from text using named-entity recognition software. To test the new algorithm, three biomedical text similarity use cases were examined: automatically aligning data elements between heterogeneous data sets, determining degrees of similarity between medical terms using a published benchmark, and determining similarity between ICU discharge summaries. The method is highly configurable and 5 different versions were tested. The concept bag method performed particularly well aligning data elements and outperformed the compared algorithms by iv more than 5%. Another configuration that included hierarchical semantics performed particularly well at matching medical terms, meeting or exceeding 30 of 31 other published results using the same benchmark. Results for the third scenario of computing ICU discharge summary similarity were less successful. Correlations between multiple methods were low, including between terminologists. The concept bag algorithms performed consistently and comparatively well and appear to be viable options for multiple scenarios. New applications of the method and ideas for improving the algorithm are being discussed for future work, including several performance enhancements, configuration-based enhancements, and concept vector weighting using the TF-IDF formulas