106 research outputs found

    Introducing Dynamic Behavior in Amalgamated Knowledge Bases

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    The problem of integrating knowledge from multiple and heterogeneous sources is a fundamental issue in current information systems. In order to cope with this problem, the concept of mediator has been introduced as a software component providing intermediate services, linking data resources and application programs, and making transparent the heterogeneity of the underlying systems. In designing a mediator architecture, we believe that an important aspect is the definition of a formal framework by which one is able to model integration according to a declarative style. To this purpose, the use of a logical approach seems very promising. Another important aspect is the ability to model both static integration aspects, concerning query execution, and dynamic ones, concerning data updates and their propagation among the various data sources. Unfortunately, as far as we know, no formal proposals for logically modeling mediator architectures both from a static and dynamic point of view have already been developed. In this paper, we extend the framework for amalgamated knowledge bases, presented by Subrahmanian, to deal with dynamic aspects. The language we propose is based on the Active U-Datalog language, and extends it with annotated logic and amalgamation concepts. We model the sources of information and the mediator (also called supervisor) as Active U-Datalog deductive databases, thus modeling queries, transactions, and active rules, interpreted according to the PARK semantics. By using active rules, the system can efficiently perform update propagation among different databases. The result is a logical environment, integrating active and deductive rules, to perform queries and update propagation in an heterogeneous mediated framework.Comment: Other Keywords: Deductive databases; Heterogeneous databases; Active rules; Update

    A conceptual method for data integration in business analytics

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    Viele Unternehmen funktionieren derzeit in einem schnellen, dynamischen und vor allem unbestĂ€ndigen Umfeld und wettbewerbsintensiven Markt. Daraus folgt, dass schnelle und faktenbasierende Entscheidungen ein wichtiger Erfolgsfaktor sein können. Basis fĂŒr solche Entscheidungen sind oft Informationen aus Business Intelligence und Business Analytics. Eine der Herausforderungen bei der Schaffung von hochqualitativer Information fĂŒr GeschĂ€ftsentscheidungen ist die Konsolidierung der Daten, die hĂ€ufig aus mehrfachen heterogenen Systemen innerhalb eines Unternehmens oder in ein oder mehreren Standorten verteilt sind. ETL-Prozesse (Extraction, Transforming and Loading) sind hĂ€ufig im Einsatz, um heterogene Daten aus einem oder mehreren Datenquellen in einem Zielsystem zusammenzufĂŒhren mit dem Ziel Data Marts oder Date Warehouse zu erstellen. Aufgrund mangelnder allgemeiner Methoden oder AnsĂ€tze, um systematisch solche ETL-Prozesse zu bewĂ€ltigen, und Aufgrund der hohen KomplexitĂ€t der Integration von Daten aus multiplen Quellen in einer allgemeinen, vereinheitlichten Darstellung, ist es sowohl fĂŒr Fachleute als auch fĂŒr die wenige erfahrene Anwender schwierig, Daten erfolgreich zu konsolidieren. Derzeit wird der analytische Prozess oft ohne vordefiniertes Rahmenwerk durchgefĂŒhrt und basiert eher auf informelles Wissen als auf eine wissenschaftliche Methodik. Das grĂ¶ĂŸte Problem mit kommerzieller Software, die den Datenintegrationsprozess inklusive Visualisierung, Wiederverwendung von analytischen Sequenzen und automatischer Übersetzung der visuellen Beschreibung in einem ausfĂŒhrbaren Code unterstĂŒtzt, ist, dass Metadaten fĂŒr die Datenintegration generell nur syntaktisches Wissen darstellt. Semantische Informationen ĂŒber die Datenstruktur sind typsicherweise nur in rudimentĂ€rer 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 KomplexitĂ€t der analytischen Prozesse zu reduzieren und FachkrĂ€fte in ihrer Arbeit zu unterstĂŒtzen, um somit auch den Prozess fĂŒr weniger erfahrene Anwender in unterschiedlichen DomĂ€nen zugĂ€nglich zu machen. Das Konzept ist detailliertes Wissen ĂŒber Daten in Business Analytics, speziell Information ĂŒber Semantik, zu berĂŒcksichtigen. Der Fokus liegt auf die Einbeziehung der strukturierten Beschreibung der Transformationsprozesse im Business Analytics, wo Informationen ĂŒber AbhĂ€ngigkeiten und Nebeneffekte von Algorithmen auch inkludiert sind. DarĂŒber hinaus bezieht dieser Ansatz das Meta-Modell Konzept mit ein: es prĂ€sentiert ein Rahmenwerk mit Modellierungskonzepte fĂŒr Datenintegration fĂŒr Business Analytics. Basierend auf Grossmans Ansatz ist das Ziel dieser Masterarbeit die Entwicklung eines Meta-Model Prototyps, der die Datenintegration fĂŒr Business Analytics unterstĂŒtz. 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 fĂŒr eine bestimmte angewandte Meta-Model Plattform passt. Das Ergebnis ist ein Prototyp, der auf einer generischen konzeptuellen Methode basiert, welche unabhĂ€ngig von der AusfĂŒhrbarkeit einer Plattform ist. DarĂŒber hinaus gibt es keine vordefinierte GranularitĂ€tsebene und die Modellobjekte sind fĂŒr die unterschiedlichen Phasen der Datenintegration Prozess wiederverwendbar. Der Prototyp wurde auf der Open Model Plattform eingesetzt. Die Open Model Plattform ist eine Initiative der UniversitĂ€t Wien mit dem Ziel die Verwendung von Modellierungsmethoden zu erweitern und diese durch das Rahmenwerk, welches alle mögliche ModellierungsaktivitĂ€ten beinhaltet, fĂŒr GeschĂ€ftsdomĂ€ne zur VerfĂŒgung zu stellen und nĂŒtzlich zu machen, um die ZugĂ€nglichkeit 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

    Toward Self-Organising Service Communities

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    This paper discusses a framework in which catalog service communities are built, linked for interaction, and constantly monitored and adapted over time. A catalog service community (represented as a peer node in a peer-to-peer network) in our system can be viewed as domain specific data integration mediators representing the domain knowledge and the registry information. The query routing among communities is performed to identify a set of data sources that are relevant to answering a given query. The system monitors the interactions between the communities to discover patterns that may lead to restructuring of the network (e.g., irrelevant peers removed, new relationships created, etc.)

    Reconciling Equational Heterogeneity within a Data Federation

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    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

    Graph BI & analytics: current state and future challenges

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    In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.Peer ReviewedPostprint (author's final draft

    31th International Conference on Information Modelling and Knowledge Bases

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    Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers
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