11,401 research outputs found

    A unified view of data-intensive flows in business intelligence systems : a survey

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    Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

    BI dashboard to empower PAC with near real-time decision support

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    Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis master's thesis focuses on the development and implementation of a Business Intelligence (BI) dashboard for an airline company. The objective is to provide a comprehensive data visualization solution that enables real-time monitoring and analysis of key performance indicators (KPIs) in the aviation industry. The research design combines qualitative and quantitative methods. The qualitative approach involves gathering requirements from stakeholders through the Agile methodology and iterative user feedback. The quantitative approach includes data analysis and the construction of a data model and dashboard using MicroStrategy. The data model follows the star schema model, facilitating efficient data integration and optimized query performance. The requirement-gathering process involved capturing relevant KPIs and dimensions from existing reports and functional requirements from stakeholders. These requirements were categorized into topics such as general requirements, on-time performance (OTP), crew requirements, cancelled legs, aircraft on the ground (AOGs), maintenance, and spares. The results section showcases the developed BI dashboard, emphasizing the dashboard design, selected KPIs, and visualizations. The user interface offers intuitive navigation and interactive features to facilitate data exploration and decision-making. User testing and feedback were incorporated to improve the dashboard's usability and effectiveness. Furthermore, the physical data model based on the star schema model contributes to query performance optimization, meeting the requirement of response times below 5 seconds for live dashboard usage. The use of best practices and methodologies, particularly those advocated by Kimball and Caserta, enhances the data modelling process. In conclusion, this master's thesis project presents a business intelligence dashboard tailored for the airline industry. By leveraging the power of data visualization and analytics, the dashboard empowers stakeholders to make informed decisions and gain valuable insights into various aspects of airline operations. The research contributes to the field of information management by showcasing the application of agile development, star schema modelling, and user-centric design in developing a robust BI solution for aviation

    Unleashing the Effectiveness of Process-oriented Information Systems: Problem Analysis, Critical Success Factors, Implications

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    Process-oriented information systems (IS) aim at the computerized support of business processes. So far, contemporary IS have often fail to meet this goal. To better understand this drawback, to systematically identify its rationales, and to derive critical success factors for business process support, we conducted three empirical studies: an exploratory case study in the automotive domain, an online survey among 79 IT professionals, and another online survey among 70 business process management (BPM) experts. This paper summarizes the findings of these studies, puts them in relation with each other, and uses them to show that "process-orientation" is scarce and "process-awareness" is needed in IS engineering

    Computer-Aided Warehouse Engineering (CAWE): Leveraging MDA and ADM for the Development of Data Warehouses

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    During the last decade, data warehousing has reached a high maturity and is a well-accepted technology in decision support systems. Nevertheless, development and maintenance are still tedious tasks since the systems grow over time and complex architectures have been established. The paper at hand adopts the concepts of Model Driven Architecture (MDA) and Architecture Driven Modernization (ADM) taken from the software engineering discipline to the data warehousing discipline. We show the works already available, outline further research directions and give hints for implementation of Computer-Aided Warehouse Engineering systems

    Economic impacts of SEZs: Theoretical approaches and analysis of newly notified SEZs in India

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    This study aims at examining the economic impacts of SEZs in the Indian context. While doing so, it addresses the conceptual confusion about SEZs, outlines the evolution of SEZs; traces economic philosophies explaining the rationale and benefits of SEZs; extends existing theoretical literature to explain the economic impacts of SEZs; assesses the economic impacts of newly notified SEZs in India; reviews the strategies followed by various state governments in the implementation of the policy ; and draws policy implications. It argues that the existing economic theories donot adequately explain the rationale and contribution of SEZs. These approaches need to be extended by integrating the provisions of the theories of agglomeration economies and global value chains within the existing theoretical frameworks. It analyses the economic impacts of SEZs within the extended theoretical framework. It finds that while SEZs are stimulating direct investment and employment, their role appears to be more valuable in bringing about economic transformation from a resource-led economy to a skill and technology-led economy; from low value added economic activities to high value added economic activities; from low productive sectors to high productive sectors; and from unorganised to organized sectors, both at the national and regional levels. They have the potential of promoting new knowledge intensive industries; augmenting existing industrial clusters/industrial states; diversifying the local industrial base; and localizing global value chain. However, a strategic approach is required to reap the opportunities offered by SEZs.Special economic zones; Exports; FDI; Economic diversification; Agglomeration economies; global value chains;India

    Managing Metadata in Data Warehouses: Pitfalls and Possibilities

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    This paper motivates a comprehensive academic study of metadata and the roles that metadata plays in organizational information systems. While the benefits of metadata and challenges in implementing metadata solutions are widely addressed in practitioner publications, explicit discussion of metadata in academic literature is rare. Metadata, when discussed, is perceived primarily as a technology solution. Integrated management of metadata and its business value are not well addressed. This paper discusses both the benefits offered by and the challenges associated with integrating metadata. It also describes solutions for addressing some of these challenges. The inherent complexity of an integrated metadata repository is demonstrated by reviewing the metadata functionality required in a data warehouse: a decision support environment where its importance is acknowledged. Comparing this required functionality with metadata management functionalities offered by data warehousing software products identifies crucial gaps. Based on these analyses, topics for further research on metadata are proposed

    Modélisation des transformations pour l'évolution de modèles multidimensionnels

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    La modélisation et l'entreposage des données ont constitué, depuis plus d'une décennie, une problématique de recherche pour laquelle différentes approches ont été proposées. Ces approches se focalisent sur des aspects statiques de l'entrepôt de données. Or, l'évolution du système d'information qui alimente un entrepôt peut avoir un impact sur ce dernier et peut conduire, par conséquent, à l'évolution de son modèle multidimensionnel. Dans ce contexte évolutif, nous proposons une démarche dirigée par les modèles pour automatiser la propagation de l'évolution du modèle de la source de données relationnelle vers l'entrepôt. Cette démarche est fondée sur deux modèles d'évolution ainsi qu'un ensemble de règles de transformation formalisées en Query/View/Transformation. Nous développons un prototype logiciel nommé DWE (« Data Warehouse Evolution ») qui supporte cette démarche
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