6,541 research outputs found

    Healthcare Data Analytics on the Cloud

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    Meaningful analysis of voluminous health information has always been a challenge in most healthcare organizations. Accurate and timely information required by the management to lead a healthcare organization through the challenges found in the industry can be obtained using business intelligence (BI) or business analytics tools. However, these require large capital investments to implement and support the large volumes of data that needs to be analyzed to identify trends. They also require enormous processing power which places pressure on the business resources in addition to the dynamic changes in the digital technology. This paper evaluates the various nuances of business analytics of healthcare hosted on the cloud computing environment. The paper explores BI being offered as Software as a Service (SaaS) solution towards offering meaningful use of information for improving functions in healthcare enterprise. It also attempts to identify the challenges that healthcare enterprises face when making use of a BI SaaS solution

    Designing a Prototype for Analytical Model Selection and Execution to Support Self-Service BI

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    This paper presents a prototype of a modeling tool specifically designed for business analysts with little modeling experience. The proposed tool has an interactive user interface for a dimensional data store that contains a library of analytical models that business analysts can evaluate and use to create models they can run on their own data sets. Using a design science approach, we review the relevant literature in self-efficacy and feedforward to provide a kernel theory that informs the design criteria met by our proof of concept prototype. Specifically, we demonstrate the prototype’s user interface with a prediction problem faced by the United States Department of Labor

    A framework for smart production-logistics systems based on CPS and industrial IoT

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    Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

    Business Intelligence Solution for an SME: A Case Study.

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    Business Intelligence (BI) leverages the usefulness of existing information. It equips business users with relevant information to perform various analyses to make key business decisions. Over the last two decades, BI has become a core strategy for the growth of many companies, in particular large corporations. However, studies show that small and medium-sized enterprises (SMEs) lag behind in implementation and exploitation of BI solutions. To stay ahead of the competition, SMEs must be able to monitor and effectively use all of their resources, in particular information resources, to assist them in making important business decisions. In this paper, we examine the challenges such as lack of technical expertise and limited budget when implementing a BI solution within an SME in the UK. In light of our experiences in tackling these issues, we discuss how these challenges can be overcome through applying various tools and strategies and the potential benefits

    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

    Data Warehouse and Business Intelligence: Comparative Analysis of Olap tools

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    Data Warehouse applications are designed basically to provide the business communities with accurate and consolidated information. The objective of Data Warehousing applications are not just for collecting data and reporting, but rather for analyzing, it requires technical and business expertise tools. To achieve business intelligence it requires proper tools to be selected. The most commonly used Business intelligence (BI) technologies are Online Analytical Processing (OLAP) and Reporting tools for analyzing the data and to make tactical decision for the better performance of the organization, and more over to provide quick and fast access to end user request. This study will review data warehouse environment and architecture, business intelligence concepts, OLAP and the related theories involved on it. As well as the concept of data warehouse and OLAP, this study will also present comparative analysis of commonly used OLAP tools in Organization

    Relatório de Estágio - Solução de BI Roaming Data Science (RoaDS) em ambiente Vodafone

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    A telecom company (Vodafone), had the need to implement a Business Intelligence solution for Roaming data across a wide set of different data sources. Based on the data visualization of this solution, its key users with decision power, can make a business analysis and needs of infrastructure and software expansion. This document aims to expose the scientific papers produced with the various stages of production of the solution (state of the art, architecture design and implementation results), this Business Intelligence solution was designed and implemented with OLAP methodologies and technologies in a Data Warehouse composed of Data Marts arranged in constellation, the visualization layer was custom made in JavaScript (VueJS). As a base for the results a questionnaire was created to be filled in by the key users of the solution. Based on this questionnaire it was possible to ascertain that user acceptance was satisfactory. The proposed objectives for the implementation of the BI solution with all the requirements was achieved with the infrastructure itself created from scratch in Kubernetes. This BI platform can be expanded using column storage databases created specifically with OLAP workloads in mind, removing the need for an OLAP cube layer. Based on Machine Learning algorithms, the platform will be able to perform the predictions needed to make decisions about Vodafone's Roaming infrastructure

    Empowering SMEs to make better decisions with Business Intelligence: A Case Study

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    With the advance of Business Information Systems (BIS), irrespective of the size, companies have adopted an approach to electronic data collection and management for two decades. The advancement in technology means they have in their possessions large volumes of historical data. Large organizations have cached on this and use a range of tools and techniques to leverage the usefulness of this information to make more informed business decisions. For most small and medium- sized enterprises (SMEs), however, such data typically sits in an archive without being utilized. While SMEs appreciate the need for utilizing historical data to make more informed business decisions, they often lack the technical knowhow and funding to embrace an effective BI solution. In this paper, drawing from our experience in implementing a BI solution for a UK SME we discuss some potential tools and strategies that could help SMEs overcome these challenges so as to reap the benefits of adopting an effective BI solution

    Scalable BI Cloud Solution

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    Nos últimos anos, tem-se assistido a um significante desenvolvimento do volume de transações no mercado da bolsa. Tendo em mente o fator volátil, porém poderoso que caracterizam a bolsa, torna-se valiosa a manipulação de dados relativos a este colosso mercado. Desta forma, a extração e tratamento destes dados leva à criação modelos preditivos e de análise, que possibilitam efetuar previsões levando, por sua vez, à melhoria da eficácia aquando do processo de tomada de decisão. Este projeto pretende explorar conhecimentos na área de processamento e análise de dados através do uso de tecnologias ETL em ambiente de cloud. Os processos de ETL tradicionais formam a espinha dorsal de todas as ferramentas de análise de dados a partir de um Data Warehouse, esta tem sido a forma de processar e analisar grandes quantidades de dados. De forma inovadora, este projeto pretende ainda construir um processo BI altamente escalável e extremamente eficiente, tanto a nível de custos como de disponibilidade e velocidade de operações, recorrendo a tecnologias cloud inovadoras e a uma orquestração ETL(T), que pretende desafiar a quase inquestionável atualidade e eficiência do tradicional ET
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