78,000 research outputs found
Software como um Serviço: uma plataforma eficaz para oferta de sistemas holĂsticos de gestĂŁo da performance
This study main objective was to assess the viability of development of a Performance Management (PM) system, delivered in the form of Software as a Service (SaaS), specific for the hospitality industry and to evaluate the benefits of its use. Software deployed in the cloud, delivered and licensed as a service, is becoming increasingly common and accepted in a business context. Although, Business Intelligence (BI) solutions are not usually distributed in the SaaS model, there are some examples that this is changing. To achieve the study objective, design science research methodology was employed in the development of a prototype. This prototype was deployed in four hotels and its results evaluated. Evaluation of the prototype was focused both on the system technical characteristics and business benefits. Results shown that hotels were very satisfied with the system and that building a prototype and making it available in the form of SaaS is a good solution to assess BI systems contribution to improve management performance.O objetivo principal deste estudo Ă© avaliar a viabilidade de
desenvolvimento de um sistema de GestĂŁo da Performance, entregue
sob a forma de “Software como Serviço” (SaaS), especĂfico para o setor
hoteleiro, e tambĂ©m avaliar os benefĂcios de seu uso. O software
implantado na cloud, entregue e licenciado como um serviço, é cada vez
mais aceite num contexto de negĂłcios. Todavia, nĂŁo Ă© comum que
soluções de Business Intelligence (BI) sejam distribuĂdas neste modelo
SaaS. No entanto, existem alguns exemplos de que isso se está a alterar.
Para atingir o objetivo do estudo, foi utilizada Design Science Research
como metodologia de pesquisa cientĂfica para desenvolvimento de um
protótipo. Este protótipo foi implementado em quatro hotéis para que
os seus resultados pudessem ser avaliados. A avaliação foi focada tanto
nas caracterĂsticas tĂ©cnicas do sistema como nos benefĂcios para o
negócio. Os resultados mostraram que os hotéis estavam muito
satisfeitos com o sistema e que construir um protótipo e disponibilizá-lo sob a forma de SaaS é uma boa solução para avaliar a contribuição
dos sistemas de BI para melhorar o desempenho da gestĂŁo.info:eu-repo/semantics/publishedVersio
Knowledge Engineering Architecture for the Analysis of Organizational Log Data: a software tool for log data analysis
Organisation log data are generate by software and can provide help to maintenance team addressing issues reported by the client. Once an application is in production, there are defects and other issues that need to be handled. This is also cause by customisation and maintenance of the software. That can compromise software integrity and functionality. This happening in production environment which the maintenance team don’t have access becomes a difficult to resolve. The issue must be handling in development environment which causes a condition to understand the problem in order to be able to fix it. To help with this, using log data from production to trace actions that occur of the issue. The log data doesn’t contain any of private data; it only contains actions events as a result of software usage. The main objective of this thesis work is to build a framework for an automatic log analyser to assist maintenance team addressing software issues. This framework also provides a knowledge management system allowing registering tacit experience into explicit knowledge. A prototype was developed to produce metrics and make a proof of this framework. This was done on a real environment and is related to a software migration project which means transferring data between databases that holds company business
"BURO Case Study" In "Making the Repository Count: lessons from successful implementation"
Matt Holland and Tim Denning continue the research theme and consider the importance of IRs in support of research, focussing on three areas; how the IR fits with the university organisation; how to promote the use of the IR to end users and contributors; and how to secure long term benefits for the broadest range of stakeholders. They incorporate two case studies into the discussion, and include a description of the implementation of Bournemouth University Research Online (BURO). With contributions from Emma Crowley, BURO Manager
Mercury: using the QuPreSS reference model to evaluate predictive services
Nowadays, lots of service providers offer predictive services that show in advance a condition or occurrence about the future. As a consequence, it becomes necessary for service customers to select the predictive service that best satisfies their needs. The QuPreSS reference model provides a standard solution for the selection of predictive services based on the quality of their predictions. QuPreSS has been designed to be applicable in any predictive domain (e.g., weather forecasting, economics, and medicine). This paper presents Mercury, a tool based on the QuPreSS reference model and customized to the weather forecast domain. Mercury measures weather predictive services' quality, and automates the context-dependent selection of the most accurate predictive service to satisfy a customer query. To do so, candidate predictive services are monitored so that their predictions can be eventually compared to real observations obtained from a trusted source. Mercury is a proof-of-concept of QuPreSS that aims to show that the selection of predictive services can be driven by the quality of their predictions. Throughout the paper, we show how Mercury was built from the QuPreSS reference model and how it can be installed and used.Peer ReviewedPostprint (author's final draft
Data Management Roles for Librarians
In this Chapter:â—Ź Looking at data through different lensesâ—Ź Exploring the range of data use and data support â—Ź Using data as the basis for informed decision making â—Ź Treating data as a legitimate scholarly research produc
Measuring Software Process: A Systematic Mapping Study
Context: Measurement is essential to reach predictable performance and high capability processes. It provides
support for better understanding, evaluation, management, and control of the development process
and project, as well as the resulting product. It also enables organizations to improve and predict its process’s
performance, which places organizations in better positions to make appropriate decisions. Objective:
This study aims to understand the measurement of the software development process, to identify studies,
create a classification scheme based on the identified studies, and then to map such studies into the scheme
to answer the research questions. Method: Systematic mapping is the selected research methodology for this
study. Results: A total of 462 studies are included and classified into four topics with respect to their focus
and into three groups based on the publishing date. Five abstractions and 64 attributes were identified,
25 methods/models and 17 contexts were distinguished. Conclusion: capability and performance were the
most measured process attributes, while effort and performance were the most measured project attributes.
Goal Question Metric and Capability Maturity Model Integration were the main methods and models used
in the studies, whereas agile/lean development and small/medium-size enterprise were the most frequently
identified research contexts.Ministerio de EconomĂa y Competitividad TIN2013-46928-C3-3-RMinisterio de EconomĂa y Competitividad TIN2016-76956-C3-2- RMinisterio de EconomĂa y Competitividad TIN2015-71938-RED
Nonprofit Performance Management: Using Data to Measure and Improve Programs
Tracking and measuring data can give nonprofits a better understanding of the populations they serve and how they serve them. It can also help them identify areas they can improve to boost the reach and effectiveness of their programs. But many organizations struggle with the idea of using data.How do successful nonprofits go about the process of implementing their data practices? What software do they use? What are the obstacles they face, and how do they overcome them? To find out, we reached out to our network of experts and consultants for examples of organizations that were successfully using data to improve and direct their work, and narrowed their list of recommendations down to 10 nonprofits of different sizes, missions, and locations.We talked to staffers at each who were involved with data and analyzed the information we gathered for common themes, best practices, and any patterns that might be useful. We also asked them for advice for other organizations looking to replicate their successes and learn from their mistakes.From those 10 organizations, we chose seven for case studies about the different ways they were using data. This report is built around those case studies and the additional conversations we had
Predicting Defects in Software Using Grammar-Guided Genetic Programming
The knowledge of the software quality can allow an organization to allocate the needed resources for the code maintenance. Maintaining the software is considered as a high cost factor for most organizations. Consequently, there is need to assess software modules in respect of defects that will arise. Addressing the prediction of software defects by means of computational intelligence has only recently become evident. In this paper, we investigate the capability of the genetic programming approach for producing solution composed of decision rules. We applied the model into four software engineering databases of NASA. The overall performance of this system denotes its competitiveness as compared with past methodologies, and is shown capable of producing simple, highly accurate, tangible rules
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