Adding semantic modules to improve goal-oriented analysis of data warehouses using I-star

The success rate of data warehouse (DW) development is improved by performing a requirements elicitation stage in which the users’ needs are modeled. Currently, among the different proposals for modeling requirements, there is a special focus on goal-oriented models, and in particular on the i* framework. In order to adapt this framework for DW development, we previously developed a UML profile for DWs. However, as the general i* framework, the proposal lacks modularity. This has a specially negative impact for DW development, since DW requirement models tend to include a huge number of elements with crossed relationships between them. In turn, the readability of the models is decreased, harming their utility and increasing the error rate and development time. In this paper, we propose an extension of our i* profile for DWs considering the modularization of goals. We provide a set of guidelines in order to correctly apply our proposal. Furthermore, we have performed an experiment in order to assess the validity our proposal. The benefits of our proposal are an increase in the modularity and scalability of the models which, in turn, increases the error correction capability, and makes complex models easier to understand by DW developers and non expert users.This work has been partially supported by the ProS-Req (TIN2010-19130-C02-01) and by the MESOLAP (TIN2010-14860) and SERENIDAD (PEII-11-0327-7035) projects from the Spanish Ministry of Education and the Junta de Comunidades de Castilla La Mancha respectively. Alejandro Maté is funded by the Generalitat Valenciana under an ACIF grant (ACIF/2010/298)

Using i* to describe data structures

This paper explores the use of the i* language as a notation to describe data structures to be used in classical imperative programs written in e.g. Java or C#. Data structures are described at two levels of abstraction, their specification and their implementation (the data structure properly said). We analyze how iStar 2.0, enriched with both modularization and dependum specialization constructs, can be used in this context.Peer ReviewedPostprint (published version

Software analytics tools: an intentional view

Software analytic tools consume big amounts of data coming from either (or both) the software development process or the system usage and aggregate them into indicators which are rendered to different types of stakeholders, also offering them a portfolio of techniques and capabilities such as what-if analysis, prediction and alerts. Precisely, the variety of stakeholders and the different goals they pursue justifies the convenience of performing an intentional analysis of the use of software analytics tools. With this aim, we first enumerate the different stakeholders and identify their intentional relationships with software analytics tools in the form of dependencies. Then, we focus on one particular stakeholder, namely the requirements engineer, and identify further intentional elements represented in a strategic rationale model. The resulting model provides an abstract view of the domain which may help stakeholders when deciding on the adoption of software analytic tools in their particular context.This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

The New Era of Business Intelligence Applications: Buildingfrom a Collaborative Point of View

Collaborative business intelligence (BI) iswidely embraced by enterprises as a way of making themost of their business processes. However, decision mak-ers usually work in isolation without the knowledge or thetime needed to obtain and analyze all the available infor-mation for making decisions. Unfortunately, collaborativeBI is currently based on exchanging e-mails and documentsbetween participants. As a result, information may be lost,participants may become disoriented, and the decision-making task may not yield the needed results. The authorspropose a modeling language aimed at modeling andeliciting the goals and information needs of participants ofcollaborative BI systems. This approach is based on inno-vative methods to elicit and model collaborative systemsand BI requirements. A controlled experiment was per-formed to validate this language, assessing its under-standability, scalability, efficiency, and user satisfaction byanalyzing two collaborative BI systems. By using theframework proposed in this work, clear guideless can beprovided regarding: (1) collaborative tasks, (2) their par-ticipants, and (3) the information to be shared among them.By using the approach to design collaborative BI systems,practitioners may easily trace every element needed in thedecision processes, avoiding the loss of information andfacilitating the collaboration of the stakeholders of suchprocesses

Lean thinking in healthcare services: learning from case studies

JEL: D22, I12Healthcare organisations, especially in public sector, have been adopting Lean management practices with increasing outcomes’ evidences in several parts of the world, since the beginning of this century. However, Lean deployment in Healthcare services has been addressed in the literature in a surgical way by an array of case reports addressing the “hard” side of Lean deployment, sometimes with no result’s consistency or even follow-up analysis. This thesis seek to add to the operational side of Lean deployment in Healthcare, a complementary understanding of Lean deployment approaches, addressing both “hard” and “soft” sides, identifying the real constraints of Lean in Healthcare sector and the sustainability factors. Supported by two main literature reviews and a multi-case approach, a deep research on the eligible Portuguese cases was conducted answering the questions: (i) What are the different outcomes from Lean deployment in Healthcare?; (ii) What are the barriers to Lean implementation in Healthcare?; (iii) What enables Lean implementation in Healthcare?; (iv) What are the risks of Lean in Healthcare?; (v) How to measure Lean achievements in Healthcare services?; and (vi) How to develop a sustainable Lean culture? This contribution to the academic debate on Lean deployment in Healthcare creates clarity on what can be called Lean practices in Healthcare settings under the light of the concept’s founders; what pattern of a Lean deployment journey was followed by Healthcare organisations; and how different cultural (organisational and national) contexts can influence the pace in pursuing that pattern.As organizações de saúde, nomeadamente públicas, têm vindo a adoptar práticas de gestão Lean com crescente evidência de resultados em várias partes do mundo, desde o início deste século. Contudo, a aplicação do Lean em serviços de saúde tem tido um tratamento cirúrgico na literatura, recaindo apenas nos aspectos “hard” e sem grande consistência ou seguimento de resultados . Esta tese pretende acrescentar aos aspectos “hard” do Lean, um entendimento complementar juntando os aspectos “hard” e “soft”, identificando as restrições e factores de sustentabilidade da aplicação do Lean no sector da saúde. Tendo por base duas revisões bibliográficas primordiais e uma abordagem empírica multi-caso a partir de casos portugueses elegíveis, esta tese fornece respostas às questões: (i) Quais os diferentes resultados da aplicação do Lean na Saúde?; (ii) Quais as barreiras à aplicação do Lean na Saúde?; (iii) Quais os facilitadores da implementação do Lean na Saúde?; (iv) Quais os riscos do Lean na Saúde?; (v) Como medir a implementação do Lean na Saúde; e (vi) como desenvolver uma cultura Lean sustentável? Este contributo para o debate académico sobre a aplicação do Lean na Saúde introduz clareza sobre o que pode ou não ser chamado de práticas Lean na Saúde tendo como referência os conceitos dos fundadores; que padrão de implementação é seguido pelas organizações; e de que forma diferentes contextos culturais (nacionais e organizacionais) influenciam o ritmo desse padrão de implementação

The GOALS approach: business and software modeling traceability by means of human-computer interaction: enterprise modeling language and method

The management of an enterprise relies on the continuous organization and development of its business and software systems. A process that requires merging the ideas of the enterprise’ systems managers, targeting the specification of business requirements and the conception and implementation of a supporting information system. This process finds obstacles in the identification and communication of requirements, and also in their transformation in software artefacts, leading to difficulties or loss of traceability between business and software models. Existing methods, languages and techniques are still not sufficiently standardized to ensure that when a business improvement is introduced, the supportive software solution will be implemented within budget and time. Methods are still too closed to the concepts of their original scientific domains, conceiving solutions which are not representative of the business and software conceptual relation and of the complexity concealed in an improvement effort, namely concerning usability and user experience. Moreover, the lack of a common modeling language and method for the conception of holistic and traceable software solutions, also refrains the performance of the enterprise development process. The GOALS Approach presents a solution to surpass these barriers by means of the specification of an enterprise modeling language that relates the business and software conceptual structures using a shared set of concepts, a notation, process, method and techniques, that allow the design of the software as a result of the business organization, ensuring traceability by means of the permanent representation of the business structure in the software structure

The Mental Database

This article uses database, evolution and physics considerations to suggest how the mind stores and processes its data. Its innovations in its approach lie in:- A) The comparison between the capabilities of the mind to those of a modern relational database while conserving phenomenality. The strong functional similarity of the two systems leads to the conclusion that the mind may be profitably described as being a mental database. The need for material/mental bridging and addressing indexes is discussed. B) The consideration of what neural correlates of consciousness (NCC) between sensorimotor data and instrumented observation one can hope to obtain using current biophysics. It is deduced that what is seen using the various brain scanning methods reflects only that part of current activity transactions (e.g. visualizing) which update and interrogate the mind, but not the contents of the integrated mental database which constitutes the mind itself. This approach yields reasons why there is much neural activity in an area to which a conscious function is ascribed (e.g. the amygdala is associated with fear), yet there is no visible part of its activity which can be clearly identified as phenomenal. The concept is then situated in a Penrosian expanded physical environment, requiring evolutionary continuity, modularity and phenomenality.Several novel Darwinian advantages arising from the approach are described