Semantic Bridging between Conceptual Modeling Standards and Agile Software Projects Conceptualizations

Software engineering benefitted from modeling standards (e.g. UML, BPMN), but Agile Software Project Management tends to marginalize most forms of documentation including diagrammatic modeling, focusing instead on the tracking of a project\u27s backlog and related issues. Limited means are available for annotating Jira items with diagrams, however not on a granular and semantically traceable level. Business processes tend to get lost on the way between process analysis (if any) and backlog items; UML design decisions are often disconnected from the issue tracking environment. This paper proposes domain-specific conceptual modeling to obtain a diagrammatic view on a Jira project, motivated by past conceptualizations of the agile paradigm while also offering basic interoperability with Jira to switch between environments and views. The underlying conceptualization extends conceptual modeling languages (BPMN, UML) with an agile project management perspective to enrich contextual traceability of a project\u27s elements while ensuring that data structures handled by Jira can be captured and exposed to Jira if needed. Therefore, concepts underlying the typical software development project management are integrated with established modeling concepts and tailored (with metamodeling means) for the domain-specificity of agile project management. A Design Science approach was pursued to develop a modeling method artifact, resulting in a domain-specific modeling tool for software project managers that want to augment agile practices and enrich issue annotation

Increasing Students’ Learning Outcomes through Cooperative Learning Model Mind Mapping Type

The purpose of this study is to find out how to apply the cooperative learning model, mind mapping type to improve the learning outcomes of elementary school students. This type of research is classroom action research which consists of two cycles which each cycle is carried out 3 times. The research procedure includes planning, implementing actions, observing and reflecting. The subjects in this study were 26 fourth grade students at SD Inpres Batanghari Jambi. The research result showed that in the first cycle, from 26 students, only 14 students completed individually, with an average score of 67.30. This has met the minimum completeness criteria or is in the moderate category, but this result does not meet the classical completeness criteria because only 53.85% of students have studied thoroughly, while the classical completeness that must be achieved is 85% of the total number of students. In cycle II, from 26 students, there were 24 students (92.31%) who met the minimum completeness criteria. Classically, it has also been fulfilled, namely the average value obtained is 80.19 or is in the high category. Based on the results of the analysis, it was concluded that the learning outcomes of fourth grade elementary school students through the application of the Mind Mapping cooperative learning model had increased

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

Methodological approaches and techniques for designing ontologies in information systems requirements engineering

Programa doutoral em Information Systems and TechnologyThe way we interact with the world around us is changing as new challenges arise, embracing innovative business models, rethinking the organization and processes to maximize results, and evolving change management. Currently, and considering the projects executed, the methodologies used do not fully respond to the companies' needs. On the one hand, organizations are not familiar with the languages used in Information Systems, and on the other hand, they are often unable to validate requirements or business models. These are some of the difficulties encountered that lead us to think about formulating a new approach. Thus, the state of the art presented in this paper includes a study of the models involved in the software development process, where traditional methods and the rivalry of agile methods are present. In addition, a survey is made about Ontologies and what methods exist to conceive, transform, and represent them. Thus, after analyzing some of the various possibilities currently available, we began the process of evolving a method and developing an approach that would allow us to design ontologies. The method we evolved and adapted will allow us to derive terminologies from a specific domain, aggregating them in order to facilitate the construction of a catalog of terminologies. Next, the definition of an approach to designing ontologies will allow the construction of a domain-specific ontology. This approach allows in the first instance to integrate and store the data from different information systems of a given organization. In a second instance, the rules for mapping and building the ontology database are defined. Finally, a technological architecture is also proposed that will allow the mapping of an ontology through the construction of complex networks, allowing mapping and relating terminologies. This doctoral work encompasses numerous Research & Development (R&D) projects belonging to different domains such as Software Industry, Textile Industry, Robotic Industry and Smart Cities. Finally, a critical and descriptive analysis of the work done is performed, and we also point out perspectives for possible future work.A forma como interagimos com o mundo à nossa volta está a mudar à medida que novos desafios surgem, abraçando modelos empresariais inovadores, repensando a organização e os processos para maximizar os resultados, e evoluindo a gestão da mudança. Atualmente, e considerando os projetos executados, as metodologias utilizadas não respondem na totalidade às necessidades das empresas. Por um lado, as organizações não estão familiarizadas com as linguagens utilizadas nos Sistemas de Informação, por outro lado, são muitas vezes incapazes de validar requisitos ou modelos de negócio. Estas são algumas das dificuldades encontradas que nos levam a pensar na formulação de uma nova abordagem. Assim, o estado da arte apresentado neste documento inclui um estudo dos modelos envolvidos no processo de desenvolvimento de software, onde os métodos tradicionais e a rivalidade de métodos ágeis estão presentes. Além disso, é efetuado um levantamento sobre Ontologias e quais os métodos existentes para as conceber, transformar e representar. Assim, e após analisarmos algumas das várias possibilidades atualmente disponíveis, iniciou-se o processo de evolução de um método e desenvolvimento de uma abordagem que nos permitisse conceber ontologias. O método que evoluímos e adaptamos permitirá derivar terminologias de um domínio específico, agregando-as de forma a facilitar a construção de um catálogo de terminologias. Em seguida, a definição de uma abordagem para conceber ontologias permitirá a construção de uma ontologia de um domínio específico. Esta abordagem permite em primeira instância, integrar e armazenar os dados de diferentes sistemas de informação de uma determinada organização. Num segundo momento, são definidas as regras para o mapeamento e construção da base de dados ontológica. Finalmente, é também proposta uma arquitetura tecnológica que permitirá efetuar o mapeamento de uma ontologia através da construção de redes complexas, permitindo mapear e relacionar terminologias. Este trabalho de doutoramento engloba inúmeros projetos de Investigação & Desenvolvimento (I&D) pertencentes a diferentes domínios como por exemplo Indústria de Software, Indústria Têxtil, Indústria Robótica e Smart Cities. Finalmente, é realizada uma análise critica e descritiva do trabalho realizado, sendo que apontamos ainda perspetivas de possíveis trabalhos futuros

New Generation Sensor Web Enablement

Many sensor networks have been deployed to monitor Earth’s environment, and more will follow in the future. Environmental sensors have improved continuously by becoming smaller, cheaper, and more intelligent. Due to the large number of sensor manufacturers and differing accompanying protocols, integrating diverse sensors into observation systems is not straightforward. A coherent infrastructure is needed to treat sensors in an interoperable, platform-independent and uniform way. The concept of the Sensor Web reflects such a kind of infrastructure for sharing, finding, and accessing sensors and their data across different applications. It hides the heterogeneous sensor hardware and communication protocols from the applications built on top of it. The Sensor Web Enablement initiative of the Open Geospatial Consortium standardizes web service interfaces and data encodings which can be used as building blocks for a Sensor Web. This article illustrates and analyzes the recent developments of the new generation of the Sensor Web Enablement specification framework. Further, we relate the Sensor Web to other emerging concepts such as the Web of Things and point out challenges and resulting future work topics for research on Sensor Web Enablement

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

Large Software Implementation Project: A study of software development and project management literature

This study focuses on large scale software delivery, where development is done on top of an existing system or parallel to it. This thesis aims to answer to the question: How to implement a large scale custom solution? Large scale projects take longer than smaller projects to implement and usually they are done in more than in one release. The application’s life-cycle is also planned to last up to decades. Large projects also need special project management skills, executive support, internal investments, strategical vision as well as alignment between IT and business. Large projects are usually complex and have several dependencies. This study also explains what issues projects usually have and what are the constrains of legacy systems and data migration. Different eras of IT systems are also presented as well as reasons why companies should invest to IT solutions. Waterfall model and Agile methodology fundamentals and background are presented shortly. From Agile methodology Scrum and SAFe frameworks are presented as examples. Keywords: Legacy system, Data Migration, Software implementation, project management, COTS, Agile development, Waterfal