Using Goal Models Downstream

Horkoff, Jennifer; Li, Tong; Li, Fenglin; Salnitri, Mattia; Cardoso, Evellin; Giorgini, Paolo; Mylopoulos, John

doi:10.4018/ijismd.2015040101

Cited by 23 publications

(6 citation statements)

References 87 publications

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“…In the present study, we employed the SLR approach, which assists in understanding the research content and aligning the sources, authors, and future research perspectives (Horkoff et al, 2015). Moreover, based on certain assumptions, the SLR creates a reproducible procedure for other researchers to follow (Tranfield, Denyer, and Smart, 2003).…”

Section: Methodsmentioning

confidence: 99%

The relationship between the Internet of Things and knowledge management in smart ecosystem development

Ikeda

Silva

Penha

et al. 2021

Knowl Process Manag

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Creating an environment capable of responding to its users and organizations in real‐time is a challenging task. However, with the advent of the Internet of Things (IoT) ecosystem, a new perspective about creating an intelligent and interconnected world has become a reality. The goal of the present study aims to explore the relationship between IoT and Knowledge Management (KM) when generating a Smart environment. Herein, we conducted a Systematic Literature Review and present a discussion about the evolution of the IoT concept and the IoT ecosystem. More specifically we focused on the main IoT elements that serve as the foundation of the IoT ecosystem and the relationship between the IoT and KM. Although there is a lack of studies about KM supporting the IoT, our study provides evidence that the IoT is composed of a set of technologies that enable KM and KM processes can facilitate the development of IoT Smart environments. Thus, there is an opportunity to study how to generate more intelligence and connectivity, which will support the players of the ecosystem, promote interactions among objects and people and ultimately add value to organizations and users.

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Section: Methodsmentioning

confidence: 99%

The relationship between the Internet of Things and knowledge management in smart ecosystem development

Ikeda

Silva

Penha

et al. 2021

Knowl Process Manag

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“…Goal-oriented Requirement Engineering (GORE) provides a means to support the requirements elicitation and decompose them into well-defined entities and reason about the alternatives to meet them [46]. In this way, goal-oriented modeling has been recognized as a suitable strategy to represent the objectives of a software system and stakeholders' preferences [47]. Goal models may then be used as part of the entire system life-cycle, in particular, to improve the continuous delivery of software systems [48].…”

Section: Requirements Engineeringmentioning

confidence: 99%

ToffA-DSPL: An approach of Trade-off Analysis for designing Dynamic Software Product Lines

Carvalho,

Santos,

Masiero

et al. 2023

Preprint

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Software engineers have adopted the Dynamic Software Product Lines (DSPL) engineering practices to develop Dynamically Adaptable Software (DAS). DAS is seen as a DSPL application and must cope with a large number of configurations of features, Non-functional Requirements (NFRs), and contexts. However, the accurate representation of the impact of features over NFRs and contexts for the identification of optimal configurations is not a trivial task. Software engineers need to have domain knowledge and design DAS before deploying to satisfy those requirements. Aiming to handle them, we proposed an approach of Trade-off Analysis for DSPL at design-time, named ToffA-DSPL. It deals with the configuration selection process considering interactions between NFRs and contexts. We performed an exploratory study based on simulations to identify the usefulness of the ToffA-DSPL approach. In general, the configurations suggested by ToffA-DSPL provide high satisfaction levels of NFRs. Based on simulations, we evidenced that our approach aims to explore reuse and is useful for generating valid and optimal configurations. In addition, ToffA-DSPL enables software engineers to conduct trade-off analysis, evaluate changes in the context feature, and define an adaptation model from optimal configurations found in the analysis.

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“…In the literature, several goal-based modeling languages are defined (Horkoff et al, 2015). However, as far as we know, no goal-based modeling language allows the definition of adaptation actions and their impact on goal models.…”

Section: Improving E-health Monitoring With Data-utility Driven Adaptmentioning

confidence: 99%

“…The approach proposed in this paper to express requirements about data movement relies on goal-based models that are usually adopted in requirement engineering to specify the objectives of users and applications to be designed (Van Lamsweerde, 2001;Amyot and Mussbacher, 2011;Horkoff et al, 2015). By using the tree-like structures of goal models, decisions on which subset of the modeled goals must be achieved can be taken.…”

Section: Related Workmentioning

confidence: 99%

Improving Health Monitoring With Adaptive Data Movement in Fog Computing

et al. 2020

Self Cite

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Pervasive sensing is increasing our ability to monitor the status of patients not only when they are hospitalized but also during home recovery. As a result, lots of data are collected and are available for multiple purposes. If operations can take advantage of timely and detailed data, the huge amount of data collected can also be useful for analytics. However, these data may be unusable for two reasons: data quality and performance problems. First, if the quality of the collected values is low, the processing activities could produce insignificant results. Second, if the system does not guarantee adequate performance, the results may not be delivered at the right time. The goal of this document is to propose a data utility model that considers the impact of the quality of the data sources (e.g., collected data, biographical data, and clinical history) on the expected results and allows for improvement of the performance through utility-driven data management in a Fog environment. Regarding data quality, our approach aims to consider it as a context-dependent problem: a given dataset can be considered useful for one application and inadequate for another application. For this reason, we suggest a context-dependent quality assessment considering dimensions such as accuracy, completeness, consistency, and timeliness, and we argue that different applications have different quality requirements to consider. The management of data in Fog computing also requires particular attention to quality of service requirements. For this reason, we include QoS aspects in the data utility model, such as availability, response time, and latency. Based on the proposed data utility model, we present an approach based on a goal model capable of identifying when one or more dimensions of quality of service or data quality are violated and of suggesting which is the best action to be taken to address this violation. The proposed approach is evaluated with a real and appropriately anonymized dataset, obtained as part of the experimental procedure of a research project in which a device with a set of sensors (inertial, temperature, humidity, and light sensors) is used to collect motion and environmental data associated with the daily physical activities of healthy young volunteers.

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Using Goal Models Downstream

Cited by 23 publications

References 87 publications

The relationship between the Internet of Things and knowledge management in smart ecosystem development

The relationship between the Internet of Things and knowledge management in smart ecosystem development

ToffA-DSPL: An approach of Trade-off Analysis for designing Dynamic Software Product Lines

Improving Health Monitoring With Adaptive Data Movement in Fog Computing

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